artificial intelligence in thesis statement

The Main Topics for Coursework or a Thesis Statement in Artificial Intelligence

Artificial Intelligence (AI) is changing the world, from machine learning and the Internet of Things to Robotics and Natural Language processing.

Research is needed to understand more about AI and how it will affect the future.

AI-powered machines are likely to replace humans in many fields and the consequences of this are still largely unknown.

There are many topics of vital importance to choose from if you’re a student trying to decide on a topic involving AI for your thesis.

Image source: Freepik.com

Machine learning (ML) as a Thesis Topic

Artificial intelligence enables machines to automatically learn a task from experience and improve performance without any human intervention.

Machines need high-quality data to start with. They are trained by building machine learning models using the data and different algorithms.

The algorithms depend on the type of data and the tasks that need automation.

A topic for your research could involve discussing wearable devices. They are powered by machine learning and are becoming increasingly popular.

You could discuss their relevance in fields like health and insurance as well as how they can help individuals to improve their daily routines and move towards a more healthy lifestyle.

Deep learning (DL) as a Thesis Topic

Deep Learning is a subset of ML where learning imitates the inner workings of the human brain. It uses artificial neural networks to process data and make decisions.

The web-like networks take a non-linear approach to processing data which is superior to traditional algorithms that take a linear approach.

Google’s RankBrain is an example of an artificial neural network.

Deep learning is driving many AI applications such as object recognition, playing computer games, controlling self-driving cars and language translation.

A research topic could involve discussing deep learning and its various applications.

Reinforcement learning (RL) as a Thesis Topic

Reinforcement learning is the closest form of learning to the way human beings learn. For instance, students learn from their mistakes and a process of trial-and-error.

There are many different ways to use AI in education to help students, such as using AI-powered tutors, customized learning and smart content.

RL works on a similar principle to learning from a process of trial-and-error. Google’s AlphaGo program beat the world champion of Go in 2017 by using RL.

Students who don’t yet have the skills to handle complex assignments can make use of various tools, writing apps and professional writers.

To find help with your student papers when you’re conducting research for a university, EduBirdie has free plagiarism checker and citations tools but professional writers who can take the pressure off you.

At U.K. EduBirdie , a professional thesis writer will finish your paper for you. It also offers editing and proofreading services at very reasonable prices.

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Natural language processing (NLP) as a Thesis Topic

This area of AI relates to how machines can learn to recognize and analyze human speech. Speech recognition, natural language translation and natural language generation are some of the areas of NLP.

With the help of NLP, systems can even read sentiment and predict which parts of the language are important. Revolutionary tools like IBM Watson, Google Translate, Speech Recognition and sentiment analysis show the importance of NLP in the daily lives of individuals.

NLP helps build intelligent systems, such as customer support applications like chatbots and AI in education is also a great example.

Chatbots use NLP and machine learning to interact with customers and solve their queries. Your research topic could relate to chatbots and their interaction with humans.

Computer vision (CV) as a Thesis Topic

Millions of images are uploaded daily on the internet. Computers are very good at certain tasks but they can struggle with simple tasks like being able to recognize and identify objects.

Computer vision is a field of AI that makes systems so smart that they can analyze and understand images. CV systems can even outperform humans now in some tasks like classifying visual objects.

One of the applications of computer vision is in autonomous vehicles that need to analyze images of surroundings in order to navigate.

A study topic could involve discussing computer vision and how using it allows smart systems to be built. Applications of computer vision could then be presented.

Recommender systems (RS) as a Thesis Topic

Recommender systems use algorithms to offer relevant suggestions to users. These may be suggestions on a TV show, a product, a service or even who to date.

You will receive many recommendations after you search for a particular product or browse a list of favorite movies. RS can base suggestions on your past behavior and past preferences, trends and the preferences of your peers.

A very relevant topic would be to explore the use of recommender systems in the field of e-commerce. Industry giants like Amazon are currently using recommender systems to help customers find the right products or services.

You could discuss their implementation and the type of results they bring to ecommerce businesses.

Robotics as a Thesis Topic

Robots can behave and perform the same actions as human beings, thanks to AI. They can act intelligently and even solve problems and learn in controlled environments.

For example, Kismet is a social interaction robot developed by MIT’s AI lab that can recognize human language and interact with humans.

Robots and AI are changing the way businesses work. Some people argue that this will have an adverse effect on humans as they are replaced by AI-powered machines.

A research topic could aim to understand to what extent businesses will be impacted by AI-powered machines and assess their future in different businesses.

There is an increase in the number of research papers being published in different areas of AI. If you’re a student wanting to come up with a topic involving artificial intelligence for your thesis, there are many vitally important sub-topics to choose from.

Each of these sub-topics provides plenty of opportunities for meaningful research into AI and new ideas on its application in the future as machines keep growing in intelligence.

About The Author

Paul Calderon

Paul Calderon is data security specialist working with a tech startup based in Silicon Valley. After work hours, he helps students studying for their computer science degrees or programming courses with essays, dissertations and term papers. When he isn’t doing any work, he likes playing tennis, cycling, and creating vlogs on local travel.

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Artificial intelligence & machine learning thesis statement examples.

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Artificial Intelligence (AI) and Machine Learning (ML) are pioneering technologies driving innovation across various sectors. When composing a thesis in this dynamic field, it is essential to commence with a concise and precise thesis statement that encapsulates your research’s essence. Below are examples of good and bad thesis statements, each followed by an analysis illustrating their effectiveness or shortcomings.

Good Thesis Statement Examples

Specific and Clear: “This thesis will investigate the application of machine learning algorithms in predicting stock prices with a focus on the technology sector.” Unclear: “Machine learning can be used to predict stock prices.”

The good example is clear and specific, detailing the application area (stock price prediction) and narrowing the focus to the technology sector. In contrast, the bad statement is vague, lacking both specificity and a defined scope.

Arguable and Debatable: “Despite its benefits, the implementation of AI in hiring processes can inadvertently reinforce existing biases, thus exacerbating workplace inequality.” Dull: “AI in hiring has pros and cons.”

The good statement is debatable and presents a clear argument, highlighting the potential downside of AI in hiring. Meanwhile, the bad statement is indecisive and fails to present a clear argument or stance.

Researchable and Measurable: “This study explores the efficacy of deep learning in the early detection of breast cancer through the analysis of mammographic images.” Uninspiring: “AI can help detect diseases early.”

A good example is researchable and measurable, specifying the AI type (deep learning), application (early detection of breast cancer), and method (analysis of mammographic images). Conversely, the bad statement is too general and lacks specificity.

Bad Thesis Statement Examples

Overly Broad: “Artificial intelligence is changing the world.”

While true, this statement is overly broad, providing no clear direction or focus for research.

Lack of Clear Argument: “AI and ML are important in data analysis.”

This statement, while factual, lacks a clear argument or focus, not providing the reader with an understanding of the research’s purpose or direction.

Unoriginal and Unengaging: “AI is used in many areas like healthcare, finance, and technology.”

Though factual, this statement is unoriginal and unengaging, lacking a specific focus or claim to guide the research.

Crafting an effective thesis statement for AI and ML research necessitates clarity, specificity, and a well-defined argument. Good thesis statements serve as a robust foundation, guiding both the researcher and the reader through the research journey. Conversely, bad thesis statements are vague, broad, and lack a clear focus, which might misguide the research process. By considering the examples provided, students can adeptly craft thesis statements that not only encapsulate their research focus but also engage readers with compelling arguments in the ever-evolving fields of Artificial Intelligence and Machine Learning.

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12 Best Artificial Intelligence Topics for Research in 2024

Explore the "12 Best Artificial Intelligence Topics for Research in 2024." Dive into the top AI research areas, including Natural Language Processing, Computer Vision, Reinforcement Learning, Explainable AI (XAI), AI in Healthcare, Autonomous Vehicles, and AI Ethics and Bias. Stay ahead of the curve and make informed choices for your AI research endeavours.

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Table of Contents

1) Top Artificial Intelligence Topics for Research

a) Natural Language Processing

b) Computer vision

c) Reinforcement Learning

d) Explainable AI (XAI)

e) Generative Adversarial Networks (GANs)

f) Robotics and AI

g) AI in healthcare

h) AI for social good

i) Autonomous vehicles

j) AI ethics and bias

2) Conclusion

Natural Language Processing

Natural Language Processing (NLP) is centred around empowering machines to comprehend, interpret, and even generate human language. Within this domain, three distinctive research avenues beckon:

1) Sentiment analysis: This entails the study of methodologies to decipher and discern emotions encapsulated within textual content. Understanding sentiments is pivotal in applications ranging from brand perception analysis to social media insights.

2) Language generation: Generating coherent and contextually apt text is an ongoing pursuit. Investigating mechanisms that allow machines to produce human-like narratives and responses holds immense potential across sectors.

3) Question answering systems: Constructing systems that can grasp the nuances of natural language questions and provide accurate, coherent responses is a cornerstone of NLP research. This facet has implications for knowledge dissemination, customer support, and more.

Computer Vision

Computer Vision, a discipline that bestows machines with the ability to interpret visual data, is replete with intriguing avenues for research:

1) Object detection and tracking: The development of algorithms capable of identifying and tracking objects within images and videos finds relevance in surveillance, automotive safety, and beyond.

2) Image captioning: Bridging the gap between visual and textual comprehension, this research area focuses on generating descriptive captions for images, catering to visually impaired individuals and enhancing multimedia indexing.

3) Facial recognition: Advancements in facial recognition technology hold implications for security, personalisation, and accessibility, necessitating ongoing research into accuracy and ethical considerations.

Reinforcement Learning

Reinforcement Learning revolves around training agents to make sequential decisions in order to maximise rewards. Within this realm, three prominent Artificial Intelligence Topics emerge:

1) Autonomous agents: Crafting AI agents that exhibit decision-making prowess in dynamic environments paves the way for applications like autonomous robotics and adaptive systems.

2) Deep Q-Networks (DQN): Deep Q-Networks, a class of reinforcement learning algorithms, remain under active research for refining value-based decision-making in complex scenarios.

3) Policy gradient methods: These methods, aiming to optimise policies directly, play a crucial role in fine-tuning decision-making processes across domains like gaming, finance, and robotics.

Introduction To Artificial Intelligence Training

Explainable AI (XAI)

The pursuit of Explainable AI seeks to demystify the decision-making processes of AI systems. This area comprises Artificial Intelligence Topics such as:

1) Model interpretability: Unravelling the inner workings of complex models to elucidate the factors influencing their outputs, thus fostering transparency and accountability.

2) Visualising neural networks: Transforming abstract neural network structures into visual representations aids in comprehending their functionality and behaviour.

3) Rule-based systems: Augmenting AI decision-making with interpretable, rule-based systems holds promise in domains requiring logical explanations for actions taken.

Generative Adversarial Networks (GANs)

The captivating world of Generative Adversarial Networks (GANs) unfolds through the interplay of generator and discriminator networks, birthing remarkable research avenues:

1) Image generation: Crafting realistic images from random noise showcases the creative potential of GANs, with applications spanning art, design, and data augmentation.

2) Style transfer: Enabling the transfer of artistic styles between images, merging creativity and technology to yield visually captivating results.

3) Anomaly detection: GANs find utility in identifying anomalies within datasets, bolstering fraud detection, quality control, and anomaly-sensitive industries.

Robotics and AI

The synergy between Robotics and AI is a fertile ground for exploration, with Artificial Intelligence Topics such as:

1) Human-robot collaboration: Research in this arena strives to establish harmonious collaboration between humans and robots, augmenting industry productivity and efficiency.

2) Robot learning: By enabling robots to learn and adapt from their experiences, Researchers foster robots' autonomy and the ability to handle diverse tasks.

3) Ethical considerations: Delving into the ethical implications surrounding AI-powered robots helps establish responsible guidelines for their deployment.

AI in healthcare

AI presents a transformative potential within healthcare, spurring research into:

1) Medical diagnosis: AI aids in accurately diagnosing medical conditions, revolutionising early detection and patient care.

2) Drug discovery: Leveraging AI for drug discovery expedites the identification of potential candidates, accelerating the development of new treatments.

3) Personalised treatment: Tailoring medical interventions to individual patient profiles enhances treatment outcomes and patient well-being.

AI for social good

Harnessing the prowess of AI for Social Good entails addressing pressing global challenges:

1) Environmental monitoring: AI-powered solutions facilitate real-time monitoring of ecological changes, supporting conservation and sustainable practices.

2) Disaster response: Research in this area bolsters disaster response efforts by employing AI to analyse data and optimise resource allocation.

3) Poverty alleviation: Researchers contribute to humanitarian efforts and socioeconomic equality by devising AI solutions to tackle poverty.

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Autonomous vehicles

Autonomous Vehicles represent a realm brimming with potential and complexities, necessitating research in Artificial Intelligence Topics such as:

1) Sensor fusion: Integrating data from diverse sensors enhances perception accuracy, which is essential for safe autonomous navigation.

2) Path planning: Developing advanced algorithms for path planning ensures optimal routes while adhering to safety protocols.

3) Safety and ethics: Ethical considerations, such as programming vehicles to make difficult decisions in potential accident scenarios, require meticulous research and deliberation.

AI ethics and bias

Ethical underpinnings in AI drive research efforts in these directions:

1) Fairness in AI: Ensuring AI systems remain impartial and unbiased across diverse demographic groups.

2) Bias detection and mitigation: Identifying and rectifying biases present within AI models guarantees equitable outcomes.

3) Ethical decision-making: Developing frameworks that imbue AI with ethical decision-making capabilities aligns technology with societal values.

Future of AI

The vanguard of AI beckons Researchers to explore these horizons:

1) Artificial General Intelligence (AGI): Speculating on the potential emergence of AI systems capable of emulating human-like intelligence opens dialogues on the implications and challenges.

2) AI and creativity: Probing the interface between AI and creative domains, such as art and music, unveils the coalescence of human ingenuity and technological prowess.

3) Ethical and regulatory challenges: Researching the ethical dilemmas and regulatory frameworks underpinning AI's evolution fortifies responsible innovation.

AI and education

The intersection of AI and Education opens doors to innovative learning paradigms:

1) Personalised learning: Developing AI systems that adapt educational content to individual learning styles and paces.

2) Intelligent tutoring systems: Creating AI-driven tutoring systems that provide targeted support to students.

3) Educational data mining: Applying AI to analyse educational data for insights into learning patterns and trends.

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Conclusion

The domain of AI is ever-expanding, rich with intriguing topics about Artificial Intelligence that beckon Researchers to explore, question, and innovate. Through the pursuit of these twelve diverse Artificial Intelligence Topics, we pave the way for not only technological advancement but also a deeper understanding of the societal impact of AI. By delving into these realms, Researchers stand poised to shape the trajectory of AI, ensuring it remains a force for progress, empowerment, and positive transformation in our world.

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8 Best Topics for Research and Thesis in Artificial Intelligence

Imagine a future in which intelligence is not restricted to humans!!! A future where machines can think as well as humans and work with them to create an even more exciting universe. While this future is still far away, Artificial Intelligence has still made a lot of advancement in these times. There is a lot of research being conducted in almost all fields of AI like Quantum Computing, Healthcare, Autonomous Vehicles, Internet of Things , Robotics , etc. So much so that there is an increase of 90% in the number of annually published research papers on Artificial Intelligence since 1996.

Keeping this in mind, if you want to research and write a thesis based on Artificial Intelligence, there are many sub-topics that you can focus on. Some of these topics along with a brief introduction are provided in this article. We have also mentioned some published research papers related to each of these topics so that you can better understand the research process.

Table of Content

1. Machine Learning

2. deep learning, 3. reinforcement learning, 4. robotics, 5. natural language processing (nlp), 6. computer vision, 7. recommender systems, 8. internet of things.

Best-Topics-for-Research-and-Thesis-in-Artificial-Intelligence

So without further ado, let’s see the different Topics for Research and Thesis in Artificial Intelligence!

Machine Learning involves the use of Artificial Intelligence to enable machines to learn a task from experience without programming them specifically about that task. (In short, Machines learn automatically without human hand holding!!!) This process starts with feeding them good quality data and then training the machines by building various machine learning models using the data and different algorithms. The choice of algorithms depends on what type of data do we have and what kind of task we are trying to automate.

However, generally speaking, Machine Learning Algorithms are generally divided into 3 types: Supervised Machine Learning Algorithms , Unsupervised Machine Learning Algorithms , and Reinforcement Machine Learning Algorithms . If you are interested in gaining practical experience and understanding these algorithms in-depth, check out the Data Science Live Course by us.

Deep Learning is a subset of Machine Learning that learns by imitating the inner working of the human brain in order to process data and implement decisions based on that data. Basically, Deep Learning uses artificial neural networks to implement machine learning. These neural networks are connected in a web-like structure like the networks in the human brain (Basically a simplified version of our brain!).

This web-like structure of artificial neural networks means that they are able to process data in a nonlinear approach which is a significant advantage over traditional algorithms that can only process data in a linear approach. An example of a deep neural network is RankBrain which is one of the factors in the Google Search algorithm.

Reinforcement Learning is a part of Artificial Intelligence in which the machine learns something in a way that is similar to how humans learn. As an example, assume that the machine is a student. Here the hypothetical student learns from its own mistakes over time (like we had to!!). So the Reinforcement Machine Learning Algorithms learn optimal actions through trial and error.

This means that the algorithm decides the next action by learning behaviors that are based on its current state and that will maximize the reward in the future. And like humans, this works for machines as well! For example, Google’s AlphaGo computer program was able to beat the world champion in the game of Go (that’s a human!) in 2017 using Reinforcement Learning.

Robotics is a field that deals with creating humanoid machines that can behave like humans and perform some actions like human beings. Now, robots can act like humans in certain situations but can they think like humans as well? This is where artificial intelligence comes in! AI allows robots to act intelligently in certain situations. These robots may be able to solve problems in a limited sphere or even learn in controlled environments.

An example of this is Kismet , which is a social interaction robot developed at M.I.T’s Artificial Intelligence Lab. It recognizes the human body language and also our voice and interacts with humans accordingly. Another example is Robonaut , which was developed by NASA to work alongside the astronauts in space.

It’s obvious that humans can converse with each other using speech but now machines can too! This is known as Natural Language Processing where machines analyze and understand language and speech as it is spoken (Now if you talk to a machine it may just talk back!). There are many subparts of NLP that deal with language such as speech recognition, natural language generation, natural language translation , etc. NLP is currently extremely popular for customer support applications, particularly the chatbot . These chatbots use ML and NLP to interact with the users in textual form and solve their queries. So you get the human touch in your customer support interactions without ever directly interacting with a human.

Some Research Papers published in the field of Natural Language Processing are provided here. You can study them to get more ideas about research and thesis on this topic.

The internet is full of images! This is the selfie age, where taking an image and sharing it has never been easier. In fact, millions of images are uploaded and viewed every day on the internet. To make the most use of this huge amount of images online, it’s important that computers can see and understand images. And while humans can do this easily without a thought, it’s not so easy for computers! This is where Computer Vision comes in.

Computer Vision uses Artificial Intelligence to extract information from images. This information can be object detection in the image, identification of image content to group various images together, etc. An application of computer vision is navigation for autonomous vehicles by analyzing images of surroundings such as AutoNav used in the Spirit and Opportunity rovers which landed on Mars.

When you are using Netflix, do you get a recommendation of movies and series based on your past choices or genres you like? This is done by Recommender Systems that provide you some guidance on what to choose next among the vast choices available online. A Recommender System can be based on Content-based Recommendation or even Collaborative Filtering.

Content-Based Recommendation is done by analyzing the content of all the items. For example, you can be recommended books you might like based on Natural Language Processing done on the books. On the other hand, Collaborative Filtering is done by analyzing your past reading behavior and then recommending books based on that.

Artificial Intelligence deals with the creation of systems that can learn to emulate human tasks using their prior experience and without any manual intervention. Internet of Things , on the other hand, is a network of various devices that are connected over the internet and they can collect and exchange data with each other.

Now, all these IoT devices generate a lot of data that needs to be collected and mined for actionable results. This is where Artificial Intelligence comes into the picture. Internet of Things is used to collect and handle the huge amount of data that is required by the Artificial Intelligence algorithms. In turn, these algorithms convert the data into useful actionable results that can be implemented by the IoT devices.

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The Future of AI Research: 20 Thesis Ideas for Undergraduate Students in Machine Learning and Deep Learning for 2023!

A comprehensive guide for crafting an original and innovative thesis in the field of ai..

By Aarafat Islam on 2023-01-11

“The beauty of machine learning is that it can be applied to any problem you want to solve, as long as you can provide the computer with enough examples.” — Andrew Ng

This article provides a list of 20 potential thesis ideas for an undergraduate program in machine learning and deep learning in 2023. Each thesis idea includes an introduction , which presents a brief overview of the topic and the research objectives . The ideas provided are related to different areas of machine learning and deep learning, such as computer vision, natural language processing, robotics, finance, drug discovery, and more. The article also includes explanations, examples, and conclusions for each thesis idea, which can help guide the research and provide a clear understanding of the potential contributions and outcomes of the proposed research. The article also emphasized the importance of originality and the need for proper citation in order to avoid plagiarism.

1. Investigating the use of Generative Adversarial Networks (GANs) in medical imaging: A deep learning approach to improve the accuracy of medical diagnoses.

Introduction: Medical imaging is an important tool in the diagnosis and treatment of various medical conditions. However, accurately interpreting medical images can be challenging, especially for less experienced doctors. This thesis aims to explore the use of GANs in medical imaging, in order to improve the accuracy of medical diagnoses.

2. Exploring the use of deep learning in natural language generation (NLG): An analysis of the current state-of-the-art and future potential.

Introduction: Natural language generation is an important field in natural language processing (NLP) that deals with creating human-like text automatically. Deep learning has shown promising results in NLP tasks such as machine translation, sentiment analysis, and question-answering. This thesis aims to explore the use of deep learning in NLG and analyze the current state-of-the-art models, as well as potential future developments.

3. Development and evaluation of deep reinforcement learning (RL) for robotic navigation and control.

Introduction: Robotic navigation and control are challenging tasks, which require a high degree of intelligence and adaptability. Deep RL has shown promising results in various robotics tasks, such as robotic arm control, autonomous navigation, and manipulation. This thesis aims to develop and evaluate a deep RL-based approach for robotic navigation and control and evaluate its performance in various environments and tasks.

4. Investigating the use of deep learning for drug discovery and development.

Introduction: Drug discovery and development is a time-consuming and expensive process, which often involves high failure rates. Deep learning has been used to improve various tasks in bioinformatics and biotechnology, such as protein structure prediction and gene expression analysis. This thesis aims to investigate the use of deep learning for drug discovery and development and examine its potential to improve the efficiency and accuracy of the drug development process.

5. Comparison of deep learning and traditional machine learning methods for anomaly detection in time series data.

Introduction: Anomaly detection in time series data is a challenging task, which is important in various fields such as finance, healthcare, and manufacturing. Deep learning methods have been used to improve anomaly detection in time series data, while traditional machine learning methods have been widely used as well. This thesis aims to compare deep learning and traditional machine learning methods for anomaly detection in time series data and examine their respective strengths and weaknesses.

Photo by Joanna Kosinska on Unsplash

6. Use of deep transfer learning in speech recognition and synthesis.

Introduction: Speech recognition and synthesis are areas of natural language processing that focus on converting spoken language to text and vice versa. Transfer learning has been widely used in deep learning-based speech recognition and synthesis systems to improve their performance by reusing the features learned from other tasks. This thesis aims to investigate the use of transfer learning in speech recognition and synthesis and how it improves the performance of the system in comparison to traditional methods.

7. The use of deep learning for financial prediction.

Introduction: Financial prediction is a challenging task that requires a high degree of intelligence and adaptability, especially in the field of stock market prediction. Deep learning has shown promising results in various financial prediction tasks, such as stock price prediction and credit risk analysis. This thesis aims to investigate the use of deep learning for financial prediction and examine its potential to improve the accuracy of financial forecasting.

8. Investigating the use of deep learning for computer vision in agriculture.

Introduction: Computer vision has the potential to revolutionize the field of agriculture by improving crop monitoring, precision farming, and yield prediction. Deep learning has been used to improve various computer vision tasks, such as object detection, semantic segmentation, and image classification. This thesis aims to investigate the use of deep learning for computer vision in agriculture and examine its potential to improve the efficiency and accuracy of crop monitoring and precision farming.

9. Development and evaluation of deep learning models for generative design in engineering and architecture.

Introduction: Generative design is a powerful tool in engineering and architecture that can help optimize designs and reduce human error. Deep learning has been used to improve various generative design tasks, such as design optimization and form generation. This thesis aims to develop and evaluate deep learning models for generative design in engineering and architecture and examine their potential to improve the efficiency and accuracy of the design process.

10. Investigating the use of deep learning for natural language understanding.

Introduction: Natural language understanding is a complex task of natural language processing that involves extracting meaning from text. Deep learning has been used to improve various NLP tasks, such as machine translation, sentiment analysis, and question-answering. This thesis aims to investigate the use of deep learning for natural language understanding and examine its potential to improve the efficiency and accuracy of natural language understanding systems.

Photo by UX Indonesia on Unsplash

11. Comparing deep learning and traditional machine learning methods for image compression.

Introduction: Image compression is an important task in image processing and computer vision. It enables faster data transmission and storage of image files. Deep learning methods have been used to improve image compression, while traditional machine learning methods have been widely used as well. This thesis aims to compare deep learning and traditional machine learning methods for image compression and examine their respective strengths and weaknesses.

12. Using deep learning for sentiment analysis in social media.

Introduction: Sentiment analysis in social media is an important task that can help businesses and organizations understand their customers’ opinions and feedback. Deep learning has been used to improve sentiment analysis in social media, by training models on large datasets of social media text. This thesis aims to use deep learning for sentiment analysis in social media, and evaluate its performance against traditional machine learning methods.

13. Investigating the use of deep learning for image generation.

Introduction: Image generation is a task in computer vision that involves creating new images from scratch or modifying existing images. Deep learning has been used to improve various image generation tasks, such as super-resolution, style transfer, and face generation. This thesis aims to investigate the use of deep learning for image generation and examine its potential to improve the quality and diversity of generated images.

14. Development and evaluation of deep learning models for anomaly detection in cybersecurity.

Introduction: Anomaly detection in cybersecurity is an important task that can help detect and prevent cyber-attacks. Deep learning has been used to improve various anomaly detection tasks, such as intrusion detection and malware detection. This thesis aims to develop and evaluate deep learning models for anomaly detection in cybersecurity and examine their potential to improve the efficiency and accuracy of cybersecurity systems.

15. Investigating the use of deep learning for natural language summarization.

Introduction: Natural language summarization is an important task in natural language processing that involves creating a condensed version of a text that preserves its main meaning. Deep learning has been used to improve various natural language summarization tasks, such as document summarization and headline generation. This thesis aims to investigate the use of deep learning for natural language summarization and examine its potential to improve the efficiency and accuracy of natural language summarization systems.

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16. Development and evaluation of deep learning models for facial expression recognition.

Introduction: Facial expression recognition is an important task in computer vision and has many practical applications, such as human-computer interaction, emotion recognition, and psychological studies. Deep learning has been used to improve facial expression recognition, by training models on large datasets of images. This thesis aims to develop and evaluate deep learning models for facial expression recognition and examine their performance against traditional machine learning methods.

17. Investigating the use of deep learning for generative models in music and audio.

Introduction: Music and audio synthesis is an important task in audio processing, which has many practical applications, such as music generation and speech synthesis. Deep learning has been used to improve generative models for music and audio, by training models on large datasets of audio data. This thesis aims to investigate the use of deep learning for generative models in music and audio and examine its potential to improve the quality and diversity of generated audio.

18. Study the comparison of deep learning models with traditional algorithms for anomaly detection in network traffic.

Introduction: Anomaly detection in network traffic is an important task that can help detect and prevent cyber-attacks. Deep learning models have been used for this task, and traditional methods such as clustering and rule-based systems are widely used as well. This thesis aims to compare deep learning models with traditional algorithms for anomaly detection in network traffic and analyze the trade-offs between the models in terms of accuracy and scalability.

19. Investigating the use of deep learning for improving recommender systems.

Introduction: Recommender systems are widely used in many applications such as online shopping, music streaming, and movie streaming. Deep learning has been used to improve the performance of recommender systems, by training models on large datasets of user-item interactions. This thesis aims to investigate the use of deep learning for improving recommender systems and compare its performance with traditional content-based and collaborative filtering approaches.

20. Development and evaluation of deep learning models for multi-modal data analysis.

Introduction: Multi-modal data analysis is the task of analyzing and understanding data from multiple sources such as text, images, and audio. Deep learning has been used to improve multi-modal data analysis, by training models on large datasets of multi-modal data. This thesis aims to develop and evaluate deep learning models for multi-modal data analysis and analyze their potential to improve performance in comparison to single-modal models.

I hope that this article has provided you with a useful guide for your thesis research in machine learning and deep learning. Remember to conduct a thorough literature review and to include proper citations in your work, as well as to be original in your research to avoid plagiarism. I wish you all the best of luck with your thesis and your research endeavors!

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Crafting the Perfect Thesis Statement for Artificial Intelligence with WriteGo

How to craft the perfect thesis statement for artificial intelligence.

Writing a strong thesis statement is a crucial step in any academic paper, especially when tackling complex topics like artificial intelligence. With WriteGo , creating a compelling thesis statement has never been easier. This article will guide you through the process and introduce you to WriteGo, our innovative AI-powered writing tool designed to enhance your academic writing experience.

Understanding the Thesis Statement

A thesis statement is a concise summary of the main point or claim of an essay or research paper. For a topic as expansive as artificial intelligence, crafting a clear and focused thesis statement is essential. It sets the direction of your paper and informs your readers about what to expect.

Key Elements of a Strong Thesis Statement

Clarity : Your thesis statement should be clear and specific. Avoid vague language and ensure that your main point is easily understood.
Arguability : A good thesis statement should present a claim that others might dispute. It should encourage discussion and not just state a fact.
Scope : The statement should be manageable within the constraints of your paper. Avoid overly broad or overly narrow topics.

Steps to Create a Thesis Statement for Artificial Intelligence

Choose a Focused Topic : Identify a specific area within the broad field of artificial intelligence. For example, you might focus on AI ethics, machine learning, or the impact of AI on employment.
Conduct Preliminary Research : Gather information on your chosen topic to understand the existing discourse and identify gaps or areas of contention.
Formulate Your Main Argument : Based on your research, decide what specific claim or argument you want to make about your topic.
Refine Your Statement : Ensure that your thesis statement is clear, arguable, and appropriately scoped.

Example Thesis Statements for Artificial Intelligence

"The integration of artificial intelligence in healthcare can significantly enhance patient outcomes while reducing operational costs."
"Ethical considerations in artificial intelligence development are crucial to ensure technology benefits society without infringing on individual rights."
"The rise of artificial intelligence will transform the job market, necessitating new skills and education systems to prepare the workforce."

Introducing WriteGo: Your AI Writing Assistant

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WriteGo is an advanced AI-powered writing tool designed to help you generate high-quality academic content. Whether you're struggling with your thesis statement or need assistance with the entire paper, WriteGo is here to help.

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Creating a strong thesis statement for artificial intelligence topics is a critical step in your academic writing journey. By following the steps outlined above, you can craft a clear, arguable, and well-scoped thesis statement.

Using WriteGo, you can streamline your writing process and produce compelling, high-quality academic papers.

Start using WriteGo today and see how our innovative platform can transform your academic writing experience.

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AI for thesis writing — Unveiling 7 best AI tools

Writing a thesis is akin to piecing together a complex puzzle. Each research paper, every data point, and all the hours spent reading and analyzing contribute to this monumental task.

For many students, this journey is a relentless pursuit of knowledge, often marked by sleepless nights and tight deadlines.

Here, the potential of AI for writing a thesis or research papers becomes clear: artificial intelligence can step in, not to take over but to assist and guide.

Far from being just a trendy term, AI is revolutionizing academic research, offering ChatPDF and writing tools that can make the task of thesis writing more manageable, more precise, and a little less overwhelming.

In this article, we’ll discuss the impact of AI on academic writing process, and articulate the best AI tools for thesis writing to enhance your thesis writing process.

The Impact of AI on Thesis Writing

Artificial Intelligence offers a supportive hand in thesis writing, adeptly navigating vast datasets, suggesting enhancements in writing, and refining the narrative.

With the integration of AI writing assistant, instead of requiring you to manually sift through endless articles, AI tools can spotlight the most pertinent pieces in mere moments. Need clarity or the right phrasing? AI-driven writing assistants are there, offering real-time feedback, ensuring your work is both articulative and academically sound.

AI tools for thesis writing harness Natural Language Processing (NLP) to generate content, check grammar, and assist in literature reviews. Simultaneously, Machine Learning (ML) techniques enable data analysis, provide personalized research recommendations, and aid in proper citation.

And for the detailed tasks of academic formatting and referencing? AI streamlines it all, ensuring your thesis meets the highest academic standards.

However, understanding AI's role is pivotal. It's a supportive tool, not the primary author. Your thesis remains a testament to your unique perspective and voice.

AI for writing thesis is there to amplify that voice, ensuring it's heard clearly and effectively.

How AI tools supplement your thesis writing

AI tools have emerged as invaluable allies for scholars. With just a few clicks, these advanced platforms can streamline various aspects of thesis writing, from data analysis to literature review.

Let's explore how an AI tool can supplement and transform your thesis writing style and process.

Efficient literature review : AI tools can quickly scan and summarize vast amounts of literature, making the process of literature review more efficient. Instead of spending countless hours reading through papers, researchers can get concise summaries and insights, allowing them to focus on relevant content.

Enhanced data analysis : AI algorithms can process and analyze large datasets with ease, identifying patterns, trends, and correlations that might be difficult or time-consuming for humans to detect. This capability is especially valuable in fields with massive datasets, like genomics or social sciences.

Improved writing quality : AI-powered writing assistants can provide real-time feedback on grammar, style, and coherence. They can suggest improvements, ensuring that the final draft of a research paper or thesis is of high quality.

Plagiarism detection : AI tools can scan vast databases of academic content to ensure that a researcher's work is original and free from unintentional plagiarism .

Automated citations : Managing and formatting citations is a tedious aspect of academic writing. AI citation generators can automatically format citations according to specific journal or conference standards, reducing the chances of errors.

Personalized research recommendations : AI tools can analyze a researcher's past work and reading habits to recommend relevant papers and articles, ensuring that they stay updated with the latest in their field.

Interactive data visualization : AI can assist in creating dynamic and interactive visualizations, making it easier for researchers to present their findings in a more engaging manner.

Top 7 AI Tools for Thesis Writing

The academic field is brimming with AI tools tailored for academic paper writing. Here's a glimpse into some of the most popular and effective ones.

Here we'll talk about some of the best ai writing tools, expanding on their major uses, benefits, and reasons to consider them.

If you've ever been bogged down by the minutiae of formatting or are unsure about specific academic standards, Typeset is a lifesaver.

You can also check out SciSpace ChatPDF for your research paper exploration.

Typeset specializes in formatting, ensuring academic papers align with various journal and conference standards.

It automates the intricate process of academic formatting, saving you from the manual hassle and potential errors, inflating your writing experience.

An AI-driven writing assistant, Wisio elevates the quality of your thesis content. It goes beyond grammar checks, offering style suggestions tailored to academic writing.

This ensures your thesis is both grammatically correct and maintains a scholarly tone. For moments of doubt or when maintaining a consistent style becomes challenging, Wisio acts as your personal editor, providing real-time feedback.

Known for its ability to generate and refine thesis content using AI algorithms, Texti ensures logical and coherent content flow according to the academic guidelines.

When faced with writer's block or a blank page, Texti can jumpstart your thesis writing process, aiding in drafting or refining content.

JustDone is an AI for thesis writing and content creation. It offers a straightforward three-step process for generating content, from choosing a template to customizing details and enjoying the final output.

JustDone AI can generate thesis drafts based on the input provided by you. This can be particularly useful for getting started or overcoming writer's block.

This platform can refine and enhance the editing process, ensuring it aligns with academic standards and is free from common errors. Moreover, it can process and analyze data, helping researchers identify patterns, trends, and insights that might be crucial for their thesis.

Tailored for academic writing, Writefull offers style suggestions to ensure your content maintains a scholarly tone.

This AI for thesis writing provides feedback on your language use, suggesting improvements in grammar, vocabulary, and structure . Moreover, it compares your written content against a vast database of academic texts. This helps in ensuring that your writing is in line with academic standards.

Isaac Editor

For those seeking an all-in-one solution for writing, editing, and refining, Isaac Editor offers a comprehensive platform.

Combining traditional text editor features with AI, Isaac Editor streamlines the writing process. It's an all-in-one solution for writing, editing, and refining, ensuring your content is of the highest quality.

PaperPal , an AI-powered personal writing assistant, enhances academic writing skills, particularly for PhD thesis writing and English editing.

This AI for thesis writing offers comprehensive grammar, spelling, punctuation, and readability suggestions, along with detailed English writing tips.

It offers grammar checks, providing insights on rephrasing sentences, improving article structure, and other edits to refine academic writing.

The platform also offers tools like "Paperpal for Word" and "Paperpal for Web" to provide real-time editing suggestions, and "Paperpal for Manuscript" for a thorough check of completed articles or theses.

Is it ethical to use AI for thesis writing?

The AI for writing thesis has ignited discussions on authenticity. While AI tools offer unparalleled assistance, it's vital to maintain originality and not become overly reliant. Research thrives on unique contributions, and AI should be a supportive tool, not a replacement.

The key question: Can a thesis, significantly aided by AI, still be viewed as an original piece of work?

AI tools can simplify research, offer grammar corrections, and even produce content. However, there's a fine line between using AI as a helpful tool and becoming overly dependent on it.

In essence, while AI offers numerous advantages for thesis writing, it's crucial to use it judiciously. AI should complement human effort, not replace it. The challenge is to strike the right balance, ensuring genuine research contributions while leveraging AI's capabilities.

Wrapping Up

Nowadays, it's evident that AI tools are not just fleeting trends but pivotal game-changers.

They're reshaping how we approach, structure, and refine our theses, making the process more efficient and the output more impactful. But amidst this technological revolution, it's essential to remember the heart of any thesis: the researcher's unique voice and perspective .

AI tools are here to amplify that voice, not overshadow it. They're guiding you through the vast sea of information, ensuring our research stands out and resonates.

Try these tools out and let us know what worked for you the best.

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Frequently Asked Questions

Yes, you can use AI to assist in writing your thesis. AI tools can help streamline various aspects of the writing process, such as data analysis, literature review, grammar checks, and content refinement.

However, it's essential to use AI as a supportive tool and not a replacement for original research and critical thinking. Your thesis should reflect your unique perspective and voice.

Yes, there are AI tools designed to assist in writing research papers. These tools can generate content, suggest improvements, help with formatting, and even provide real-time feedback on grammar and coherence.

Examples include Typeset, JustDone, Writefull, and Texti. However, while they can aid the process, the primary research, analysis, and conclusions should come from the researcher.

The "best" AI for writing papers depends on your specific needs. For content generation and refinement, Texti is a strong contender.

For grammar checks and style suggestions tailored to academic writing, Writefull is highly recommended. JustDone offers a user-friendly interface for content creation. It's advisable to explore different tools and choose one that aligns with your requirements.

To use AI for writing your thesis:

1. Identify the areas where you need assistance, such as literature review, data analysis, content generation, or grammar checks.

2. Choose an AI tool tailored for academic writing, like Typeset, JustDone, Texti, or Writefull.

3. Integrate the tool into your writing process. This could mean using it as a browser extension, a standalone application, or a plugin for your word processor.

4. As you write or review content, use the AI tool for real-time feedback, suggestions, or content generation.

5. Always review and critically assess the suggestions or content provided by the AI to ensure it aligns with your research goals and maintains academic integrity.

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Artificial Intelligence

Recent advances in artificial intelligence (AI) for writing (including CoPilot and ChatGPT ) can quickly create coherent, cohesive prose and paragraphs on a seemingly limitless set of topics. The potential for abuse in academic integrity is clear, and our students are likely using these tools already. There are similar AI tools for creating images, computer code, and many other domains. Here’s an overview video (33 minutes) of what AI actually is and how it impacts teaching and learning.

Most of this guide concerns generative AI (GenAI) such as large-language models (LLMs) that function as word-predictors and can generate text and entire essays. As AI represents a permanent addition to society and students’ tools, we need to permanently re-envision how we assign college writing and other projects. As such, FCTL has assembled this set of ideas to consider.

Category 1: Lean into the Software’s Abilities

Re-envision writing as editing/revising . Assign students to create an AI essay with a given prompt, and then heavily edit the AI output using Track Changes and margin comments. Such an assignment refocuses the work of writing away from composition and toward revision, which may be more common in an AI-rich future workplace. Generative AI (GenAI, such at Copilot or ChatGPT) is spectacular at providing summaries, but they lack details and specifics, which could be what the students are tasked to do. Other examples include better connecting examples to claims, and revising overall paragraph structure in service of a larger argument. Here are some example assignments using GenAI as part of the writing prompt.
Re-envision writing as first and third stage human work, with AI performing the middle . Instead of asking students to generate the initials drafts (i.e., “writing as composition”), imagine the student work instead focusing on creating effective prompts for the AI, as well editing the AI output.
Focus student learning on creative thesis writing by editing AI-created theses . The controlling statement for most AI essays can be characterized as summary in nature, rather than analytical. Students can be challenged to transform AI output into more creative, analytical theses.
Refine editing skills via grading . Assign students to create an AI essay and grade it, providing specific feedback justifying each of the scores on the rubric. This assignment might be paired with asking students to create their own essay responding to the same prompt.
Write rebuttals. Ask the AI to produce a custom output you’ve intentionally designed, then assign students to write a rebuttal of the AI output.
Create counterarguments . Provide the AI with your main argument and ask it to create counterarguments, which can be incorporated – then overcome – in the main essay.
Evaluate AI writing for bias . Because the software is only as good as information it finds and ingests (remember the principle of GIGO: garbage in, garbage out), it may well create prose that mimics structural bias and racism that is present in its source material. AI writing might also reveal assumptions about the “cultural war” separating political parties in the United States.
Teach information literacy through AI . Many students over-trust information they find on websites; use AI software to fuel a conversation about when to trust, when to verify, and when to use information found online.
Give only open-book exams (especially online) . Assume that students can and will use the Internet and any available AI to assist them.
Assign essays, projects, and tests that aim for “application” and above in Bloom’s taxonomy . Since students can look up knowledge/information answers and facts, it’s better to avoid testing them on such domains, especially online.
Teach debate and critical thinking skills . Ask the AI to produce a stance, then using the tools of your discipline evaluate and find flaws/holes in its position or statements.
Ask the AI to role play as a character or historical figure . Since GenAI is conversation-based, holding a conversation with an in-character personality yields insights.
Overcome writer’s block . The AI output could provide a starting point for an essay outline, a thesis statement, or even ideas for paragraphs. Even if none of the paragraphs (or even sentences) are used, asking the AI can be useful for ideation to be put into one’s own words.
Treat it like a Spellchecker . Ask your students to visit GenAI, type “suggest grammar and syntax fixes:” and then paste their pre-written essay to gain ideas before submission. (Note: for classes where writing ability is a main learning outcome, it might be advisable to require that students disclose any such assistance).
Make the AI your teaching assistant . When preparing a course, ask the AI to explain why commonly-wrong answers are incorrect. Then, use the Canvas feedback options on quiz/homework questions to paste the AI output for each question.
Teach sentence diagramming and parts of speech . Since AI can quickly generate text with variety in sentence structures, use the AI output to teach grammar and help students how better to construct sophisticated sentences.
Engage creativity and multiple modes of representation to foster better recall . Studies show that student recall increases when they use words to describe a picture, or draw a picture to capture information in words. Using AI output as the base, ask students to create artwork (or performances) that capture the same essence.
Teach AI prompt strategies as a discreet subject related to your field . AI-created content is sure to be a constant in the workplace of the future. Our alumni will need to be versed in crafting specific and sophisticated inputs to obtain best AI outputs.
Create sample test questions to study for your test . Given appropriate prompts, AI can generate college-level multiple choice test questions on virtually any subject, and provide the right answer. Students can use such questions as modern-day flash cards and test practice.
View more ideas in this free e-book written by FCTL : “ 60+ Ideas for ChatGPT Assignments ,” which is housed in the UCF Library’s STARS system. Even though the ebook mentions ChatGPT in its title, the assignment prompts work for most GenAI, including CoPilot, our official university LLM.

Category 2 : Use the software to make your teaching/faculty life easier

Create grading rubrics for major assignments . Give specifics about the assignment when asking the software to create a rubric in table format. Optionally, give it the desired sub-grades of the rubric.
Write simple or mechanical correspondence for you . GenAI is fairly good at writing letters and formulaic emails. The more specific the inputs are, the better the output is. However, always keep in mind the ethics of using AI-generated writing wholesale, representing the writing as your own words–particularly if you are evaluating or recommending anything. AI output should not be used, for instance, in submitting peer reviews.
Adjust, simplify, shorten, or enhance your formal writing . The software could be asked to shorten (or lengthen) any professional writing you are composing, or to suggest grammar and syntax fixes (particularly useful for non-native speakers of English!) In short, you could treat it like Spellchecker before you submit it. However, again consider the ethics of using AI content wholesale–journals and granting agencies are still deciding how (or whether) to accept AI-assisted submissions, and some have banned it.
Summarize one-minute papers . If you ask students for feedback, or to prove they understand a concept via one-minute papers, you can submit these en masse and ask the AI to provide a summary.
Generate study guides for your students . If you input your lecture notes and ask for a summary, this can be given to students as a study guide.
Create clinical case studies for students to analyze . You can generate different versions of a case with a similar prompt.
Evaluate qualitative data . Provide the AI with raw data and ask it to identify patterns, not only in repeated words but in similar concepts.
What about AI and research? It’s best to be cautious, if not outright paranoid, about privacy, legality, ethics, and many related concerns, when thinking about exposing your primary research to any AI platform–especially anything novel that could lead to patent and commercialization. Consult the IT department and the Office of Research before taking any action.
Create test questions and banks . The AI can create nearly limitless multiple-choice questions (with correct answers identified) on many topics and sub-topics. Obviously, these need to be proof-read and verified before using with a student audience.

Category 3: Teach Ethics, Integrity, and Career-Related Skills

Discuss the ethical and career implications of AI-writing with your students . Early in the semester (or at least when assigning a writing prompt), have a frank discussion with your students about the existence of AI writing. Point out to them the surface-level ethical problem with mis-representing their work if they choose to attempt it, as well as the deeper problem of “cheating themselves” by entering the workforce without adequate preparation for writing skills, a quality that employers highly prize.
Create and prioritize an honor code in your class . Submitting AI-created work as one’s own is, fundamentally, dishonest. As professionals, we consider it among our top priorities to graduate individuals of character who can perform admirably in their chosen discipline, all of which requires a set of core beliefs rooted in honor. Make this chain of logic explicit to students (repeatedly if necessary) in an effort to convince them to adopt a similar alignment toward personal honesty. A class-specific honor code can aid this effort, particularly if invoked or attested to when submitting major assignments and tests.
Reduce course-related workload to disincentivize cheating . Many instances of student cheating, including the use of AI-writing, is borne out of desperation and a lack of time. Consider how realistic the workload you expect of students is

Category 4: Attempt to neutralize the software

Faculty looking to continue assigning take-home writing and essays may be interested in this list of ideas to customize their assignments so that students do not benefit from generative AI. However, this approach will likely fail in time, as the technology is improving rapidly, and automated detection methods are already unreliable (at UCF, in fact, the office of Student Conduct and Academic Integrity will not pursue administrative cases against students where the only evidence is from AI detectors). Artificial intelligence is simply a fact of life in modern society, and its use will only become more widespread.

Possible Syllabus Statements

Faculty looking for syllabus language may consider one of these options:

Use of AI prohibited . Only some Artificial Intelligence (AI) tools, such as spell-check or Grammarly, are acceptable for use in this class. Use of other AI tools via website, app, or any other access, is not permitted in this class. Representing work created by AI as your own is plagiarism and will be prosecuted as such. Check with your instructor to be sure of acceptable use if you have any questions.
Use of AI only with explicit permission . This class will make use of Artificial Intelligence (AI) in various ways. You are permitted to use AI only in the manner and means described in the assignments. Any other use of AI requires explicit permission from the instructor. Any attempt to represent AI output inappropriately as your own work will be treated as plagiarism.
Use of AI only with acknowledgement . Students are allowed to use Artificial Intelligence (AI) tools on assignments if the usage is properly documented and credited. For example, text generated from Copilot should include a citation such as: “Copilot. Accessed 2023-12-03. Prompt: ‘Summarize the Geneva Convention in 50 words.’ Generated using http://bing.com/chat.”
Use of AI is freely permitted with no acknowledgement . Students are allowed to use Artificial Intelligence (AI) tools in all assignments in this course, with no need to cite, document, or acknowledge any support received from AI tools.
Use of AI will be required . In this course, various assignments require you to complete tasks with the aid of Copilot, while logged in with your NID. When logged in this way, Copilot is safe, private, and free.

If you write longer announcements or policies for students, try to aim for a level-headed tone that neither overly demonizes AI nor overly idolizes it. Students who are worried about artificial intelligence and/or privacy will be reassured by a steady, business-like tone.

AI Detection and Unauthorized Student Use

AI detectors are not reliable and relatively easy for students to beat , so UCF does not have a current contract with any detector. If you use third-party detectors, you should keep in mind that both false positives and false negatives can occur, and student use of Grammarly can return a result of “written by AI.”

FCTL recommends that you NOT use AI detectors.

Because the detectors don’t work , independent verification is required. If you have other examples of this student’s writing that does not match, that might be reason enough to take action. Evidence of a hallucinated citation is even stronger.

A confession of using AI by the student is, of course, the gold standard for taking action.
One approach might be to call the student to a private (virtual?) conference and explain why you suspect the student used AI and ask them how they would account for these facts.
Another option is to inform them of your intention to fail the paper, but offer them the chance to perform proctored, in-person writing on a similar prompt to prove they can write at this level.

The S tudent Conduct and Academic Integrity office will not “prosecute” a case where the only evidence comes from an AI detector, due to the possibility of false positives and false negatives. A hallucinated citation does constitute evidence.

They do still encourage you to file a report in any event and can offer suggestions on how to proceed.
Existing university-level policies ban students from representing work that they did not create as their own, so it’s not always necessary to have a specific AI policy in your syllabus – but it IS a best practice to have such a policy for transparency to students and to communicate your expectations. After all, the lived experience of students is that different faculty have different expectations regarding AI, and extreme clarity is always best.

At the end of the day, the final say about grading remains with the instructor. We recognize that in marginal cases, it might come down to a “gut feeling.” Every instructor has a spectrum of response available to them, from “F” for the term, an “F” or zero for the assignment, a grade penalty (10%? 20%?) applied to the assigned grade, a chance to rewrite the assignment (with or without a grade penalty), taking no grade action but warning the student not to do it again, or to simply letting it go without even approaching the student.

Be aware that students have the right to appeal academic grades. For that reason, it may be advisable to check with your supervisor about how to proceed in specific cases.

Because of all of these uncertainties, FCTL suggests that faculty consider replacing essay writing with another deliverable that AI cannot today generate (examples include narrated PowerPoint, narrated Prezi, selfie video presentation WITHOUT reading from a script, digital poster, flowcharts, etc.) An alternative is to include AI-generated output as part of the assignment prompt, and then require the students to “do something” with the output, such as analyze or evaluate it.

The Faculty Center recommends that UCF faculty work with Copilot (formerly Bing Chat Enterprise) over other large-language model AI tools. The term Copilot is also used by Microsoft to refer to embedded AI in MS Office products, but the web-based chat tool is separate.

Copilot with Commercial Protection is NOT the same thing as “Copilot.” The latter is the public model of Microsoft’s LLM, also available on the web. Copilot with Commercial Protection (if logged in with a UCF NID) is a “walled garden” for UCF that offers several benefits:

It searches the current Internet and is not limited to a fixed point in time when it was trained
It uses GPT-4 (faster, better) without having to pay a premium
It uses DALL-E 3.0 to generate images (right there inside CoPilot rather than on a different site)
It provides a live Internet link to verify the information and confirm there was no hallucination
It does not store history by user; each logout or new session wipes the memory. In fact, each query is a new blank slate even within the same session, so it’s not possible to have a “conversation” with Copilot (like you can with ChatGPT)
Faculty and students log in with their NID
Data stays local and is NOT uploaded to Microsoft or the public model version of Copilot. Inputs into Copilot with Commercial Protection are NOT added to the system’s memory, database, or future answers

The safe version of UCF’s Copilot is accessed via this procedure:

If the site doesn’t recognize your UCF email, switch to Bing .
If it still doesn’t work, switch to the Edge browser.
Click “sign in” at the top-right
Select “work or school” for the type of account
Type your full UCF email (including @ucf.edu) and click NEXT
Log in with your NID and NID password. (Note: you may need to alter your SafeSearch settings away from “Strict”)
Note : if image-generation isn’t working, switch to Edge browser and start at Bing and then sign in using NID.

We recommend that faculty approach the AI revolution with the recognition that AI is here to stay and will represent a needed skill in the workplace of the future (or even the present!) As such, both faculty and students need to develop AI Fluency skills, which we define as:

Understanding how AI works – knowing how LLMs operate will help users calibrate how much they should (mis)trust the output.
Deciding when to use AI (and when not to) – AI is just another tool. In some circumstances users will get better results than a web-based search engine, but in other circumstances the reverse may be true. There are also moments when it may be unethical to use AI without disclosing the help.
Valuing AI – a dispositional change such as this one is often overshadowed by outcomes favored by faculty on the cognitive side, yet true fluency with AI – especially the AI of the future – will require a favorable disposition to using AI. Thus, we owe it to students to recognize AI’s value.
Applying effective prompt engineering methods – as the phrase goes, “garbage in, garbage out” applies when it comes to the kind of output AI creates. Good prompts give better results than lazy or ineffective prompts. Writing effective prompts is likely to remain a tool-specific skill, with different AI interfaces needing to be learned separately.
Evaluating AI output – even today’s advanced AI tools can create hallucinations or contain factual mistakes. Employees in the workplace of the future – and thus our students today – need expertise in order to know how trustworthy the output is, and they need practice in fixing/finalizing the output, as this is surely how workplaces will use AI.
Adding human value – things that can be automated by AI will, in fact, eventually become fully automated. But there will always be a need for human involvement for elements such as judgment, creativity, or emotional intelligence. Our students need to hone the skill of constantly seeking how humans add value to AI output. This includes sensing where (or when) the output could use human input, extrapolation, or interpretation, and then creating effective examples of them. Since this will be context-dependent, it’s not a single skill needed so much as a set of tools that enable our alumni to flourish alongside AI.
Displaying digital adaptability – today’s AI tools will evolve, or may be replaced by completely different AI tools. Students and faculty need to be prepared for a lifetime of changing AI landscapes. They will need the mental dexterity and agility to accept these changes as inevitable, and the disposition to not fight against these tidal forces. The learning about AI, in other words, should be expected to last a lifetime.

“60+ ChatGPT Assignments to Use in Your Classroom Today”

The Faculty Center staff assembled this open-source book to give you ideas about how to actually use AI in your assignments. It is free for anyone to use, and may be shared with others both inside and outside of UCF.

“Teach with AI” Conference

UCF’s Faculty Center and Center for Distributed Learning are co-hosts of the “Teach with AI” annual conference . This is a national sharing conference that uses short-format presentations and open forums to focus on the sharing of classroom practices by front-line faculty and administrators, rather than research about AI. Although this conference is not free for UCF faculty and staff, we hold separate internal events about AI that are free for UCF stakeholders.

AI Fundamentals for Educators Course

Interested in diving deeper in using AI, not just for teaching but also in your own research? Join the Faculty Center for this 6-week course! Held face to face on the Orlando campus, this course includes topics such as:

LLM models (explore the differences in ChatGPT, Bard/Genesis, CoPilot, and Claude), the art of prompt engineering , and how to incorporate these tools into lesson planning, assignments, and assessments .
Image, audio, and video generation tools and how to create interactive audio and video experiences using various GenAI tools while meeting digital accessibility requirements .
Assignment and assessment alterations to include—or combat—the use of GenAI tools in student work.
Interactive teaching tools for face-to-face AND online courses.
AI tools that assist students—and faculty—with discipline-specific academic papers and research.
Teaching AI fluency and ethics to students.

Registration details are on our “ AI Fundamentals page .”

Asynchronous Training Module on AI

Looking for a deeper dive into using AI in your teaching and research, but need a self-paced online option? We’ve got that too! Click on this website to self-enroll in a Webcourse.

Repository of AI Tools

There are several repositories that attempt to catalog all AI tools (futurepedia.io and theresanaiforthat stand out in particular), but we’ve been curating a smaller, more targeted list here .

AI Glossary

Canva – a “freemium” online image creating/editing tool that added AI-image generation in 2023
ChatGPT – the text-generating AI created by OpenAI
Claude – the text-generating AI created by Anthropic (ex-employees of OpenAI)
Copilot – a UCF-specific instance of Microsoft’s LLM, using UCF logins and keeping data local (note: confusingly, this name is ALSO used by Microsoft for AI embedded in Microsoft Office products, but UCF does not purchase this subscription).
DALL-E – the image-generating AI created by OpenAI
Gemini – an LLM from Google (formerly known as Bard)
Generative AI – a type of AI that “generates” an output, such as text or images. Large language models like ChatGPT are generative AI
Grok – the generative AI product launched by Elon Musk
Khanmigo – Khan Academy’s GPT-powered AI, which will be integrated into Canvas/Webcourses (timeline uncertain)
LLM (Large Language Model) – a type of software / generative AI that accesses large databases it’s been trained on to predict the next logical word in a sentence, given the task/question it’s been given. Advanced models have excellent “perplexity” (plausibility in the word choice) and “burstiness” (variation of the sentences).
Midjourney – an industry-leading text-to-AI solution (for profit)
OpenAI – the company that created ChatGPT and DALL-E
Sora – a text-to-video generative AI from OpenAI

Artificial Intelligence Thesis Topics

1000 Artificial Intelligence Thesis Topics and Ideas

Selecting the right artificial intelligence thesis topic is a crucial step in your academic journey, as it sets the foundation for a meaningful and impactful research project. With the rapid advancements and wide-reaching applications of AI, the field offers a vast array of topics that can cater to diverse interests and career aspirations. To help you navigate this process, we have compiled a comprehensive list of artificial intelligence thesis topics, meticulously categorized into 20 distinct areas. Each category includes 50 topics, ensuring a broad selection that encompasses current issues, recent trends, and future directions in the field of AI. This list is designed to inspire and guide you in choosing a topic that not only aligns with your interests but also contributes to the ongoing developments in artificial intelligence.

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Supervised learning algorithms: An in-depth study.
Unsupervised learning and clustering techniques.
The role of reinforcement learning in autonomous systems.
Advances in transfer learning for AI applications.
Machine learning for predictive maintenance in manufacturing.
Bias and fairness in machine learning algorithms.
The impact of feature engineering on model performance.
Machine learning in personalized medicine: Opportunities and challenges.
Semi-supervised learning techniques and their applications.
Ethical implications of machine learning in decision-making.
Machine learning for fraud detection in financial systems.
The role of ensemble methods in improving model accuracy.
Applications of machine learning in natural disaster prediction.
Machine learning for real-time traffic management.
The impact of data augmentation on machine learning models.
Explainability in machine learning models: Methods and challenges.
The use of machine learning in drug discovery.
Machine learning for predictive analytics in business.
Transfer learning and domain adaptation in AI.
The role of machine learning in personalized marketing.
Applications of machine learning in autonomous vehicles.
Machine learning techniques for cybersecurity threat detection.
The impact of deep reinforcement learning on robotics.
Machine learning in agriculture: Precision farming applications.
Challenges in deploying machine learning models at scale.
Machine learning for predictive policing: Ethical concerns and solutions.
The future of machine learning in healthcare diagnostics.
Applications of machine learning in renewable energy optimization.
Machine learning for natural language understanding.
The role of machine learning in supply chain optimization.
Machine learning in financial market prediction.
Reinforcement learning for game AI development.
The impact of quantum computing on machine learning.
Machine learning for real-time video analysis.
The role of machine learning in enhancing human-computer interaction.
Machine learning in the detection of deepfakes.
The future of machine learning in autonomous robotics.
Machine learning for climate change modeling and prediction.
The impact of machine learning on personalized learning environments.
Machine learning in the detection and prevention of cyberbullying.
Applications of machine learning in genomic data analysis.
Machine learning for optimizing logistics and transportation networks.
The role of machine learning in smart city development.
Machine learning for customer sentiment analysis.
The future of machine learning in augmented reality.
Challenges in ensuring the privacy of machine learning models.
The role of machine learning in predictive customer analytics.
Machine learning in medical imaging: Advances and challenges.
The impact of machine learning on predictive maintenance in aviation.
Machine learning in the optimization of energy consumption.
Advances in convolutional neural networks for image recognition.
The role of deep learning in natural language processing.
Applications of deep learning in autonomous driving.
Deep learning for facial recognition systems: Privacy and ethics.
The impact of generative adversarial networks (GANs) on creative industries.
Deep learning for real-time speech recognition.
The role of deep learning in healthcare diagnostics.
Challenges in training deep learning models with limited data.
The future of deep learning in robotics and automation.
Applications of deep learning in video analysis.
Deep learning for predictive analytics in finance.
The role of deep learning in enhancing cybersecurity.
Deep learning in drug discovery and development.
The impact of deep learning on virtual and augmented reality.
Applications of deep learning in remote sensing and earth observation.
Deep learning for customer behavior prediction.
The role of deep learning in personalized content recommendation.
Challenges in deploying deep learning models at scale.
The impact of deep learning on natural language generation.
Deep learning for predictive maintenance in industrial systems.
The role of deep learning in autonomous robotics.
Deep learning for real-time object detection and tracking.
Applications of deep learning in medical imaging.
The impact of deep learning on fraud detection systems.
Deep learning for time series forecasting in finance.
The role of deep learning in enhancing human-computer interaction.
Applications of deep learning in climate change modeling.
Deep learning for predictive policing: Ethical implications.
The future of deep learning in smart city development.
Deep learning for real-time traffic management.
The role of deep learning in enhancing voice assistants.
Applications of deep learning in genomic data analysis.
The impact of deep learning on personalized learning environments.
Deep learning for predictive customer analytics.
The future of deep learning in augmented reality.
Challenges in ensuring the transparency of deep learning models.
The role of deep learning in detecting and preventing cyberattacks.
Applications of deep learning in social media analysis.
The impact of deep learning on autonomous systems.
Deep learning for predictive maintenance in transportation.
The role of deep learning in enhancing digital marketing strategies.
Deep learning for real-time video content moderation.
The impact of deep learning on the entertainment industry.
Applications of deep learning in supply chain optimization.
The future of deep learning in personalized healthcare.
Challenges in deep learning for speech synthesis and recognition.
The role of deep learning in fraud detection in e-commerce.
Applications of deep learning in financial market prediction.
The impact of deep learning on smart home technologies.
Deep learning for natural language understanding in multilingual systems.
The role of NLP in sentiment analysis.
Advances in machine translation using NLP.
NLP for automated customer service systems.
The impact of NLP on content moderation.
NLP in social media monitoring: Challenges and opportunities.
The role of NLP in enhancing search engine performance.
Applications of NLP in automated summarization.
The future of NLP in human-computer interaction.
NLP for predictive text generation.
The impact of NLP on fake news detection.
NLP in sentiment analysis for financial markets.
The role of NLP in personalized content recommendation.
Applications of NLP in healthcare: Analyzing patient records.
The impact of NLP on automated translation systems.
NLP for automated sentiment analysis in social media.
The role of NLP in content creation and curation.
Applications of NLP in detecting hate speech.
The future of NLP in personalized marketing.
Challenges in building multilingual NLP models.
The role of NLP in enhancing voice assistants.
Applications of NLP in legal document analysis.
The impact of NLP on automated essay grading.
NLP for real-time speech recognition systems.
The role of NLP in enhancing customer experience.
Applications of NLP in e-commerce: Product recommendations.
The impact of NLP on machine translation accuracy.
NLP for automated sentiment analysis in online reviews.
The role of NLP in enhancing virtual assistants.
Applications of NLP in analyzing social media trends.
The impact of NLP on personalized learning systems.
NLP for predictive text generation in chatbots.
The role of NLP in content moderation on social media platforms.
Applications of NLP in summarizing financial reports.
The impact of NLP on real-time language translation.
NLP for enhancing search engine optimization strategies.
The role of NLP in detecting plagiarism in academic writing.
Applications of NLP in detecting and preventing spam.
The future of NLP in personalized education tools.
Challenges in ensuring the ethical use of NLP.
The role of NLP in improving customer support systems.
Applications of NLP in analyzing legal texts.
The impact of NLP on detecting and mitigating bias in AI.
NLP for real-time transcription in video conferencing.
The role of NLP in enhancing digital marketing strategies.
Applications of NLP in detecting cyberbullying.
The impact of NLP on automated customer support systems.
NLP for analyzing and categorizing large text datasets.
The role of NLP in improving information retrieval systems.
Applications of NLP in identifying and preventing misinformation.
NLP for sentiment analysis in multilingual social media platforms.
The impact of computer vision on autonomous vehicles.
Advances in facial recognition technology.
Applications of computer vision in healthcare diagnostics.
The role of computer vision in enhancing security systems.
Challenges in implementing computer vision in real-time applications.
Computer vision for automated quality control in manufacturing.
The impact of computer vision on augmented reality.
Applications of computer vision in sports analytics.
The role of computer vision in detecting deepfakes.
Computer vision for object detection in retail environments.
The future of computer vision in smart cities.
Applications of computer vision in agriculture.
The impact of computer vision on medical imaging.
The role of computer vision in enhancing user interfaces.
Computer vision for real-time traffic monitoring.
The impact of computer vision on social media platforms.
Applications of computer vision in drone technology.
The role of computer vision in automated surveillance systems.
Computer vision for gesture recognition in human-computer interaction.
The impact of computer vision on video content analysis.
Applications of computer vision in environmental monitoring.
The future of computer vision in retail automation.
Challenges in ensuring the accuracy of computer vision algorithms.
Computer vision for facial expression recognition.
The role of computer vision in enhancing interactive gaming experiences.
Applications of computer vision in underwater exploration.
The impact of computer vision on traffic safety systems.
The role of computer vision in detecting anomalies in industrial processes.
Computer vision for real-time facial recognition in security systems.
Applications of computer vision in disaster management.
The impact of computer vision on automated customer service.
The role of computer vision in enhancing smart home technologies.
Applications of computer vision in wildlife monitoring.
The future of computer vision in personalized advertising.
Challenges in implementing computer vision in low-light environments.
Computer vision for real-time video surveillance in public spaces.
The role of computer vision in enhancing virtual reality experiences.
Applications of computer vision in analyzing historical documents.
The impact of computer vision on fraud detection in finance.
The role of computer vision in autonomous robotics.
Computer vision for real-time detection of road signs in autonomous vehicles.
Applications of computer vision in human pose estimation.
The impact of computer vision on improving accessibility for the visually impaired.
The role of computer vision in enhancing video conferencing tools.
Applications of computer vision in sports performance analysis.
The future of computer vision in personalized shopping experiences.
Challenges in ensuring the fairness of computer vision algorithms.
Computer vision for real-time detection of environmental hazards.
The role of computer vision in improving traffic flow management.
Applications of computer vision in virtual fashion try-on tools.
The role of AI in enhancing autonomous vehicle safety.
Advances in robotic navigation systems.
The impact of AI on industrial automation.
Robotics in healthcare: Opportunities and challenges.
The future of autonomous drones in delivery services.
Ethical considerations in the deployment of autonomous systems.
The role of AI in human-robot collaboration.
Robotics in disaster response: AI-driven solutions.
The impact of AI on robotic process automation.
Autonomous systems in agriculture: AI applications.
Challenges in ensuring the safety of autonomous robots.
The role of AI in enhancing robotic perception.
Robotics in manufacturing: AI-driven efficiency improvements.
The future of AI in personal robotics.
The impact of AI on the development of social robots.
Autonomous underwater vehicles: AI-driven exploration.
The role of AI in enhancing autonomous drone navigation.
Robotics in elder care: AI applications and challenges.
The impact of AI on the future of autonomous public transportation.
The role of AI in autonomous supply chain management.
Robotics in education: AI-driven learning tools.
The future of autonomous delivery robots in urban environments.
Ethical implications of AI-driven autonomous weapons systems.
The role of AI in enhancing the dexterity of robotic arms.
Robotics in space exploration: AI applications.
The impact of AI on autonomous warehouse management.
The role of AI in autonomous farming equipment.
Robotics in construction: AI-driven innovation.
The future of AI in autonomous waste management systems.
The impact of AI on robotic caregiving for people with disabilities.
The role of AI in enhancing autonomous vehicle communication.
Robotics in logistics: AI applications and challenges.
The future of AI in autonomous firefighting robots.
The impact of AI on the development of underwater robotics.
The role of AI in enhancing the autonomy of robotic exoskeletons.
Robotics in retail: AI-driven customer service automation.
The future of AI in autonomous security systems.
The impact of AI on the development of robotic assistants.
The role of AI in enhancing the safety of autonomous aircraft.
Robotics in environmental conservation: AI applications.
The future of AI in autonomous food delivery systems.
Ethical considerations in the development of AI-driven companion robots.
The role of AI in enhancing robotic vision systems.
Robotics in mining: AI-driven automation and safety.
The impact of AI on the development of autonomous rescue robots.
The future of AI in autonomous maintenance systems.
The role of AI in enhancing robotic learning capabilities.
Robotics in military applications: AI-driven advancements.
The future of AI in autonomous infrastructure inspection.
The role of AI in swarm robotics for coordinated autonomous tasks.
Ethical implications of AI in decision-making processes.
The impact of AI on privacy and data security.
AI bias and fairness: Challenges and solutions.
The role of AI in perpetuating or mitigating societal inequalities.
Ethical considerations in the use of AI for surveillance.
The future of ethical AI in healthcare decision-making.
The role of ethics in the development of autonomous weapons systems.
Ethical challenges in the deployment of AI in law enforcement.
The impact of AI on employment and the future of work.
AI ethics in autonomous vehicles: Decision-making in critical situations.
The role of transparency in building ethical AI systems.
Ethical implications of AI in personalized marketing.
The future of AI governance: Developing ethical frameworks.
The role of AI ethics in protecting user privacy.
Ethical challenges in AI-driven content moderation.
The impact of AI on human autonomy and decision-making.
AI ethics in the context of predictive policing.
The role of ethical guidelines in AI research and development.
Ethical implications of AI in financial decision-making.
The future of AI ethics in healthcare diagnostics.
The role of ethics in AI-driven social media algorithms.
Ethical challenges in the development of AI for autonomous drones.
The impact of AI on the ethical considerations in biomedical research.
The role of ethics in AI-driven environmental monitoring.
Ethical implications of AI in smart cities.
The future of ethical AI in human-robot interactions.
The role of ethics in AI-driven educational tools.
Ethical challenges in the deployment of AI in military applications.
The impact of AI on ethical considerations in cybersecurity.
AI ethics in the context of facial recognition technology.
The role of ethics in AI-driven decision-making in finance.
Ethical implications of AI in autonomous retail systems.
The future of ethical AI in personalized healthcare.
The role of ethics in the development of AI-driven assistive technologies.
Ethical challenges in the use of AI for public health surveillance.
The impact of AI on ethical considerations in autonomous vehicles.
The role of ethics in AI-driven content creation.
Ethical implications of AI in automated hiring processes.
The future of ethical AI in data-driven decision-making.
The role of ethics in AI-driven security systems.
Ethical challenges in the development of AI for smart homes.
The impact of AI on ethical considerations in environmental conservation.
AI ethics in the context of digital identity verification.
The role of ethics in AI-driven predictive analytics.
Ethical implications of AI in autonomous transportation systems.
The future of ethical AI in personalized education.
The role of ethics in AI-driven decision-making in the legal field.
Ethical challenges in the deployment of AI in disaster response.
The impact of AI on ethical considerations in personalized advertising.
The ethical implications of AI in predictive policing and surveillance technologies.
The role of AI in personalized medicine.
AI-driven diagnostics: Opportunities and challenges.
The impact of AI on predictive analytics in healthcare.
Ethical considerations in AI-driven healthcare decision-making.
The future of AI in drug discovery and development.
AI in medical imaging: Enhancing diagnostic accuracy.
The role of AI in patient monitoring and management.
AI-driven healthcare chatbots: Benefits and limitations.
The impact of AI on healthcare data privacy and security.
The role of AI in improving surgical outcomes.
AI in mental health care: Opportunities and ethical challenges.
The future of AI in genomics and precision medicine.
AI-driven predictive models for disease outbreak management.
The role of AI in healthcare resource optimization.
AI in telemedicine: Enhancing patient care at a distance.
The impact of AI on healthcare workforce efficiency.
Ethical implications of AI in genetic testing and counseling.
The role of AI in improving clinical trial design and execution.
AI-driven patient triage systems: Opportunities and challenges.
The future of AI in robotic-assisted surgery.
AI in healthcare administration: Streamlining processes and reducing costs.
The role of AI in early detection and prevention of chronic diseases.
AI-driven mental health assessments: Benefits and ethical considerations.
The impact of AI on patient-doctor relationships.
AI in personalized treatment planning: Opportunities and challenges.
The role of AI in improving public health surveillance.
AI-driven wearable health technology: Benefits and challenges.
The future of AI in rehabilitative care.
AI in healthcare fraud detection: Opportunities and limitations.
The role of AI in enhancing patient safety in hospitals.
AI-driven predictive analytics for chronic disease management.
The impact of AI on reducing healthcare disparities.
AI in healthcare supply chain management: Opportunities and challenges.
The role of AI in improving healthcare accessibility in remote areas.
AI-driven decision support systems in healthcare: Benefits and limitations.
The future of AI in healthcare policy and regulation.
AI in personalized nutrition: Opportunities and ethical challenges.
The role of AI in improving healthcare outcomes for aging populations.
AI-driven healthcare data analysis: Benefits and challenges.
The impact of AI on the future of nursing and allied health professions.
AI in healthcare quality improvement: Opportunities and limitations.
The role of AI in addressing mental health care gaps.
AI-driven healthcare automation: Benefits and ethical considerations.
The future of AI in global health initiatives.
AI in personalized wellness programs: Opportunities and challenges.
The role of AI in improving patient adherence to treatment plans.
AI-driven healthcare risk assessment: Opportunities and limitations.
The impact of AI on healthcare cost reduction strategies.
AI in healthcare education and training: Opportunities and challenges.
The role of AI in enhancing mental health diagnosis and treatment through digital platforms.
The role of AI in algorithmic trading.
AI-driven financial forecasting: Opportunities and challenges.
The impact of AI on fraud detection in financial institutions.
The future of AI in personalized financial planning.
AI in credit scoring: Enhancing accuracy and fairness.
The role of AI in risk management for financial institutions.
AI-driven investment strategies: Benefits and limitations.
The impact of AI on financial market stability.
The role of AI in enhancing customer experience in banking.
AI in financial regulation: Opportunities and challenges.
The future of AI in insurance underwriting.
AI-driven wealth management: Opportunities and limitations.
The role of AI in improving financial compliance.
AI in anti-money laundering efforts: Opportunities and challenges.
The impact of AI on financial data security.
The role of AI in enhancing financial inclusion.
AI-driven portfolio management: Benefits and limitations.
The future of AI in financial advisory services.
Ethical considerations in AI-driven financial products.
AI in financial risk assessment: Opportunities and challenges.
The role of AI in enhancing payment processing systems.
AI-driven credit risk management: Benefits and limitations.
The impact of AI on reducing operational costs in financial institutions.
AI in financial fraud prevention: Opportunities and challenges.
The future of AI in automated financial reporting.
The role of AI in improving financial transparency.
AI-driven customer segmentation in banking: Benefits and challenges.
The impact of AI on financial decision-making in investment firms.
AI in financial planning and analysis: Opportunities and challenges.
The future of AI in robo-advisory services.
AI-driven transaction monitoring in banking: Benefits and limitations.
The role of AI in enhancing financial literacy.
AI in financial product development: Opportunities and challenges.
The impact of AI on customer data privacy in financial institutions.
The future of AI in financial auditing.
AI-driven financial stress testing: Benefits and challenges.
The role of AI in improving financial customer support services.
AI in financial crime detection: Opportunities and limitations.
The impact of AI on financial regulatory compliance.
AI-driven risk modeling in finance: Benefits and challenges.
The future of AI in enhancing financial stability.
The role of AI in improving investment decision-making.
AI in financial forecasting for small businesses: Opportunities and challenges.
The impact of AI on personalized banking services.
AI-driven asset management: Benefits and limitations.
The role of AI in improving financial product recommendations.
AI in predictive analytics for financial markets: Opportunities and challenges.
The future of AI in reducing financial transaction costs.
The impact of AI on automating credit risk assessment for lending decisions.
The role of AI in personalized learning environments.
AI-driven educational analytics: Opportunities and challenges.
The impact of AI on student assessment and evaluation.
Ethical considerations in AI-driven education systems.
The future of AI in adaptive learning technologies.
AI in student engagement: Enhancing motivation and participation.
The role of AI in curriculum development and planning.
AI-driven tutoring systems: Benefits and limitations.
The impact of AI on reducing educational disparities.
AI in language learning: Opportunities and challenges.
The future of AI in special education.
AI-driven student performance prediction: Benefits and limitations.
The role of AI in enhancing teacher-student interactions.
AI in educational content creation: Opportunities and challenges.
The impact of AI on educational data privacy and security.
The role of AI in improving educational accessibility.
AI-driven learning management systems: Benefits and limitations.
The future of AI in educational policy and decision-making.
AI in collaborative learning: Opportunities and challenges.
Ethical implications of AI in personalized education.
The role of AI in improving student retention and success.
AI-driven educational games: Benefits and challenges.
The impact of AI on teacher professional development.
The future of AI in lifelong learning and adult education.
AI in educational research: Opportunities and challenges.
The role of AI in enhancing online learning experiences.
AI-driven formative assessment: Benefits and limitations.
The impact of AI on reducing educational administrative burdens.
The future of AI in vocational training and skills development.
AI in student support services: Opportunities and challenges.
The role of AI in improving educational outcomes for marginalized communities.
AI-driven course recommendations: Benefits and challenges.
The impact of AI on student engagement in remote learning.
The future of AI in educational technology integration.
AI in academic advising: Opportunities and challenges.
The role of AI in enhancing peer learning and collaboration.
AI-driven learning analytics: Benefits and limitations.
The impact of AI on improving student well-being and mental health.
The future of AI in educational content delivery.
AI in educational equity: Opportunities and challenges.
The role of AI in improving student feedback and assessment.
AI-driven personalized learning paths: Benefits and challenges.
The impact of AI on student motivation and achievement.
The future of AI in enhancing educational outcomes in developing countries.
AI in student behavior analysis: Opportunities and challenges.
The role of AI in improving educational resource allocation.
AI-driven learning personalization: Benefits and limitations.
The impact of AI on reducing dropout rates in education.
The role of AI in developing adaptive learning systems for students with special needs.
AI-driven assessment tools for personalized feedback in online education.
AI in Marketing and Sales
The role of AI in personalized marketing campaigns.
AI-driven customer segmentation: Opportunities and challenges.
The impact of AI on sales forecasting accuracy.
Ethical considerations in AI-driven marketing strategies.
The future of AI in automated customer relationship management (CRM).
AI in content marketing: Enhancing engagement and conversion.
The role of AI in optimizing pricing strategies.
AI-driven sales analytics: Benefits and limitations.
The impact of AI on improving customer retention.
AI in social media marketing: Opportunities and challenges.
The future of AI in influencer marketing.
AI-driven product recommendations: Benefits and limitations.
The role of AI in enhancing customer experience in e-commerce.
AI in targeted advertising: Opportunities and challenges.
The impact of AI on reducing customer churn.
The role of AI in improving lead generation and qualification.
AI-driven marketing automation: Benefits and limitations.
The future of AI in customer journey mapping.
AI in sales performance analysis: Opportunities and challenges.
Ethical implications of AI in personalized advertising.
The role of AI in improving customer satisfaction and loyalty.
AI-driven sentiment analysis in marketing: Benefits and challenges.
The impact of AI on cross-selling and upselling strategies.
The future of AI in dynamic pricing and demand forecasting.
AI in customer lifetime value prediction: Opportunities and challenges.
The role of AI in enhancing marketing campaign effectiveness.
AI-driven behavioral targeting: Benefits and limitations.
The impact of AI on improving salesforce productivity.
The future of AI in conversational marketing.
AI in predictive lead scoring: Opportunities and challenges.
The role of AI in improving marketing return on investment (ROI).
AI-driven personalization in digital marketing: Benefits and challenges.
The impact of AI on customer acquisition strategies.
The future of AI in programmatic advertising.
AI in customer sentiment analysis: Opportunities and challenges.
The role of AI in improving customer feedback analysis.
AI-driven marketing analytics: Benefits and limitations.
The impact of AI on optimizing marketing budgets.
The future of AI in customer engagement and interaction.
AI in sales enablement: Opportunities and challenges.
The role of AI in enhancing brand loyalty and advocacy.
AI-driven demand forecasting in retail: Benefits and limitations.
The impact of AI on improving customer acquisition costs.
The future of AI in omni-channel marketing strategies.
AI in customer journey optimization: Opportunities and challenges.
The role of AI in improving sales pipeline management.
AI-driven marketing performance measurement: Benefits and challenges.
The impact of AI on enhancing customer lifetime value.
The future of AI in predictive marketing analytics.
The impact of AI on real-time dynamic pricing strategies in e-commerce.
AI in Cybersecurity
The role of AI in detecting and preventing cyberattacks.
AI-driven threat intelligence: Opportunities and challenges.
The impact of AI on improving network security.
Ethical considerations in AI-driven cybersecurity solutions.
The future of AI in securing critical infrastructure.
AI in fraud detection and prevention: Benefits and limitations.
The role of AI in enhancing endpoint security.
AI-driven malware detection: Opportunities and challenges.
The impact of AI on improving data breach detection.
AI in phishing detection and prevention: Opportunities and challenges.
The future of AI in automated incident response.
AI in cybersecurity risk assessment: Benefits and limitations.
The role of AI in enhancing user authentication systems.
AI-driven vulnerability management: Opportunities and challenges.
The impact of AI on improving email security.
The role of AI in securing cloud computing environments.
AI in cybersecurity analytics: Benefits and challenges.
The future of AI in predictive threat modeling.
AI in behavioral analysis for cybersecurity: Opportunities and limitations.
Ethical implications of AI in automated cybersecurity decisions.
The role of AI in improving cybersecurity threat hunting.
AI-driven anomaly detection in cybersecurity: Benefits and challenges.
The impact of AI on reducing false positives in threat detection.
The future of AI in cybersecurity automation.
AI in securing Internet of Things (IoT) devices: Opportunities and challenges.
The role of AI in enhancing threat intelligence sharing.
AI-driven incident detection and response: Benefits and limitations.
The impact of AI on improving cybersecurity training and awareness.
The future of AI in identity and access management.
AI in securing mobile devices: Opportunities and challenges.
The role of AI in improving cybersecurity policy enforcement.
AI-driven network traffic analysis for cybersecurity: Benefits and challenges.
The impact of AI on securing remote work environments.
The future of AI in zero-trust security models.
AI in securing blockchain networks: Opportunities and challenges.
The role of AI in improving cybersecurity for critical industries.
AI-driven cyber threat prediction: Benefits and limitations.
The impact of AI on improving incident response times.
The future of AI in securing supply chains.
AI in cybersecurity for autonomous systems: Opportunities and challenges.
The role of AI in enhancing cybersecurity compliance.
AI-driven deception technologies for cybersecurity: Benefits and challenges.
The impact of AI on reducing the cost of cybersecurity.
The future of AI in cybersecurity governance and regulation.
AI in securing financial institutions: Opportunities and challenges.
The role of AI in improving cybersecurity in healthcare.
AI-driven threat detection in social media: Benefits and challenges.
The impact of AI on securing smart cities.
The future of AI in improving cybersecurity resilience.
The role of AI in detecting and mitigating insider threats within organizations.
Explainable AI (XAI)
The role of explainable AI in improving transparency.
Ethical considerations in developing explainable AI models.
The impact of explainable AI on trust in AI systems.
Challenges in ensuring the explainability of complex AI models.
The future of explainable AI in healthcare decision-making.
Explainable AI in autonomous systems: Opportunities and challenges.
The role of explainable AI in enhancing regulatory compliance.
The impact of explainable AI on financial decision-making.
Explainable AI in predictive analytics: Benefits and limitations.
The future of explainable AI in personalized education.
The role of explainable AI in improving user understanding of AI decisions.
Explainable AI in cybersecurity: Opportunities and challenges.
The impact of explainable AI on reducing bias in AI models.
The future of explainable AI in automated decision-making.
Explainable AI in fraud detection: Benefits and limitations.
The role of explainable AI in enhancing AI-driven content moderation.
The impact of explainable AI on improving AI model transparency.
Explainable AI in autonomous vehicles: Opportunities and challenges.
The future of explainable AI in personalized healthcare.
The role of explainable AI in improving AI ethics and accountability.
Explainable AI in customer experience management: Benefits and limitations.
The impact of explainable AI on enhancing user trust in AI systems.
The future of explainable AI in financial services.
Explainable AI in recommendation systems: Opportunities and challenges.
The role of explainable AI in improving decision support systems.
The impact of explainable AI on increasing transparency in AI-driven decisions.
Explainable AI in social media algorithms: Benefits and challenges.
The future of explainable AI in legal decision-making.
The role of explainable AI in improving AI-driven content recommendations.
Explainable AI in predictive maintenance: Opportunities and challenges.
The impact of explainable AI on improving AI model interpretability.
The future of explainable AI in autonomous robotics.
Explainable AI in healthcare diagnostics: Benefits and limitations.
The role of explainable AI in improving fairness and equity in AI decisions.
The impact of explainable AI on enhancing AI-driven marketing strategies.
Explainable AI in natural language processing: Opportunities and challenges.
The future of explainable AI in enhancing human-AI collaboration.
The role of explainable AI in improving AI transparency in financial markets.
Explainable AI in human resources: Benefits and limitations.
The impact of explainable AI on improving AI model robustness.
The future of explainable AI in AI-driven public policy decisions.
Explainable AI in machine learning models: Opportunities and challenges.
The role of explainable AI in improving the explainability of AI-driven predictions.
The impact of explainable AI on increasing accountability in AI systems.
Explainable AI in AI-driven legal decisions: Benefits and limitations.
The future of explainable AI in enhancing AI-driven content filtering.
The role of explainable AI in improving AI model fairness.
Explainable AI in human-AI interactions: Opportunities and challenges.
The impact of explainable AI on improving AI transparency in autonomous systems.
The future of explainable AI in improving user confidence in AI decisions.
AI and Big Data
The role of AI in big data analytics.
AI-driven data mining: Opportunities and challenges.
The impact of AI on big data processing and storage.
Ethical considerations in AI-driven big data analysis.
The future of AI in predictive analytics with big data.
AI in big data visualization: Enhancing interpretability and insights.
The role of AI in improving big data quality and accuracy.
AI-driven real-time data processing: Benefits and limitations.
The impact of AI on big data-driven decision-making.
AI in big data security and privacy: Opportunities and challenges.
The future of AI in big data-driven marketing strategies.
AI in big data integration: Benefits and limitations.
The role of AI in enhancing big data scalability.
AI-driven big data personalization: Opportunities and challenges.
The impact of AI on big data-driven healthcare solutions.
The future of AI in big data-driven financial services.
AI in big data-driven business intelligence: Benefits and limitations.
The role of AI in improving big data-driven risk management.
AI-driven big data clustering: Opportunities and challenges.
The impact of AI on big data-driven predictive maintenance.
The future of AI in big data-driven smart city initiatives.
AI in big data-driven customer analytics: Benefits and limitations.
The role of AI in improving big data-driven supply chain management.
AI-driven big data sentiment analysis: Opportunities and challenges.
The impact of AI on big data-driven product development.
The future of AI in big data-driven personalized healthcare.
AI in big data-driven financial forecasting: Benefits and limitations.
The role of AI in improving big data-driven marketing automation.
AI-driven big data anomaly detection: Opportunities and challenges.
The impact of AI on big data-driven fraud detection.
The future of AI in big data-driven autonomous systems.
AI in big data-driven customer experience management: Benefits and limitations.
The role of AI in improving big data-driven environmental monitoring.
AI-driven big data trend analysis: Opportunities and challenges.
The impact of AI on big data-driven social media analysis.
The future of AI in big data-driven energy management.
AI in big data-driven real-time analytics: Benefits and limitations.
The role of AI in improving big data-driven financial risk assessment.
AI-driven big data optimization: Opportunities and challenges.
The impact of AI on big data-driven marketing personalization.
The future of AI in big data-driven fraud prevention.
AI in big data-driven predictive analytics: Benefits and limitations.
The role of AI in improving big data-driven financial reporting.
AI-driven big data clustering and classification: Opportunities and challenges.
The impact of AI on big data-driven public health initiatives.
The future of AI in big data-driven manufacturing processes.
AI in big data-driven supply chain optimization: Benefits and limitations.
The role of AI in improving big data-driven energy consumption analysis.
AI-driven big data forecasting: Opportunities and challenges.
AI-driven predictive maintenance using big data analytics in industrial settings.
AI in Gaming
The role of AI in game design and development.
AI-driven procedural content generation: Opportunities and challenges.
The impact of AI on player behavior analysis.
Ethical considerations in AI-driven game development.
The future of AI in adaptive game difficulty.
AI in non-player character (NPC) behavior modeling: Benefits and limitations.
The role of AI in enhancing multiplayer gaming experiences.
AI-driven game testing and quality assurance: Opportunities and challenges.
The impact of AI on player engagement and retention.
AI in game level design: Opportunities and challenges.
The future of AI in virtual and augmented reality gaming.
AI in player emotion recognition: Benefits and limitations.
The role of AI in improving game balancing and fairness.
AI-driven personalized gaming experiences: Opportunities and challenges.
The impact of AI on real-time strategy (RTS) game development.
The future of AI in narrative-driven games.
AI in player behavior prediction: Benefits and limitations.
The role of AI in enhancing game graphics and animation.
AI-driven player matchmaking: Opportunities and challenges.
The impact of AI on game monetization strategies.
The future of AI in educational games.
AI in procedural terrain generation: Benefits and limitations.
The role of AI in improving game physics simulations.
AI-driven in-game advertising: Opportunities and challenges.
The impact of AI on social interaction in online games.
The future of AI in e-sports and competitive gaming.
AI in game world generation: Benefits and limitations.
The role of AI in enhancing virtual economies in games.
AI-driven dynamic storytelling in games: Opportunities and challenges.
The impact of AI on game analytics and player insights.
The future of AI in immersive gaming experiences.
AI in game character animation: Benefits and limitations.
The role of AI in improving game audio and sound design.
AI-driven game difficulty scaling: Opportunities and challenges.
The impact of AI on procedural generation of game assets.
The future of AI in real-time multiplayer games.
AI in game user interface (UI) design: Benefits and limitations.
The role of AI in enhancing player feedback and interaction.
AI-driven game content recommendation: Opportunities and challenges.
The impact of AI on improving player onboarding in games.
The future of AI in game storytelling and narrative generation.
AI in game performance optimization: Benefits and limitations.
The role of AI in improving player immersion in games.
AI-driven game event prediction: Opportunities and challenges.
The impact of AI on real-time game data analysis.
The future of AI in game modding and customization.
AI in game asset creation: Benefits and limitations.
The role of AI in enhancing player agency in games.
AI-driven player engagement analysis: Opportunities and challenges.
The impact of AI on the evolution of game genres.
AI in Natural Sciences
The role of AI in analyzing large-scale scientific data.
AI-driven climate modeling: Opportunities and challenges.
The impact of AI on genomics and precision medicine.
Ethical considerations in AI-driven scientific research.
The future of AI in environmental monitoring and conservation.
AI in drug discovery and development: Benefits and limitations.
The role of AI in improving weather forecasting accuracy.
AI-driven ecological modeling: Opportunities and challenges.
The impact of AI on space exploration and astronomy.
The future of AI in analyzing complex biological systems.
AI in chemical analysis and molecular modeling: Benefits and limitations.
The role of AI in enhancing agricultural productivity.
AI-driven geological modeling: Opportunities and challenges.
The impact of AI on improving water resource management.
The future of AI in biodiversity conservation.
AI in synthetic biology: Benefits and limitations.
The role of AI in improving energy consumption analysis.
AI-driven environmental impact assessment: Opportunities and challenges.
The impact of AI on natural disaster prediction and management.
The future of AI in personalized medicine and healthcare.
AI in renewable energy optimization: Benefits and limitations.
The role of AI in enhancing soil and crop analysis.
AI-driven analysis of ecological networks: Opportunities and challenges.
The impact of AI on improving forest management and conservation.
The future of AI in studying complex ecological systems.
AI in marine biology and oceanography: Benefits and limitations.
The role of AI in improving the accuracy of geological surveys.
AI-driven environmental data analysis: Opportunities and challenges.
The impact of AI on studying climate change and its effects.
The future of AI in developing sustainable agriculture practices.
AI in studying animal behavior and ecology: Benefits and limitations.
The role of AI in improving resource management and conservation.
AI-driven analysis of atmospheric data: Opportunities and challenges.
The impact of AI on improving environmental sustainability.
The future of AI in studying natural hazards and risks.
AI in environmental pollution monitoring: Benefits and limitations.
The role of AI in enhancing the study of complex ecosystems.
AI-driven analysis of meteorological data: Opportunities and challenges.
The impact of AI on improving agricultural sustainability.
The future of AI in studying the impact of human activities on ecosystems.
AI in studying plant biology and genetics: Benefits and limitations.
The role of AI in improving the understanding of climate dynamics.
AI-driven analysis of geological formations: Opportunities and challenges.
The impact of AI on improving environmental impact modeling.
The future of AI in studying the impact of climate change on biodiversity.
AI in studying ocean circulation patterns: Benefits and limitations.
The role of AI in improving the study of natural resource management.
AI-driven analysis of ecological data: Opportunities and challenges.
The impact of AI on improving environmental policy decisions.
The role of AI in predicting and modeling the effects of climate change on biodiversity.
AI in Human-Computer Interaction (HCI)
The role of AI in enhancing user interface design.
AI-driven user experience (UX) optimization: Opportunities and challenges.
The impact of AI on improving accessibility in digital interfaces.
Ethical considerations in AI-driven HCI research.
The future of AI in adaptive user interfaces.
AI in natural language interfaces: Benefits and limitations.
The role of AI in improving user feedback mechanisms.
AI-driven personalization in HCI: Opportunities and challenges.
The impact of AI on reducing cognitive load in user interfaces.
The future of AI in virtual and augmented reality interfaces.
AI in gesture recognition for HCI: Benefits and limitations.
The role of AI in enhancing multimodal interaction.
AI-driven emotion recognition in HCI: Opportunities and challenges.
The impact of AI on improving user engagement in digital environments.
The future of AI in voice user interfaces (VUIs).
AI in improving user satisfaction in HCI: Benefits and limitations.
The role of AI in enhancing social interaction in digital platforms.
AI-driven predictive analytics in HCI: Opportunities and challenges.
The impact of AI on reducing user frustration in digital interfaces.
The future of AI in personalized HCI experiences.
AI in eye-tracking interfaces: Benefits and limitations.
The role of AI in improving user interaction in smart home systems.
AI-driven adaptive learning in HCI: Opportunities and challenges.
The impact of AI on improving user trust in digital systems.
The future of AI in conversational interfaces.
AI in improving the usability of digital platforms: Benefits and limitations.
The role of AI in enhancing collaborative work in HCI.
AI-driven human-robot interaction: Opportunities and challenges.
The impact of AI on reducing user errors in digital interfaces.
The future of AI in enhancing user autonomy in HCI.
AI in improving the personalization of digital content: Benefits and limitations.
The role of AI in enhancing HCI for people with disabilities.
AI-driven adaptive user interfaces: Opportunities and challenges.
The impact of AI on improving user satisfaction in online platforms.
The future of AI in enhancing emotional interaction in HCI.
AI in improving user interaction in wearable devices: Benefits and limitations.
The role of AI in enhancing trust and transparency in HCI.
AI-driven predictive modeling in HCI: Opportunities and challenges.
The impact of AI on improving user interaction in educational platforms.
The future of AI in enhancing the accessibility of digital tools.
AI in improving the personalization of online services: Benefits and limitations.
The role of AI in enhancing user experience in e-commerce platforms.
AI-driven human-centered design in HCI: Opportunities and challenges.
The impact of AI on improving user satisfaction in healthcare interfaces.
The future of AI in enhancing user interaction in gaming.
AI in improving the personalization of digital advertisements: Benefits and limitations.
The role of AI in enhancing the user experience in digital learning environments.
AI-driven user behavior analysis in HCI: Opportunities and challenges.
The impact of AI on improving the user experience in virtual environments.
The impact of AI on enhancing adaptive user interfaces for individuals with disabilities.
AI in Social Media
The role of AI in social media content moderation.
AI-driven sentiment analysis in social media: Opportunities and challenges.
The impact of AI on personalized content recommendations in social media.
Ethical considerations in AI-driven social media algorithms.
The future of AI in detecting fake news on social media platforms.
AI in enhancing user engagement on social media: Benefits and limitations.
The role of AI in social media advertising optimization.
AI-driven influencer marketing on social media: Opportunities and challenges.
The impact of AI on improving user privacy on social media platforms.
The future of AI in social media trend analysis.
AI in identifying and mitigating cyberbullying on social media: Benefits and limitations.
The role of AI in improving social media analytics.
AI-driven personalized marketing on social media: Opportunities and challenges.
The impact of AI on social media user behavior analysis.
The future of AI in enhancing social media customer support.
AI in social media crisis management: Benefits and limitations.
The role of AI in improving social media content creation.
AI-driven predictive analytics in social media: Opportunities and challenges.
The impact of AI on social media user retention.
The future of AI in automating social media interactions.
AI in social media brand management: Benefits and limitations.
The role of AI in enhancing social media influencer engagement.
AI-driven social media monitoring: Opportunities and challenges.
The impact of AI on improving social media content curation.
The future of AI in social media sentiment tracking.
AI in social media user segmentation: Benefits and limitations.
The role of AI in enhancing social media marketing campaigns.
AI-driven social media listening: Opportunities and challenges.
The impact of AI on improving social media user experience.
The future of AI in social media content personalization.
AI in social media audience analysis: Benefits and limitations.
The role of AI in enhancing social media influencer marketing strategies.
AI-driven social media engagement analysis: Opportunities and challenges.
The impact of AI on improving social media ad targeting.
The future of AI in social media content generation.
AI in social media sentiment prediction: Benefits and limitations.
The role of AI in improving social media crisis communication.
AI-driven social media data analysis: Opportunities and challenges.
The impact of AI on improving social media brand loyalty.
The future of AI in enhancing social media video content.
AI in social media campaign optimization: Benefits and limitations.
The role of AI in enhancing social media content discovery.
AI-driven social media trend prediction: Opportunities and challenges.
The impact of AI on improving social media customer engagement.
The future of AI in social media user feedback analysis.
AI in social media event detection: Benefits and limitations.
The role of AI in enhancing social media influencer analytics.
AI-driven social media sentiment analysis: Opportunities and challenges.
The impact of AI on improving social media content strategy.
The role of AI in detecting and curbing the spread of misinformation on social media platforms.
AI in Supply Chain Management
The role of AI in optimizing supply chain logistics.
AI-driven demand forecasting in supply chains: Opportunities and challenges.
The impact of AI on improving supply chain resilience.
Ethical considerations in AI-driven supply chain management.
The future of AI in supply chain risk management.
AI in inventory management: Benefits and limitations.
The role of AI in enhancing supply chain transparency.
AI-driven supplier selection and evaluation: Opportunities and challenges.
The impact of AI on reducing supply chain costs.
The future of AI in supply chain sustainability.
AI in supply chain network design: Benefits and limitations.
The role of AI in improving supply chain agility.
AI-driven demand planning in supply chains: Opportunities and challenges.
The impact of AI on supply chain decision-making.
The future of AI in supply chain digitalization.
AI in supply chain collaboration: Benefits and limitations.
The role of AI in enhancing supply chain forecasting accuracy.
AI-driven supply chain optimization: Opportunities and challenges.
The impact of AI on improving supply chain efficiency.
The future of AI in supply chain automation.
AI in supply chain risk assessment: Benefits and limitations.
The role of AI in enhancing supply chain innovation.
AI-driven supply chain analytics: Opportunities and challenges.
The impact of AI on improving supply chain customer service.
The future of AI in supply chain resilience planning.
AI in supply chain cost optimization: Benefits and limitations.
The role of AI in enhancing supply chain decision support systems.
AI-driven supply chain performance measurement: Opportunities and challenges.
The impact of AI on improving supply chain visibility.
The future of AI in supply chain strategy development.
AI in supply chain process automation: Benefits and limitations.
The role of AI in enhancing supply chain risk mitigation.
AI-driven supply chain scenario analysis: Opportunities and challenges.
The impact of AI on improving supply chain flexibility.
The future of AI in supply chain predictive analytics.
AI in supply chain quality management: Benefits and limitations.
The role of AI in enhancing supply chain cost management.
AI-driven supply chain optimization for e-commerce: Opportunities and challenges.
The impact of AI on improving supply chain sustainability practices.
The future of AI in supply chain network optimization.
AI in supply chain inventory optimization: Benefits and limitations.
The role of AI in enhancing supply chain collaboration and communication.
AI-driven supply chain forecasting for global markets: Opportunities and challenges.
The impact of AI on improving supply chain responsiveness.
The future of AI in supply chain digital transformation.
AI in supply chain procurement optimization: Benefits and limitations.
The role of AI in enhancing supply chain agility and adaptability.
AI-driven supply chain cost reduction: Opportunities and challenges.
The impact of AI on improving supply chain planning accuracy.
The impact of AI on real-time supply chain visibility and tracking.
Reinforcement Learning
Advances in deep reinforcement learning algorithms.
The impact of reinforcement learning on robotic control.
Ethical considerations in reinforcement learning applications.
The future of reinforcement learning in game AI development.
Reinforcement learning in financial decision-making: Benefits and limitations.
The role of reinforcement learning in optimizing resource allocation.
Reinforcement learning-driven traffic management: Opportunities and challenges.
The impact of reinforcement learning on improving industrial automation.
The future of reinforcement learning in personalized education.
Reinforcement learning in healthcare decision-making: Benefits and limitations.
The role of reinforcement learning in improving supply chain management.
Reinforcement learning-driven energy management: Opportunities and challenges.
The impact of reinforcement learning on real-time strategy games.
The future of reinforcement learning in smart city management.
Reinforcement learning in adaptive user interfaces: Benefits and limitations.
The role of reinforcement learning in optimizing marketing strategies.
Reinforcement learning-driven personalized recommendations: Opportunities and challenges.
The impact of reinforcement learning on improving cybersecurity.
The future of reinforcement learning in autonomous robotics.
Reinforcement learning in finance: Portfolio optimization benefits and limitations.
The role of reinforcement learning in enhancing autonomous vehicle navigation.
Reinforcement learning-driven customer segmentation: Opportunities and challenges.
The impact of reinforcement learning on improving warehouse management.
The future of reinforcement learning in adaptive learning systems.
Reinforcement learning in robotics: Task planning benefits and limitations.
The role of reinforcement learning in improving smart grid management.
Reinforcement learning-driven demand forecasting: Opportunities and challenges.
The impact of reinforcement learning on improving industrial robotics.
The future of reinforcement learning in autonomous drone navigation.
Reinforcement learning in financial market prediction: Benefits and limitations.
The role of reinforcement learning in enhancing real-time decision-making.
Reinforcement learning-driven customer experience optimization: Opportunities and challenges.
The impact of reinforcement learning on improving logistics and transportation.
The future of reinforcement learning in autonomous warehouse robots.
Reinforcement learning in natural language processing: Benefits and limitations.
The role of reinforcement learning in improving process automation.
Reinforcement learning-driven resource management: Opportunities and challenges.
The impact of reinforcement learning on improving energy efficiency.
The future of reinforcement learning in adaptive marketing strategies.
Reinforcement learning in healthcare: Personalized treatment benefits and limitations.
The role of reinforcement learning in enhancing robotic perception.
Reinforcement learning-driven financial modeling: Opportunities and challenges.
The impact of reinforcement learning on improving product recommendations.
The future of reinforcement learning in autonomous industrial systems.
Reinforcement learning in game theory: Benefits and limitations.
The role of reinforcement learning in improving industrial control systems.
Reinforcement learning-driven supply chain optimization: Opportunities and challenges.
The impact of reinforcement learning on improving predictive analytics.
The application of reinforcement learning in optimizing robotic grasping and manipulation tasks.
AI and Quantum Computing
The role of quantum computing in advancing AI algorithms.
Quantum machine learning: Opportunities and challenges.
The impact of quantum computing on AI-driven optimization.
Ethical considerations in AI and quantum computing applications.
The future of AI in quantum cryptography.
Quantum-enhanced AI for big data analysis: Benefits and limitations.
The role of quantum computing in improving AI model training.
Quantum AI in drug discovery: Opportunities and challenges.
The impact of quantum computing on AI-driven financial modeling.
The future of AI in quantum machine learning algorithms.
Quantum-enhanced AI for natural language processing: Benefits and limitations.
The role of quantum computing in improving AI model interpretability.
Quantum AI in healthcare: Personalized medicine opportunities and challenges.
The impact of quantum computing on AI-driven climate modeling.
The future of AI in quantum-enhanced optimization problems.
Quantum-enhanced AI for real-time data processing: Benefits and limitations.
The role of quantum computing in advancing reinforcement learning.
Quantum AI in materials science: Discovery opportunities and challenges.
The impact of quantum computing on AI-driven supply chain optimization.
The future of AI in quantum-enhanced cybersecurity.
Quantum-enhanced AI for image recognition: Benefits and limitations.
The role of quantum computing in improving AI-driven decision-making.
Quantum AI in financial portfolio optimization: Opportunities and challenges.
The impact of quantum computing on AI-driven personalized marketing.
The future of AI in quantum-enhanced predictive analytics.
Quantum-enhanced AI for autonomous systems: Benefits and limitations.
The role of quantum computing in improving AI-driven fraud detection.
Quantum AI in personalized healthcare: Opportunities and challenges.
The impact of quantum computing on AI-driven smart city management.
The future of AI in quantum-enhanced industrial automation.
Quantum-enhanced AI for natural language understanding: Benefits and limitations.
The role of quantum computing in advancing AI-driven robotics.
Quantum AI in financial risk assessment: Opportunities and challenges.
The impact of quantum computing on AI-driven environmental modeling.
The future of AI in quantum-enhanced supply chain resilience.
Quantum-enhanced AI for medical imaging: Benefits and limitations.
The role of quantum computing in improving AI-driven cybersecurity.
Quantum AI in healthcare diagnostics: Opportunities and challenges.
The impact of quantum computing on AI-driven predictive maintenance.
The future of AI in quantum-enhanced autonomous vehicles.
Quantum-enhanced AI for financial market prediction: Benefits and limitations.
The role of quantum computing in advancing AI-driven drug discovery.
Quantum AI in personalized education: Opportunities and challenges.
The impact of quantum computing on AI-driven traffic management.
The future of AI in quantum-enhanced logistics optimization.
Quantum-enhanced AI for smart home systems: Benefits and limitations.
The role of quantum computing in improving AI-driven energy management.
Quantum AI in natural disaster prediction: Opportunities and challenges.
The impact of quantum computing on AI-driven personalized advertising.
Quantum-enhanced AI for optimizing complex supply chain logistics.

This extensive list of artificial intelligence thesis topics provides a robust foundation for students eager to explore the various dimensions of AI. By covering current issues, recent trends, and future directions, these topics offer a valuable starting point for deep, meaningful research that contributes to the ongoing advancements in AI. Whether you are focused on ethical considerations, technological innovations, or the integration of AI with other emerging technologies, these topics are designed to help you navigate the complex and rapidly evolving landscape of artificial intelligence.

The Range of Artificial Intelligence Thesis Topics

Artificial intelligence (AI) is a rapidly expanding field that has become integral to numerous industries, influencing everything from healthcare and finance to education and entertainment. As AI continues to evolve, it offers a vast array of thesis topics for students, each reflecting the depth and diversity of the discipline. The range of topics within AI not only allows students to explore their specific areas of interest but also provides an opportunity to contribute to the ongoing development of this transformative technology. Selecting a relevant and impactful thesis topic is crucial, as it can help shape the direction of one’s research and career, while also addressing significant challenges and opportunities in the field.

Current Issues in Artificial Intelligence

The field of artificial intelligence is currently facing several pressing issues that are critical to its development and application. One of the foremost challenges is the ethical considerations surrounding AI. As AI systems become more autonomous, the decisions they make can have profound implications, particularly in areas such as law enforcement, healthcare, and finance. The potential for AI to perpetuate or even exacerbate societal biases is a major concern, especially in systems that rely on historical data, which may contain inherent biases. Thesis topics such as “The Role of Ethics in AI Decision-Making” or “Addressing Bias in Machine Learning Algorithms” are crucial for students who wish to explore solutions to these ethical dilemmas.

Another significant issue in AI is the challenge of data privacy. As AI systems often require vast amounts of data to function effectively, the collection, storage, and use of this data raise important privacy concerns. With increasing scrutiny on how personal data is handled, particularly in light of regulations like the GDPR, ensuring that AI systems are both effective and respectful of user privacy is paramount. Students might consider thesis topics such as “Balancing Data Privacy and AI Innovation” or “The Impact of Data Privacy Regulations on AI Development” to delve into this critical area.

Furthermore, the transparency and explainability of AI models have become vital issues, particularly as AI systems are deployed in high-stakes environments such as healthcare and criminal justice. The so-called “black box” nature of many AI models, particularly deep learning algorithms, can make it difficult to understand how decisions are made, leading to concerns about accountability and trust. Topics like “Enhancing Explainability in AI Systems” or “The Importance of Transparency in AI Decision-Making” would allow students to explore these challenges and propose solutions that could improve the trustworthiness of AI systems.

Recent Trends in Artificial Intelligence

In addition to addressing current issues, artificial intelligence is also being shaped by several recent trends that are driving its development and application across various domains. One of the most significant trends is the rise of deep learning, a subset of machine learning that has achieved remarkable success in tasks such as image and speech recognition. Deep learning models, particularly neural networks, have revolutionized fields like computer vision and natural language processing (NLP), enabling new applications in areas such as autonomous vehicles and virtual assistants. Thesis topics that align with this trend include “Advances in Convolutional Neural Networks for Image Recognition” or “The Role of Deep Learning in Natural Language Processing.”

AI’s growing presence in healthcare is another major trend. From diagnostic tools to personalized treatment plans, AI is transforming the way healthcare is delivered. AI-driven systems can analyze vast datasets to identify patterns that may not be apparent to human clinicians, leading to earlier diagnoses and more effective treatments. The application of AI in genomics, for example, is paving the way for precision medicine, where treatments are tailored to the genetic profiles of individual patients. Students interested in this trend might explore topics such as “The Impact of AI on Precision Medicine” or “AI in Healthcare: Opportunities and Challenges.”

The development and deployment of autonomous systems, such as self-driving cars and drones, represent another significant trend in AI. These systems rely on advanced AI algorithms to navigate complex environments, make real-time decisions, and interact with humans and other machines. The challenges of ensuring safety, reliability, and ethical operation in these systems are ongoing areas of research. Thesis topics like “The Future of AI in Autonomous Vehicles” or “AI in Robotics: Balancing Autonomy and Safety” offer opportunities for students to contribute to this rapidly advancing field.

Future Directions in Artificial Intelligence

Looking ahead, the future of artificial intelligence promises to bring even more profound changes, driven by emerging technologies and new ethical frameworks. One of the most exciting developments on the horizon is the integration of AI with quantum computing. Quantum computing has the potential to exponentially increase the processing power available for AI algorithms, enabling the analysis of complex datasets and the solving of problems that are currently intractable. This could revolutionize fields such as drug discovery, climate modeling, and financial forecasting. Students interested in pioneering research could explore topics such as “Quantum Computing and Its Impact on AI Algorithms” or “The Role of Quantum AI in Solving Complex Problems.”

AI ethics is another area that is expected to see significant advancements. As AI systems become more pervasive, the need for robust ethical guidelines and governance frameworks will become increasingly important. These frameworks will need to address not only issues of bias and transparency but also the broader societal impacts of AI, such as its effect on employment and the distribution of power. Future-oriented thesis topics might include “Developing Ethical Guidelines for Autonomous AI Systems” or “The Role of AI Ethics in Shaping Public Policy.”

Finally, the application of AI in education is poised to transform the way we learn and teach. AI-driven tools can provide personalized learning experiences, adapt to the needs of individual students, and offer real-time feedback to educators. These tools have the potential to democratize education by making high-quality learning resources available to a global audience, regardless of location or socioeconomic status. Students interested in the intersection of AI and education might consider topics such as “The Future of AI in Personalized Learning” or “AI in Education: Bridging the Gap Between Access and Quality.”

In conclusion, the field of artificial intelligence offers a vast and diverse range of thesis topics, each with the potential to contribute to the ongoing development and ethical deployment of AI technologies. Whether addressing current issues such as bias and data privacy, exploring recent trends like deep learning and AI in healthcare, or looking toward future advancements in quantum computing and AI ethics, students have the opportunity to engage with topics that are both relevant and impactful. Selecting a well-defined and forward-thinking thesis topic is crucial for making meaningful contributions to the field and for advancing both academic knowledge and practical applications of AI. The comprehensive list of AI thesis topics provided on this page, along with the insights shared in this article, are valuable resources for students as they embark on their research journey.

iResearchNet’s Thesis Writing Services

At iResearchNet, we pride ourselves on delivering exceptional custom thesis papers on a wide range of artificial intelligence topics. Our team of expert writers, each holding advanced degrees in AI and related fields, is dedicated to providing top-quality work that meets the specific needs and academic standards of every student. Whether you are exploring cutting-edge research in machine learning, delving into the ethical implications of AI, or examining the future of quantum-enhanced AI, iResearchNet is committed to helping you achieve your academic goals with precision and excellence.

Expert degree-holding writers: Our team consists of highly qualified professionals with advanced degrees in artificial intelligence and extensive experience in academic research.
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Choosing iResearchNet for your custom thesis writing needs ensures that you receive a high-quality, expertly crafted paper that meets all your academic requirements. Our commitment to excellence, personalized service, and unwavering dedication to your success make us the ideal partner for your academic journey. Let iResearchNet help you navigate the complexities of artificial intelligence research and produce a thesis that not only meets but exceeds your expectations.

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How to Craft Effective Thesis Statements With AI Writing Tools

August 26, 2024 Updated

how-to-craft-effective-thesis-statements-with-ai-writing-tools

As an academic writer, I’ve found that the thesis statement is a critical element of any paper. It sets the tone and direction for my writing. Recently, I’ve been incorporating best AI writing tools to write thesis into my process, and they’ve significantly transformed how I approach thesis statements.

In this article, I’ll share my experience with some of the best AI writing tools, highlighting how to craft effective thesis statements with AI writing tools. Let’s get into it.

How to Craft Effective Thesis Statements With AI Writing Tools: Step-by-Step Guide

Here, I’ll share my step-by-step approach and teach you how to craft effective thesis statements with AI writing tools.

Step 1: Identifying the Topic

Step 2: narrowing the focus, step 3: formulating the argument, step 4: refining the thesis statement, step 5: seeking feedback, step 6: final review.

The first step in thesis writing is identifying a relevant and engaging topic. AI tools such as OpenAI’s GPT-4 are invaluable in this stage, offering up-to-date suggestions on trending and significant topics.

These tools analyze current research and discussions in various fields, providing me with a broad range of potential topics that are both contemporary and academically relevant.

After identifying your topic, it’s crucial to ensure your writing is clear and error-free. Utilize AI tools using AI to proofread for grammar and clarity to review your thesis statement for any grammatical errors or clarity issues.

Once a topic is chosen, the next challenge is to narrow it down to a specific aspect that is both manageable and significant.

AI tools integrated with databases like Google Scholar or JSTOR aid in this process by analyzing large volumes of data and research papers.

This helps in focusing on a particular aspect or angle of the chosen topic, ensuring that the thesis is both specific and substantive.

Formulating a clear and debatable thesis statement is a critical step. AI-powered tools like Grammarly and Hemingway Editor are useful here.

They offer linguistic analysis, suggesting ways to construct a strong argument that is both clear and persuasive.

These right AI writing tools analyze sentence structure, word choice, and overall readability, ensuring that the thesis statement is cogent and impactful.

Refinement is key to crafting an effective thesis statement. AI tools such as ProWritingAid provide suggestions on improving the clarity and conciseness of the statement.

They help in fine-tuning the language, ensuring that the thesis statement is well-phrased, impactful, and devoid of any ambiguity or redundancy.

Gathering feedback on the thesis statement is crucial. Platforms like Scribbr use AI algorithms to provide constructive feedback on the strength, coherence, and clarity of the thesis statement.

They offer insights into how the statement can be improved, making it more robust and compelling.

Finally, ensuring the originality of the thesis statement is paramount. Tools like Turnitin are essential in this final stage.

They check for originality and uniqueness, ensuring that the thesis statement is free from unintentional plagiarism and stands out in the academic discourse.

What Are the Essentials of a Thesis Statement?

In my writing, I ensure that my thesis statement is clear, concise, and well-defined, demonstrating how to craft effective thesis statements with AI writing tools . It’s the guiding light for the reader, steering them through my argument or analysis.

How-to-craft-effective-thesis statement-with-ai-writing-tools

The key elements I focus on in a strong thesis statement include:

1. Clarity and Conciseness

A thesis statement is the backbone of any well-written academic paper, providing a clear and concise summary of the argument or analysis that follows. It’s essential that this statement is devoid of complex jargon and unnecessarily long sentences.

Clarity ensures immediate comprehension by the reader, while conciseness prevents dilution of the core argument, keeping the reader’s attention focused.

2. Specificity and Focus

The effectiveness of a thesis statement largely depends on its ability to narrow down a broad topic into a specific, focused argument.

This specificity allows for a detailed and in-depth exploration of the subject matter, preventing the paper from veering off into too general or unrelated discussions.

A focused thesis guides the direction of research and writing, ensuring that every element of the paper contributes towards exploring this central argument.

3. Arguable and Defensible

An impactful thesis statement is one that presents a clear argument or perspective, which is not only open to discussion but can also be supported with concrete evidence.

This argumentative nature invites critical thinking and engagement from the reader, prompting them to consider the topic from the writer’s perspective and anticipate the evidence that will be presented in support of this argument.

4. Originality and Insight

In academic writing, a thesis statement should not merely state a fact or a universally accepted truth. Instead, it should offer an original perspective or a novel approach to the topic.

This originality is what contributes to the broader academic dialogue, providing new insights or challenging existing ones.

The thesis should make a unique contribution to the topic, showcasing the writer’s deep understanding and personal interpretation of the subject. The best AI writing tools for academic writing can help ensure originality in your thesis statement.

5. Alignment with the Paper

A thesis statement must be in harmony with the rest of the paper. Every paragraph, argument, and piece of evidence presented in the paper should directly support and reinforce the thesis statement.

This alignment ensures a cohesive and coherent structure, where all elements of the paper work synergistically to argue or analyze the central claim.

How AI Writing Tools Assist in Refining and Rewriting Thesis Statements

In my experience, artificial intelligence tools are excellent at offering alternative phrasings, checking consistency with the overall content, improving clarity, and using AI to write a white paper , embodying how to craft effective thesis statements with AI writing tools.

They suggest various ways to express the thesis and ensure it aligns with the rest of my content, similar to how one might approach choosing the right AI writing tool for your needs .

This process is not only beneficial for academic writing but extends to areas such as writing effective survey questions with AI writing tools, enhancing the way we gather and analyze data.

Refining-and-Rewriting-Thesis-Statements

1. Suggesting Variations

One of the primary ways AI tools assist in thesis writing is by suggesting multiple variations of a thesis statement.

These suggestions provide different ways of expressing the same idea, helping to find the most effective and impactful phrasing.

2. Checking Consistency

AI tools are highly efficient in ensuring that the thesis statement aligns with the overall content and tone of the paper.

They analyze the entire document to ensure that every part of the paper supports and reinforces the thesis, maintaining a cohesive argument throughout.

3. Improving Clarity

AI suggestions are instrumental in enhancing the clarity of the thesis statement. They help rephrase complex or ambiguous statements into clearer, more understandable language. This improvement in clarity is crucial for engaging and retaining the reader’s interest.

4. Enhancing Persuasiveness

AI tools are adept at suggesting more persuasive language and stronger arguments, thereby making the thesis more compelling.

They analyze the persuasive elements of language, such as word choice and sentence structure, to enhance the argumentative power of the thesis statement.

5. Detecting Redundancies

AI tools efficiently identify and eliminate redundant phrases or arguments within the thesis statement. This streamlining ensures that the statement is concise and focused, enhancing its overall impact.

6. Offering Customized Suggestions

Based on the style, tone, and topic of the paper, AI tools provide tailored advice to make the thesis more effective.

These customized suggestions are particularly helpful in ensuring that the thesis statement is well-suited to the specific requirements and expectations of the paper’s intended audience.

Incorporating AI Tools: A Comparative Insight

Comparing AI tools with traditional methods, I find that AI significantly enhances efficiency in idea generation, multilingual content creation , and real-time suggestions, making the process of crafting a thesis statement less time-consuming and more effective.

1. Speed of Research

One of the most significant advantages of using AI tools in thesis writing is the speed at which research can be conducted.

AI tools provide quick and easy access to a vast array of datasets and research materials, a process that would take significantly longer using traditional research methods.

2. Idea Generation

Compared to traditional brainstorming methods, AI tools offer immediate, diverse, and often more creative ideas for thesis statements.

These tools can analyze existing literature and trends to suggest unique angles and perspectives that might not be immediately apparent through conventional brainstorming.

3. Language and Style

In terms of language and style, AI tools offer a level of refinement and sophistication that surpasses traditional manual proofreading and editing.

They analyze the thesis statement for language use, style, tone, and readability, ensuring that it meets high standards of academic writing.

4. Consistency Checking

AI tools are more efficient and accurate in checking for consistency in the thesis statement and throughout the paper.

They ensure that every part of the paper aligns with and supports the central thesis, which can be more challenging to achieve with manual reviews.

5. Accessibility of Resources

When it comes to accessing research materials and references, AI tools provide a broader and more comprehensive range of resources than traditional library-based research.

They offer access to the latest studies, articles, and data from various disciplines, making the research process more efficient and thorough.

6. Plagiarism Detection

AI-powered plagiarism checkers offer a level of thoroughness and accuracy in detecting plagiarism that is difficult to achieve through manual methods.

They scan a wide range of sources, including academic papers and online content, ensuring that the thesis statement is original and free from unintentional plagiarism.

How Do Different Paper Types Influence Thesis Formation?

The nature of the thesis statement depends on whether the paper is analytical, expository, or argumentative. Understanding how to craft effective thesis statements with AI writing tools can greatly assist in tailoring your thesis to fit these specific types of papers effectively.

I use AI tools tailored to understand these differences, which offer appropriate suggestions based on the paper type.

1. Analytical Papers

In analytical papers, the thesis statement should dissect an idea or issue into its essential components, providing a clear and focused analysis of each part.

The thesis must guide the structure of the analysis, determining the key aspects or elements that will be examined in detail.

2. Expository Papers

For expository papers, the thesis should aim to explain or elucidate a particular concept or idea.

It should be informative and educational, providing clarity and insight into the subject.

A thesis in expository papers often outlines the aspects that will be explored to offer a comprehensive understanding of the topic.

3. Argumentative Papers

In argumentative papers, the thesis statement must take a clear and definitive stand on a particular issue or debate. It should present a strong, persuasive argument that is supported by evidence throughout the paper.

In argumentative papers, the thesis statement is often controversial or provocative, aiming to convince the reader of a particular viewpoint.

4. Comparative Papers

The thesis statement in comparative papers should focus on highlighting the similarities and differences between two or more subjects, ideas, or phenomena.

It requires a balanced and nuanced approach, analyzing each subject in relation to the others, and drawing insightful comparisons and contrasts.

5. Narrative Papers

Though less common in academic writing, narrative essays still require a thesis statement. In these papers, the thesis sets the tone for a story or personal experience, focusing on a central theme, message, or lesson.

The thesis statement in narrative essays often reflects the writer’s personal insights or learnings from the experience being shared.

Can AI write my thesis?

Can i use chatgpt for my thesis, can universities detect ai writing, which ai is best for writing a thesis, conclusion: embracing ai for effective thesis writing.

AI writing tools have revolutionized my approach to thesis writing. They serve as powerful aids, but the core of thesis writing still relies on individual critical thinking and expertise.

Now that you know how to craft effective thesis statements with AI writing tools, why not check out integrating AI writing tools into academic research to discover how you can further leverage these tools in your research endeavors?

Check out the articles in the how-to guide to learn more and you can enhance your understanding of our AI terms by visiting our AI glossary as well.

Digital marketing enthusiast by day, nature wanderer by dusk. Dave Andre blends two decades of AI and SaaS expertise into impactful strategies for SMEs. His weekends? Lost in books on tech trends and rejuvenating on scenic trails.

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Essays on Artificial Intelligence

Artificial intelligence essay topics for college students.

Welcome, college students! Writing an essay on artificial intelligence can be an exciting and challenging task. The key to a successful essay lies in selecting the right topic that sparks your interest and allows you to showcase your creativity. In this resource page, we will provide you with a variety of essay types and topics to help you get started on your AI essay journey.

Argumentative Essay Topic for Artificial Intelligence Essays

The ethical implications of AI technology
The impact of AI on job automation
Regulating AI development for societal benefits

Introduction Paragraph Example: Artificial intelligence has revolutionized the way we interact with technology, raising important ethical questions about its implications on society. In this essay, we will explore the ethical challenges of AI technology and discuss the need for regulations to ensure its responsible development.

Conclusion Paragraph Example: In conclusion, it is evident that the ethical implications of AI technology are multifaceted and require careful consideration. By implementing regulations and ethical guidelines, we can harness the benefits of AI while minimizing its potential risks.

Compare and Contrast Essay Topics for Artificial Intelligence

The differences between narrow AI and general AI
Comparing AI in science fiction to real-world applications
The impact of AI on different industries
AI vs. human intelligence: Strengths and weaknesses
Machine learning vs. deep learning
AI in healthcare vs. AI in finance
AI-driven automation vs. traditional automation
Cloud-based AI vs. edge AI
The role of AI in developed vs. developing countries
AI in education vs. AI in entertainment

Introduction Paragraph Example: The field of artificial intelligence encompasses a wide range of technologies, from narrow AI systems designed for specific tasks to the hypothetical concept of general AI capable of human-like intelligence. In this essay, we will compare and contrast the characteristics of narrow and general AI to understand their implications on society.

Conclusion Paragraph Example: Through this comparison, we have gained insights into the diverse applications of AI technology and the potential challenges it poses to various industries. By understanding the differences between narrow and general AI, we can better prepare for the future of artificial intelligence.

Descriptive Essay Essay Topics for Artificial Intelligence

The role of AI in healthcare advancements
The development of AI algorithms for autonomous vehicles
The applications of AI in natural language processing
The architecture of neural networks
The evolution of AI from the 20th century to today
The ethical implications of AI decision-making
The process of training an AI model
The impact of AI on the job market
The future potential of quantum AI
The role of AI in personalized marketing

Introduction Paragraph Example: AI technology has transformed the healthcare industry, enabling innovative solutions that improve patient care and diagnosis accuracy. In this essay, we will explore the role of AI in healthcare advancements and its impact on the future of medicine.

Conclusion Paragraph Example: In conclusion, the integration of AI technology in healthcare has the potential to revolutionize the way we approach patient care and medical research. By leveraging AI algorithms and machine learning capabilities, we can achieve significant advancements in the field of medicine.

Persuasive Essay Essay Topics for Artificial Intelligence

Promoting diversity and inclusion in AI development
The importance of ethical AI education in schools
Advocating for AI transparency and accountability
The necessity of regulating AI technology
Why AI should be used to combat climate change
The benefits of AI in improving public safety
Encouraging responsible AI usage in social media
The potential of AI to revolutionize education
Why businesses should invest in AI technology
The role of AI in enhancing cybersecurity

Introduction Paragraph Example: As artificial intelligence continues to permeate various aspects of our lives, it is essential to prioritize diversity and inclusion in AI development to ensure equitable outcomes for all individuals. In this essay, we will discuss the importance of promoting diversity and inclusion in AI initiatives and the benefits it brings to society.

Conclusion Paragraph Example: By advocating for diversity and inclusion in AI development, we can create a more equitable and socially responsible future for artificial intelligence. Through ethical education and transparent practices, we can build a foundation of trust and accountability in AI technology.

Narrative Essay Essay Topics for Artificial Intelligence

A day in the life of an AI researcher
The journey of building your first AI project
An imaginary conversation with a sentient AI being
The story of a world transformed by AI
How AI solved a major global problem
A personal encounter with AI technology
The evolution of AI in your lifetime
The challenges faced while developing an AI startup
A future where AI coexists with humans
Your experience learning about AI for the first time

Introduction Paragraph Example: Imagine a world where artificial intelligence blurs the lines between human and machine, offering new possibilities and ethical dilemmas. In this narrative essay, we will embark on a journey through the eyes of an AI researcher, exploring the challenges and discoveries that come with pushing the boundaries of technology.

Conclusion Paragraph Example: Through this narrative journey, we have delved into the complexities of artificial intelligence and the ethical considerations that accompany its development. By embracing the possibilities of AI technology while acknowledging its limitations, we can shape a future that balances innovation with ethical responsibility.

Hooks for Artificial Intelligence Essay

"Imagine a world where machines not only perform tasks but also think, learn, and make decisions just like humans. Welcome to the era of Artificial Intelligence (AI), a revolutionary force reshaping our future."
"From self-driving cars to smart personal assistants, AI is seamlessly integrating into our daily lives. But what lies beneath this cutting-edge technology, and how will it transform the way we live and work?"
"As AI continues to advance at an unprecedented pace, questions about its ethical implications and impact on society become more urgent. Can we control the intelligence we create, or will it control us?"
"AI is not just a futuristic concept confined to science fiction. It’s here, and it’s real, influencing industries, healthcare, education, and even our personal lives. How prepared are we for this technological revolution?"
"The debate over AI is heating up: Will it lead to a utopian society with endless possibilities, or is it a Pandora's box with risks we have yet to fully understand? The answers may surprise you."

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Artificial Intelligence: Applications, Advantages and Disanvantages

The possibility of machines to be able to think and feel, artificial intelligence: what really makes us human, how artificial intelligence is transforming the world, risks and benefits of ai in the future, the possibility of artificial intelligence to replace teachers, artificial intelligence, machine learning and deep learning, the ethical challenges of artificial intelligence, will artificial intelligence have a progressive or retrogressive impact on our society, artificial intelligence in medicine, impact of technology: how artificial intelligence will change the future, artificial intelligence in home automation, artificial intelligence and the future of human rights, artificial intelligence (ai) and its impact on our life, impact of artificial intelligence on hr jobs, the ability of artificial intelligence to make society more sustainable, deep learning for artificial intelligence, the role of artificial intelligence in future technology, artificial intelligence against homelessness and hiv, artificial intelligence in radiology.

Artificial intelligence (AI) refers to the intellectual capabilities exhibited by machines, contrasting with the innate intelligence observed in living beings, such as animals and humans.

The inception of artificial intelligence research as an academic field can be traced back to its establishment in 1956. It was during the renowned Dartmouth conference of the same year that artificial intelligence acquired its distinctive name, definitive purpose, initial accomplishments, and notable pioneers, thereby earning its reputation as the birthplace of AI. The esteemed figures of Marvin Minsky and John McCarthy are widely recognized as the founding fathers of this discipline.

The term "artificial intelligence" was coined in 1956 by computer scientist John McCarthy.
McKinsey Global Institute estimates that by 2030, automation and AI technologies could contribute to a global economic impact of $13 trillion.
AI is used in various industries, including healthcare, finance, and transportation.
The healthcare industry is leveraging AI for improved patient care. A study published in the journal Nature Medicine reported that an AI model was able to detect breast cancer with an accuracy of 94.5%, outperforming human radiologists.
Ethical concerns surrounding AI include privacy issues, bias in algorithms, and the potential for job displacement.

Artificial Intelligence is an important topic because it has the potential to revolutionize industries, improve efficiency, and enhance decision-making processes. As AI technology continues to advance, it is crucial for society to understand its implications, both positive and negative, in order to harness its benefits while mitigating its risks.

1. Russell, S. J., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach (3rd ed.). Prentice Hall. 2. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. 3. Kurzweil, R. (2005). The Singularity Is Near: When Humans Transcend Biology. Viking. 4. Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. 5. Chollet, F. (2017). Deep Learning with Python. Manning Publications. 6. Domingos, P. (2018). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books. 7. Ng, A. (2017). Machine Learning Yearning. deeplearning.ai. 8. Marcus, G. (2018). Rebooting AI: Building Artificial Intelligence We Can Trust. Vintage. 9. Winfield, A. (2018). Robotics: A Very Short Introduction. Oxford University Press. 10. Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press.

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163 Unique Artificial Intelligence Topics For Your Dissertation

The artificial intelligence industry is an industry of the future, but it’s also a course many students find difficult to write about. According to some students, the main reason is that there are many research topics on artificial intelligence. Several topics are already covered, and they claim not to know what to write about.

However, one of the interesting things about writing a dissertation or thesis is that you don’t need to be the number one author of an idea. It would be best if you write about the idea from a unique perspective instead. Writing from a unique perspective also means coupling your ideas with original research, giving your long essay quality and value to your professors and other students who may want to cover the same topic in the future.

This blog post will cover basic advanced AI topics and interesting ones for your next research paper or debate. This will help prepare you for your next long essay or presentation.

What is Artificial Intelligence?

Artificial Intelligence (AI) is the concept that enables humans to perform their tasks more smartly and faster through automated systems. AI is human intelligence packed in machines.

AI facilitates several computer systems such as voice recognition, machine vision, natural language processing, robotics engineering, and many others. All these systems revolutionize how work is done in today’s world.

Now that you know what artificial intelligence is, here are some advanced AI topics for your college research.

Writing Tips to Create a Good Thesis or Dissertation

Every student wants to create the best thesis and dissertation in their class. The first step to creating or researching the perfect dissertation is to write a great thesis. What are the things to be on the lookout for?

Create a Strong Thesis Statement You need this to have a concise approach to your research. Your thesis statement should, therefore, be specific, precise, factual, debatable, and logical enough to be an assertive point. Afterwards, the only way to create a competitive dissertation is to draw from existing research in journals and other sources.
Strong Arguments You can create a good dissertation if you have strong arguments. Your arguments must be backed by reputed sources such as academics, government, reputed media organizations, or statistic-oriented websites. All these make your arguments recognizable and accepted.
Well Organized and Logically Structured Your dissertation has different subsections, including an abstract, thesis statement, background to the study, chapters (where your body is), and concluding arguments. If you’ve embarked on quantitative data analysis, you must report the data you got and what it means for your discourse. You can even add recommendations for future research. The information you want to convey must be well structured to improve its reception by your university professors.
Concise and Free of Errors Your essay must also be straightforward. Your ideas must not be complex to understand, and you must always explain ambiguous industry terms. Revising your draft to check for grammatical errors several times is also important. Editing can be difficult, but it’s integral to determining whether your professors will love your dissertation or otherwise.

Artificial Intelligence Research Topics

Artificial intelligence is here to stay in several industries and sectors worldwide. It is the technology of the present and the future, and here are some AI topics to write about:

How will artificial intelligence contribute to the flight to Mars?
Machine learning and the challenges it poses to scientists
How can retail stores maximize machine learning?
Expatiate on what is meant by deep learning
General AI and Narrow AI: what does it mean?
AI changes the world: a case study of the gambling industry
AI improved business: a case study of SaaS industries
AI in homes: how smart homes change how humans live
The critical challenges scientists have not yet solved with AI
How students can contribute to both research and development of AI systems
Is automation the way forward for the interconnected world: an overview of the ethical issues in AI
How does cybernetics connect with AI?
How do artificial intelligence systems manifest in healthcare?
A case for artificial intelligence in how it facilitates the use of data in the criminal department
What are the innovations in the vision system applications
The inductive logic program: meaning and origin
Brain simulation and AI: right or wrong
How to maximize AI in Big data
How AI can increase cybersecurity threat
AI in companies: a case study of Telegram

Hot Topics in Artificial Intelligence

If you’d love to be one of the few who will cover hot topics in AI, researching some sub-sectors could be a way to go. There are several subsections of AI, some of which are hot AI topics causing several arguments among scholars and moralists today. Some of these are:

How natural language is generated and how AI maximizes it
Speech recognition: a case study of Alexa and how it works
How AI makes its decisions
What are known as virtual agents?
Key deep learning platforms for governments
Text analytics and the future of text-to-speech systems
How marketing automation works
Do robots operate based on rules?
AI and emotion recognition
AI and the future of biometrics
AI in content creation
AI and how data is used to create social media addiction
What can be considered core problems with AI?
What do five pieces of literature say about AI taking over the world?
How does AI help with predictive sales?
Motion planning and how AI is used in video editing
Distinguish between data science vs. artificial intelligence
Account for five failed AI experiments in the past decade
The world from the machine’s view
Project management systems from the machine’s view

Artificial Intelligence Topics for Presentation

Students are sometimes fond of presentations to show knowledge or win debates. If you’re in a debate club and would love to add a presentation to your AI topics, here are topics in artificial intelligence for you.

You can even expand these for your artificial intelligence research paper topics:

How AI has penetrated all industries
The future of cloud technologies
The future of AI in military equipment
The evolution of AI in a security application
Industrial robots: an account of Tesla’s factory
Industrial robots: an account of Amazon’s factories
An overview of deep generative models and what they mean
What are the space travel ideas fueling the innovation of AI?
What is amortized inference?
Examine the Monte Carlo methods in AI
How technology has improved maps
Comment on how AI is used to find fresh craters on the moon
Comment on two previous papers from your professor about AI

AI Research Topics

If you’d like to take a general perspective on AI, here are some topics in AI to discuss amongst your friends or for your next essay:

Are robots a threat to human jobs?
How automation has changed the world since 2020
Would you say Tesla produces robot cars?
What are the basic violations of artificial intelligence?
Account for the evolution of AI models
Weapon systems and the future of weaponry
Account for the interaction between machines and humans
Basic principles of AI risk management
How AI protects people against spam
Can AI predict election results?
What are the limits of AI?
Detailed reports on image recognition algorithms in two companies of your choice
How is AI used in customer service?
Telehealth and its significance
Can AI help predict the future?
How to measure water quality and cleanness through AI
Analyze the technology used for the Breathometer products
Key trends in AI and robotics research and development
How AI helps with fraud detection in a bank of your choice
How AI helps the academic industry.

Argument Debate Topics in AI

You’d expect controversial topics in AI, and here are some of them. These are topics for friendly debates in class or topics to start a conversation with industry leaders:

Will humans end all work when AI replaces them?
Who is liable for AI’s misdoing?
AI is smarter than humans: can it be controlled?
Machines will affect human interactions: discuss
AI bias exists and is here to stay
Artificial Intelligence cannot be humanized even if it understands emotions
New wealth and AI: how will it be distributed?
Can humans prevent AI bias?
Can AI be protected from hackers?
What will happen with the unintended consequences of using AI?

Computer Science AI Topics

Every computer science student also needs AI topics for research papers, presentations or scientific thesis . Whatever it is, here are some helpful ideas:

AI and machine learning: how does it help healthcare systems?
What does hierarchical deep learning neural network mean
AI in architecture and engineering: explain
Can robots safely perform surgery?
Can robots help with teaching?
Recent trends in machine learning
Recent trends in big data that will affect the future of the internet of things
How does AI contribute to the excavation management Industry?
Can AI help spot drug distribution?
AI and imaging system: Trends since 1990
Explain five pieces of literature on how AI can be contained
Discuss how AI reduced the escalation of COVID-19
How can natural language processing help interpret sign languages?
Review a recent book about AI and cybersecurity
Discuss the key discoveries from a recent popular seminar on AI and cybercrime
What does Stephen Hawking think about AI?
How did AI make Tesla a possibility?
How recommender systems work in the retail industry
What is the artificial Internet of Things (A-IoT)?
Explain the intricacies of enhanced AI in the pharmaceutical industry

AI Ethics Topics

There are always argumentative debate topics on AI, especially on the ethical and moral components. Here are a few ethical topics in artificial intelligence to discuss:

Is AI the end of all jobs?
Is artificial intelligence in concert with patent law?
Do humans understand machines?
What happens when robots gain self-control?
Can machines make catastrophic mistakes?
What happens when AI reads minds and executes actions even if they’re violent?
What can be done about racist robots?
Comments on how science can mediate human-machine interactions
What does Google CEO mean when he said AI would be the world’s saviour?
What are robots’ rights?
How does power balance shift with a rise in AI development?
How can human privacy be assured when robots are used as police?
What is morality for AI?
Can AI affect the environment?
Discuss ways to keep robots safe from enemies.

AI Essay Topics Technology

Technology is already intertwined with AI, but you may need hot AI topics that focus on the tech side of the innovation. Here are 20 custom topics for you:

How can we understand autonomous driving?
Pros and cons of artificial intelligence to the world?
How does modern science interact with AI?
Account for the scandalous innovations in AI in the 21st century
Account for the most destructive robots ever built
Review a documentary on AI
Review three books or journals that express AI as a threat to humans and draw conclusions based on your thoughts
What do non-experts think about AI?
Discuss the most ingenious robots developed in the past decade
Can the robotic population replace human significance?
Is it possible to be ruled by robots?
What would world domination look like: from the machine perspective
He who controls AI controls the world: discuss
Key areas in AI engineering that man must control
How Apple is using AI for its products
Would you say AI is a positive or negative invention?
AI and video gaming: how it changed the arcade Industry
Would you say eSports is toxic?
How AI helps in the hospitality industry
AI and its use in sustainable energy.

Interesting Topics in AI

There are interesting ways to look at the subject of AI in today’s world. Here are some good research topics for AI to answer some questions:

AI can be toxic: Should a high school student pursue a career in artificial intelligence?
Prediction vs. judgment: experimenting with AI
What makes AI know what’s right or wrong?
Human judgment in AI: explain
Effects of AI on businesses
Will AI play critical roles in human future affairs?
Tech devices and AI
Search application and AI: account for how AI maximizes programming languages
The history of artificial intelligence
How AI impacts market design
Data management and AI: discuss
How can AI influence the future of computing
How AI has changed the video viewing industry
How can AI contribute to the global economy?
How smart would you say artificial intelligence is?

Graduate AI NLP Research Topics

NLP (Natural Language Processing) is the aspect of artificial intelligence or computer science that deals with the ability of machines to understand spoken words and simplify them as humans can. It’s as simple as saying NLP is how computers understand human language.

If you’d like to focus your research topics on artificial intelligence on NLP, here are some topics for you:

How did natural language processing help with Twitter Space discussions?
How language is essential for regulatory and legal texts
NLP in the eCommerce industry: top trends
How NLP is used in language modelling and occlusion
How does AI manoeuvre semantic analysis in natural language processing?
History and top trends in NLP conference video call apps
Text mining techniques and the role of NLP
How physicians detected stroke since 2020 through NLP of radiology results
How does big data contribute to understanding medical acronyms in the NLP section of AI?
What does applied natural language processing mean in the mental health world?

Get Thesis Help Today

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How to Write a Better Thesis Statement Using AI (2023 Updated)

Meredith Sell

With the exceptions of poetry and fiction, every piece of writing needs a thesis statement.

- Opinion pieces for the local newspaper? Yes.

- An essay for a college class? You betcha.

- A book about China’s Ming Dynasty? Absolutely.

All of these pieces of writing need a thesis statement that sums up what they’re about and tells the reader what to expect, whether you’re making an argument, describing something in detail, or exploring ideas.

But how do you write a thesis statement? How do you even come up with one?

This step-by-step guide will show you exactly how — and help you make sure every thesis statement you write has all the parts needed to be clear, coherent, and complete.

Let’s start by making sure we understand what a thesis is (and what it’s not).

What Is a Thesis Statement?

A thesis statement is a one or two sentence long statement that concisely describes your paper’s subject, angle or position — and offers a preview of the evidence or argument your essay will present.

A thesis is not:

An exclamation
A simple fact

Think of your thesis as the road map for your essay. It briefly charts where you’ll start (subject), what you’ll cover (evidence/argument), and where you’ll land (position, angle).

Writing a thesis early in your essay writing process can help you keep your writing focused, so you won’t get off-track describing something that has nothing to do with your central point. Your central point is your thesis, and the rest of your essay fleshes it out.

Get help writing your thesis statement with this FREE AI tool > Get help writing your thesis statement with this FREE AI tool >

Different Kinds of Papers Need Different Kinds of Theses

How you compose your thesis will depend on the type of essay you’re writing. For academic writing, there are three main kinds of essays:

Persuasive, aka argumentative
Expository, aka explanatory

A persuasive essay requires a thesis that clearly states the central stance of the paper , what the rest of the paper will argue in support of.

Paper books are superior to ebooks when it comes to form, function, and overall reader experience.

An expository essay’s thesis sets up the paper’s focus and angle — the paper’s unique take, what in particular it will be describing and why . The why element gives the reader a reason to read; it tells the reader why the topic matters.

Understanding the functional design of physical books can help ebook designers create digital reading experiences that usher readers into literary worlds without technological difficulties.

A narrative essay is similar to that of an expository essay, but it may be less focused on tangible realities and more on intangibles of, for example, the human experience.

The books I’ve read over the years have shaped me, opening me up to worlds and ideas and ways of being that I would otherwise know nothing about.

As you prepare to craft your thesis, think through the goal of your paper. Are you making an argument? Describing the chemical properties of hydrogen? Exploring your relationship with the outdoors? What do you want the reader to take away from reading your piece?

Make note of your paper’s goal and then walk through our thesis-writing process.

Now that you practically have a PhD in theses, let’s learn how to write one:

How to Write (and Develop) a Strong Thesis

If developing a thesis is stressing you out, take heart — basically no one has a strong thesis right away. Developing a thesis is a multi-step process that takes time, thought, and perhaps most important of all: research .

Tackle these steps one by one and you’ll soon have a thesis that’s rock-solid.

1. Identify your essay topic.

Are you writing about gardening? Sword etiquette? King Louis XIV?

With your assignment requirements in mind, pick out a topic (or two) and do some preliminary research . Read up on the basic facts of your topic. Identify a particular angle or focus that’s interesting to you. If you’re writing a persuasive essay, look for an aspect that people have contentious opinions on (and read our piece on persuasive essays to craft a compelling argument).

If your professor assigned a particular topic, you’ll still want to do some reading to make sure you know enough about the topic to pick your specific angle.

For those writing narrative essays involving personal experiences, you may need to do a combination of research and freewriting to explore the topic before honing in on what’s most compelling to you.

Once you have a clear idea of the topic and what interests you, go on to the next step.

2. Ask a research question.

You know what you’re going to write about, at least broadly. Now you just have to narrow in on an angle or focus appropriate to the length of your assignment. To do this, start by asking a question that probes deeper into your topic.

This question may explore connections between causes and effects, the accuracy of an assumption you have, or a value judgment you’d like to investigate, among others.

For example, if you want to write about gardening for a persuasive essay and you’re interested in raised garden beds, your question could be:

What are the unique benefits of gardening in raised beds versus on the ground? Is one better than the other?

Or if you’re writing about sword etiquette for an expository essay , you could ask:

How did sword etiquette in Europe compare to samurai sword etiquette in Japan?

How does medieval sword etiquette influence modern fencing?

Kickstart your curiosity and come up with a handful of intriguing questions. Then pick the two most compelling to initially research (you’ll discard one later).

3. Answer the question tentatively.

You probably have an initial thought of what the answer to your research question is. Write that down in as specific terms as possible. This is your working thesis .

Gardening in raised beds is preferable because you won’t accidentally awaken dormant weed seeds — and you can provide more fertile soil and protection from invasive species.

Medieval sword-fighting rituals are echoed in modern fencing etiquette.

Why is a working thesis helpful?

Both your research question and your working thesis will guide your research. It’s easy to start reading anything and everything related to your broad topic — but for a 4-, 10-, or even 20-page paper, you don’t need to know everything. You just need the relevant facts and enough context to accurately and clearly communicate to your reader.

Your working thesis will not be identical to your final thesis, because you don’t know that much just yet.

This brings us to our next step:

4. Research the question (and working thesis).

What do you need to find out in order to evaluate the strength of your thesis? What do you need to investigate to answer your research question more fully?

Comb through authoritative, trustworthy sources to find that information. And keep detailed notes.

As you research, evaluate the strengths and weaknesses of your thesis — and see what other opposing or more nuanced theses exist.

If you’re writing a persuasive essay, it may be helpful to organize information according to what does or does not support your thesis — or simply gather the information and see if it’s changing your mind. What new opinion do you have now that you’ve learned more about your topic and question? What discoveries have you made that discredit or support your initial thesis?

Raised garden beds prevent full maturity in certain plants — and are more prone to cold, heat, and drought.

If you’re writing an expository essay, use this research process to see if your initial idea holds up to the facts. And be on the lookout for other angles that would be more appropriate or interesting for your assignment.

Modern fencing doesn’t share many rituals with medieval swordplay.

With all this research under your belt, you can answer your research question in-depth — and you’ll have a clearer idea of whether or not your working thesis is anywhere near being accurate or arguable. What’s next?

5. Refine your thesis.

If you found that your working thesis was totally off-base, you’ll probably have to write a new one from scratch.

For a persuasive essay , maybe you found a different opinion far more compelling than your initial take. For an expository essay , maybe your initial assumption was completely wrong — could you flip your thesis around and inform your readers of what you learned?

Use what you’ve learned to rewrite or revise your thesis to be more accurate, specific, and compelling.

Raised garden beds appeal to many gardeners for the semblance of control they offer over what will and will not grow, but they are also more prone to changes in weather and air temperature and may prevent certain plants from reaching full maturity. All of this makes raised beds the worse option for ambitious gardeners.

While swordplay can be traced back through millennia, modern fencing has little in common with medieval combat where swordsmen fought to the death.

If you’ve been researching two separate questions and theses, now’s the time to evaluate which one is most interesting, compelling, or appropriate for your assignment. Did one thesis completely fall apart when faced with the facts? Did one fail to turn up any legitimate sources or studies? Choose the stronger question or the more interesting (revised) thesis, and discard the other.

6. Get help from AI

To make the process even easier, you can take advantage of Wordtune's generative AI capabilities to craft an effective thesis statement. You can take your current thesis statement and try the paraphrase tool to get suggestions for better ways of articulating it. WordTune will generate a set of related phrases, which you can select to help you refine your statement. You can also use Wordtune's suggestions to craft the thesis statement. Write your initial introduction sentence, then click '+' and select the explain suggestion. Browse through the suggestions until you have a statement that captures your idea perfectly.

Thesis Check: Look for These Three Elements

At this point, you should have a thesis that will set up an original, compelling essay, but before you set out to write that essay, make sure your thesis contains these three elements:

Topic: Your thesis should clearly state the topic of your essay, whether swashbuckling pirates, raised garden beds, or methods of snow removal.
Position or angle: Your thesis should zoom into the specific aspect of your topic that your essay will focus on, and briefly but boldly state your position or describe your angle.
Summary of evidence and/or argument: In a concise phrase or two, your thesis should summarize the evidence and/or argument your essay will present, setting up your readers for what’s coming without giving everything away.

The challenge for you is communicating each of these elements in a sentence or two. But remember: Your thesis will come at the end of your intro, which will already have done some work to establish your topic and focus. Those aspects don’t need to be over explained in your thesis — just clearly mentioned and tied to your position and evidence.

Let’s look at our examples from earlier to see how they accomplish this:

Notice how:

The topic is mentioned by name.
The position or angle is clearly stated.
The evidence or argument is set up, as well as the assumptions or opposing view that the essay will debunk.

Both theses prepare the reader for what’s coming in the rest of the essay:

An argument to show that raised beds are actually a poor option for gardeners who want to grow thriving, healthy, resilient plants.
An exposition of modern fencing in comparison with medieval sword fighting that shows how different they are.

Examine your refined thesis. Are all three elements present? If any are missing, make any additions or clarifications needed to correct it.

It’s Essay-Writing Time!

Now that your thesis is ready to go, you have the rest of your essay to think about. With the work you’ve already done to develop your thesis, you should have an idea of what comes next — but if you need help forming your persuasive essay’s argument, we’ve got a blog for that.

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Tzu Chi Med J
v.32(4); Oct-Dec 2020

The impact of artificial intelligence on human society and bioethics

Michael cheng-tek tai.

Department of Medical Sociology and Social Work, College of Medicine, Chung Shan Medical University, Taichung, Taiwan

Artificial intelligence (AI), known by some as the industrial revolution (IR) 4.0, is going to change not only the way we do things, how we relate to others, but also what we know about ourselves. This article will first examine what AI is, discuss its impact on industrial, social, and economic changes on humankind in the 21 st century, and then propose a set of principles for AI bioethics. The IR1.0, the IR of the 18 th century, impelled a huge social change without directly complicating human relationships. Modern AI, however, has a tremendous impact on how we do things and also the ways we relate to one another. Facing this challenge, new principles of AI bioethics must be considered and developed to provide guidelines for the AI technology to observe so that the world will be benefited by the progress of this new intelligence.

W HAT IS ARTIFICIAL INTELLIGENCE ?

Artificial intelligence (AI) has many different definitions; some see it as the created technology that allows computers and machines to function intelligently. Some see it as the machine that replaces human labor to work for men a more effective and speedier result. Others see it as “a system” with the ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation [ 1 ].

Despite the different definitions, the common understanding of AI is that it is associated with machines and computers to help humankind solve problems and facilitate working processes. In short, it is an intelligence designed by humans and demonstrated by machines. The term AI is used to describe these functions of human-made tool that emulates the “cognitive” abilities of the natural intelligence of human minds [ 2 ].

Along with the rapid development of cybernetic technology in recent years, AI has been seen almost in all our life circles, and some of that may no longer be regarded as AI because it is so common in daily life that we are much used to it such as optical character recognition or the Siri (speech interpretation and recognition interface) of information searching equipment on computer [ 3 ].

D IFFERENT TYPES OF ARTIFICIAL INTELLIGENCE

From the functions and abilities provided by AI, we can distinguish two different types. The first is weak AI, also known as narrow AI that is designed to perform a narrow task, such as facial recognition or Internet Siri search or self-driving car. Many currently existing systems that claim to use “AI” are likely operating as a weak AI focusing on a narrowly defined specific function. Although this weak AI seems to be helpful to human living, there are still some think weak AI could be dangerous because weak AI could cause disruptions in the electric grid or may damage nuclear power plants when malfunctioned.

The new development of the long-term goal of many researchers is to create strong AI or artificial general intelligence (AGI) which is the speculative intelligence of a machine that has the capacity to understand or learn any intelligent task human being can, thus assisting human to unravel the confronted problem. While narrow AI may outperform humans such as playing chess or solving equations, but its effect is still weak. AGI, however, could outperform humans at nearly every cognitive task.

Strong AI is a different perception of AI that it can be programmed to actually be a human mind, to be intelligent in whatever it is commanded to attempt, even to have perception, beliefs and other cognitive capacities that are normally only ascribed to humans [ 4 ].

In summary, we can see these different functions of AI [ 5 , 6 ]:

Automation: What makes a system or process to function automatically
Machine learning and vision: The science of getting a computer to act through deep learning to predict and analyze, and to see through a camera, analog-to-digital conversion and digital signal processing
Natural language processing: The processing of human language by a computer program, such as spam detection and converting instantly a language to another to help humans communicate
Robotics: A field of engineering focusing on the design and manufacturing of cyborgs, the so-called machine man. They are used to perform tasks for human's convenience or something too difficult or dangerous for human to perform and can operate without stopping such as in assembly lines
Self-driving car: Use a combination of computer vision, image recognition amid deep learning to build automated control in a vehicle.

D O HUMAN-BEINGS REALLY NEED ARTIFICIAL INTELLIGENCE ?

Is AI really needed in human society? It depends. If human opts for a faster and effective way to complete their work and to work constantly without taking a break, yes, it is. However if humankind is satisfied with a natural way of living without excessive desires to conquer the order of nature, it is not. History tells us that human is always looking for something faster, easier, more effective, and convenient to finish the task they work on; therefore, the pressure for further development motivates humankind to look for a new and better way of doing things. Humankind as the homo-sapiens discovered that tools could facilitate many hardships for daily livings and through tools they invented, human could complete the work better, faster, smarter and more effectively. The invention to create new things becomes the incentive of human progress. We enjoy a much easier and more leisurely life today all because of the contribution of technology. The human society has been using the tools since the beginning of civilization, and human progress depends on it. The human kind living in the 21 st century did not have to work as hard as their forefathers in previous times because they have new machines to work for them. It is all good and should be all right for these AI but a warning came in early 20 th century as the human-technology kept developing that Aldous Huxley warned in his book Brave New World that human might step into a world in which we are creating a monster or a super human with the development of genetic technology.

Besides, up-to-dated AI is breaking into healthcare industry too by assisting doctors to diagnose, finding the sources of diseases, suggesting various ways of treatment performing surgery and also predicting if the illness is life-threatening [ 7 ]. A recent study by surgeons at the Children's National Medical Center in Washington successfully demonstrated surgery with an autonomous robot. The team supervised the robot to perform soft-tissue surgery, stitch together a pig's bowel, and the robot finished the job better than a human surgeon, the team claimed [ 8 , 9 ]. It demonstrates robotically-assisted surgery can overcome the limitations of pre-existing minimally-invasive surgical procedures and to enhance the capacities of surgeons performing open surgery.

Above all, we see the high-profile examples of AI including autonomous vehicles (such as drones and self-driving cars), medical diagnosis, creating art, playing games (such as Chess or Go), search engines (such as Google search), online assistants (such as Siri), image recognition in photographs, spam filtering, predicting flight delays…etc. All these have made human life much easier and convenient that we are so used to them and take them for granted. AI has become indispensable, although it is not absolutely needed without it our world will be in chaos in many ways today.

T HE IMPACT OF ARTIFICIAL INTELLIGENCE ON HUMAN SOCIETY

Negative impact.

Questions have been asked: With the progressive development of AI, human labor will no longer be needed as everything can be done mechanically. Will humans become lazier and eventually degrade to the stage that we return to our primitive form of being? The process of evolution takes eons to develop, so we will not notice the backsliding of humankind. However how about if the AI becomes so powerful that it can program itself to be in charge and disobey the order given by its master, the humankind?

Let us see the negative impact the AI will have on human society [ 10 , 11 ]:

A huge social change that disrupts the way we live in the human community will occur. Humankind has to be industrious to make their living, but with the service of AI, we can just program the machine to do a thing for us without even lifting a tool. Human closeness will be gradually diminishing as AI will replace the need for people to meet face to face for idea exchange. AI will stand in between people as the personal gathering will no longer be needed for communication
Unemployment is the next because many works will be replaced by machinery. Today, many automobile assembly lines have been filled with machineries and robots, forcing traditional workers to lose their jobs. Even in supermarket, the store clerks will not be needed anymore as the digital device can take over human labor
Wealth inequality will be created as the investors of AI will take up the major share of the earnings. The gap between the rich and the poor will be widened. The so-called “M” shape wealth distribution will be more obvious
New issues surface not only in a social sense but also in AI itself as the AI being trained and learned how to operate the given task can eventually take off to the stage that human has no control, thus creating un-anticipated problems and consequences. It refers to AI's capacity after being loaded with all needed algorithm may automatically function on its own course ignoring the command given by the human controller
The human masters who create AI may invent something that is racial bias or egocentrically oriented to harm certain people or things. For instance, the United Nations has voted to limit the spread of nucleus power in fear of its indiscriminative use to destroying humankind or targeting on certain races or region to achieve the goal of domination. AI is possible to target certain race or some programmed objects to accomplish the command of destruction by the programmers, thus creating world disaster.

P OSITIVE IMPACT

There are, however, many positive impacts on humans as well, especially in the field of healthcare. AI gives computers the capacity to learn, reason, and apply logic. Scientists, medical researchers, clinicians, mathematicians, and engineers, when working together, can design an AI that is aimed at medical diagnosis and treatments, thus offering reliable and safe systems of health-care delivery. As health professors and medical researchers endeavor to find new and efficient ways of treating diseases, not only the digital computer can assist in analyzing, robotic systems can also be created to do some delicate medical procedures with precision. Here, we see the contribution of AI to health care [ 7 , 11 ]:

Fast and accurate diagnostics

IBM's Watson computer has been used to diagnose with the fascinating result. Loading the data to the computer will instantly get AI's diagnosis. AI can also provide various ways of treatment for physicians to consider. The procedure is something like this: To load the digital results of physical examination to the computer that will consider all possibilities and automatically diagnose whether or not the patient suffers from some deficiencies and illness and even suggest various kinds of available treatment.

Socially therapeutic robots

Pets are recommended to senior citizens to ease their tension and reduce blood pressure, anxiety, loneliness, and increase social interaction. Now cyborgs have been suggested to accompany those lonely old folks, even to help do some house chores. Therapeutic robots and the socially assistive robot technology help improve the quality of life for seniors and physically challenged [ 12 ].

Reduce errors related to human fatigue

Human error at workforce is inevitable and often costly, the greater the level of fatigue, the higher the risk of errors occurring. Al technology, however, does not suffer from fatigue or emotional distraction. It saves errors and can accomplish the duty faster and more accurately.

Artificial intelligence-based surgical contribution

AI-based surgical procedures have been available for people to choose. Although this AI still needs to be operated by the health professionals, it can complete the work with less damage to the body. The da Vinci surgical system, a robotic technology allowing surgeons to perform minimally invasive procedures, is available in most of the hospitals now. These systems enable a degree of precision and accuracy far greater than the procedures done manually. The less invasive the surgery, the less trauma it will occur and less blood loss, less anxiety of the patients.

Improved radiology

The first computed tomography scanners were introduced in 1971. The first magnetic resonance imaging (MRI) scan of the human body took place in 1977. By the early 2000s, cardiac MRI, body MRI, and fetal imaging, became routine. The search continues for new algorithms to detect specific diseases as well as to analyze the results of scans [ 9 ]. All those are the contribution of the technology of AI.

Virtual presence

The virtual presence technology can enable a distant diagnosis of the diseases. The patient does not have to leave his/her bed but using a remote presence robot, doctors can check the patients without actually being there. Health professionals can move around and interact almost as effectively as if they were present. This allows specialists to assist patients who are unable to travel.

S OME CAUTIONS TO BE REMINDED

Despite all the positive promises that AI provides, human experts, however, are still essential and necessary to design, program, and operate the AI from any unpredictable error from occurring. Beth Kindig, a San Francisco-based technology analyst with more than a decade of experience in analyzing private and public technology companies, published a free newsletter indicating that although AI has a potential promise for better medical diagnosis, human experts are still needed to avoid the misclassification of unknown diseases because AI is not omnipotent to solve all problems for human kinds. There are times when AI meets an impasse, and to carry on its mission, it may just proceed indiscriminately, ending in creating more problems. Thus vigilant watch of AI's function cannot be neglected. This reminder is known as physician-in-the-loop [ 13 ].

The question of an ethical AI consequently was brought up by Elizabeth Gibney in her article published in Nature to caution any bias and possible societal harm [ 14 ]. The Neural Information processing Systems (NeurIPS) conference in Vancouver Canada in 2020 brought up the ethical controversies of the application of AI technology, such as in predictive policing or facial recognition, that due to bias algorithms can result in hurting the vulnerable population [ 14 ]. For instance, the NeurIPS can be programmed to target certain race or decree as the probable suspect of crime or trouble makers.

T HE CHALLENGE OF ARTIFICIAL INTELLIGENCE TO BIOETHICS

Artificial intelligence ethics must be developed.

Bioethics is a discipline that focuses on the relationship among living beings. Bioethics accentuates the good and the right in biospheres and can be categorized into at least three areas, the bioethics in health settings that is the relationship between physicians and patients, the bioethics in social settings that is the relationship among humankind and the bioethics in environmental settings that is the relationship between man and nature including animal ethics, land ethics, ecological ethics…etc. All these are concerned about relationships within and among natural existences.

As AI arises, human has a new challenge in terms of establishing a relationship toward something that is not natural in its own right. Bioethics normally discusses the relationship within natural existences, either humankind or his environment, that are parts of natural phenomena. But now men have to deal with something that is human-made, artificial and unnatural, namely AI. Human has created many things yet never has human had to think of how to ethically relate to his own creation. AI by itself is without feeling or personality. AI engineers have realized the importance of giving the AI ability to discern so that it will avoid any deviated activities causing unintended harm. From this perspective, we understand that AI can have a negative impact on humans and society; thus, a bioethics of AI becomes important to make sure that AI will not take off on its own by deviating from its originally designated purpose.

Stephen Hawking warned early in 2014 that the development of full AI could spell the end of the human race. He said that once humans develop AI, it may take off on its own and redesign itself at an ever-increasing rate [ 15 ]. Humans, who are limited by slow biological evolution, could not compete and would be superseded. In his book Superintelligence, Nick Bostrom gives an argument that AI will pose a threat to humankind. He argues that sufficiently intelligent AI can exhibit convergent behavior such as acquiring resources or protecting itself from being shut down, and it might harm humanity [ 16 ].

The question is–do we have to think of bioethics for the human's own created product that bears no bio-vitality? Can a machine have a mind, consciousness, and mental state in exactly the same sense that human beings do? Can a machine be sentient and thus deserve certain rights? Can a machine intentionally cause harm? Regulations must be contemplated as a bioethical mandate for AI production.

Studies have shown that AI can reflect the very prejudices humans have tried to overcome. As AI becomes “truly ubiquitous,” it has a tremendous potential to positively impact all manner of life, from industry to employment to health care and even security. Addressing the risks associated with the technology, Janosch Delcker, Politico Europe's AI correspondent, said: “I don't think AI will ever be free of bias, at least not as long as we stick to machine learning as we know it today,”…. “What's crucially important, I believe, is to recognize that those biases exist and that policymakers try to mitigate them” [ 17 ]. The High-Level Expert Group on AI of the European Union presented Ethics Guidelines for Trustworthy AI in 2019 that suggested AI systems must be accountable, explainable, and unbiased. Three emphases are given:

Lawful-respecting all applicable laws and regulations
Ethical-respecting ethical principles and values
Robust-being adaptive, reliable, fair, and trustworthy from a technical perspective while taking into account its social environment [ 18 ].

Seven requirements are recommended [ 18 ]:

AI should not trample on human autonomy. People should not be manipulated or coerced by AI systems, and humans should be able to intervene or oversee every decision that the software makes
AI should be secure and accurate. It should not be easily compromised by external attacks, and it should be reasonably reliable
Personal data collected by AI systems should be secure and private. It should not be accessible to just anyone, and it should not be easily stolen
Data and algorithms used to create an AI system should be accessible, and the decisions made by the software should be “understood and traced by human beings.” In other words, operators should be able to explain the decisions their AI systems make
Services provided by AI should be available to all, regardless of age, gender, race, or other characteristics. Similarly, systems should not be biased along these lines
AI systems should be sustainable (i.e., they should be ecologically responsible) and “enhance positive social change”
AI systems should be auditable and covered by existing protections for corporate whistleblowers. The negative impacts of systems should be acknowledged and reported in advance.

From these guidelines, we can suggest that future AI must be equipped with human sensibility or “AI humanities.” To accomplish this, AI researchers, manufacturers, and all industries must bear in mind that technology is to serve not to manipulate humans and his society. Bostrom and Judkowsky listed responsibility, transparency, auditability, incorruptibility, and predictability [ 19 ] as criteria for the computerized society to think about.

S UGGESTED PRINCIPLES FOR ARTIFICIAL INTELLIGENCE BIOETHICS

Nathan Strout, a reporter at Space and Intelligence System at Easter University, USA, reported just recently that the intelligence community is developing its own AI ethics. The Pentagon made announced in February 2020 that it is in the process of adopting principles for using AI as the guidelines for the department to follow while developing new AI tools and AI-enabled technologies. Ben Huebner, chief of the Office of Director of National Intelligence's Civil Liberties, Privacy, and Transparency Office, said that “We're going to need to ensure that we have transparency and accountability in these structures as we use them. They have to be secure and resilient” [ 20 ]. Two themes have been suggested for the AI community to think more about: Explainability and interpretability. Explainability is the concept of understanding how the analytic works, while interpretability is being able to understand a particular result produced by an analytic [ 20 ].

All the principles suggested by scholars for AI bioethics are well-brought-up. I gather from different bioethical principles in all the related fields of bioethics to suggest four principles here for consideration to guide the future development of the AI technology. We however must bear in mind that the main attention should still be placed on human because AI after all has been designed and manufactured by human. AI proceeds to its work according to its algorithm. AI itself cannot empathize nor have the ability to discern good from evil and may commit mistakes in processes. All the ethical quality of AI depends on the human designers; therefore, it is an AI bioethics and at the same time, a trans-bioethics that abridge human and material worlds. Here are the principles:

Beneficence: Beneficence means doing good, and here it refers to the purpose and functions of AI should benefit the whole human life, society and universe. Any AI that will perform any destructive work on bio-universe, including all life forms, must be avoided and forbidden. The AI scientists must understand that reason of developing this technology has no other purpose but to benefit human society as a whole not for any individual personal gain. It should be altruistic, not egocentric in nature
Value-upholding: This refers to AI's congruence to social values, in other words, universal values that govern the order of the natural world must be observed. AI cannot elevate to the height above social and moral norms and must be bias-free. The scientific and technological developments must be for the enhancement of human well-being that is the chief value AI must hold dearly as it progresses further
Lucidity: AI must be transparent without hiding any secret agenda. It has to be easily comprehensible, detectable, incorruptible, and perceivable. AI technology should be made available for public auditing, testing and review, and subject to accountability standards … In high-stakes settings like diagnosing cancer from radiologic images, an algorithm that can't “explain its work” may pose an unacceptable risk. Thus, explainability and interpretability are absolutely required
Accountability: AI designers and developers must bear in mind they carry a heavy responsibility on their shoulders of the outcome and impact of AI on whole human society and the universe. They must be accountable for whatever they manufacture and create.

C ONCLUSION

AI is here to stay in our world and we must try to enforce the AI bioethics of beneficence, value upholding, lucidity and accountability. Since AI is without a soul as it is, its bioethics must be transcendental to bridge the shortcoming of AI's inability to empathize. AI is a reality of the world. We must take note of what Joseph Weizenbaum, a pioneer of AI, said that we must not let computers make important decisions for us because AI as a machine will never possess human qualities such as compassion and wisdom to morally discern and judge [ 10 ]. Bioethics is not a matter of calculation but a process of conscientization. Although AI designers can up-load all information, data, and programmed to AI to function as a human being, it is still a machine and a tool. AI will always remain as AI without having authentic human feelings and the capacity to commiserate. Therefore, AI technology must be progressed with extreme caution. As Von der Leyen said in White Paper on AI – A European approach to excellence and trust : “AI must serve people, and therefore, AI must always comply with people's rights…. High-risk AI. That potentially interferes with people's rights has to be tested and certified before it reaches our single market” [ 21 ].

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Knowledge Base

Using AI tools

AI Writing Tools | Definition, Uses & Implications

AI writing tools are artificial intelligence (AI) software applications like ChatGPT that help to automate or assist the writing process. These tools use machine learning algorithms to generate human-sounding text in response to users’ text-based prompts.

Other AI tools, such as grammar checkers , paraphrasers and summarizers serve more specific functions, like identifying grammar and spelling mistakes or rephrasing text.

How do ai writing tools work, what can ai writing tools be used for, implications of ai writing tools, other interesting articles, frequently asked questions about ai writing tools.

AI writing tools (chatbots, grammar checkers, etc.) use natural language processing (NLP) algorithms, machine learning , and large language models (LLMs) to generate or improve written text. These tools are trained to identify patterns in vast amounts of data. The tools then use these patterns to analyze human inputs (or prompts) and generate natural-sounding outputs (similar to how predictive text works).

Different AI tools may be programmed in different ways. For example, ChatGPT, the popular chatbot developed by OpenAI and released in November 2022, uses generative pre-trained transformers (GPTs) and was trained using reinforcement learning from human feedback (RLHF) , meaning that the tool was given feedback from humans to help guide it towards the most helpful responses.

AI writing tools have a number of helpful applications. Some of these applications are discussed below.

Interactive learning and feedback

Generative AI tools can be helpful resources for conversational learning. By inputting questions, users can engage in interactive learning at any time.

You can also prompt these tools to provide feedback on your work to improve the clarity or consistency of your writing. However, it’s important not to upload sensitive information, as user inputs are generally saved by the tool for the purposes of training future models.

Below is an example of how ChatGPT can be used for interactive learning.

Example: Using ChatGPT for interactive learning

Tell me three facts about Carthaginian culture
Carthaginians were known for their maritime trade and commerce, and their navy was one of the strongest in the ancient Mediterranean world. They established colonies and trading posts throughout the Mediterranean, including in Sicily, Sardinia, and North Africa.
Carthaginians had a polytheistic religion, and their chief deity was Baal Hammon, the god of fertility, agriculture, and the sky. They also worshiped a number of other gods and goddesses, including Tanit, the goddess of love, fertility, and war.
Carthaginians were skilled metalworkers and produced a variety of metal goods, including weapons, armor, jewelry, and household objects. They were particularly famous for their bronze work, which was highly prized in the ancient world. Carthaginian bronze was so valued that it was used by the Romans to make their famous statues and other works of art.
Tell me three more facts about the Carthaginian navy
The Carthaginian navy was one of the most advanced and powerful navies of the ancient world. It was instrumental in establishing Carthage as a dominant power in the Mediterranean region.
The Carthaginians were known for their skill in shipbuilding and developed a variety of ship types for different purposes, including warships, transport ships, and cargo ships. One of their most famous ship types was the quinquereme, a large warship with five rows of oars.
The Carthaginian navy was led by a commander called the “admiral.” The admiral was responsible for overseeing the fleet and making strategic decisions about naval warfare. One of the most famous Carthaginian admirals was Hannibal Barca, who led the Carthaginian fleet during the First Punic War against Rome.

Generating research questions and outlines

AI writing tools can be used to generate potential research questions or research paper outlines . You can then refine the tool’s responses by further prompting it or directly editing the output yourself to ensure the result makes sense and fits your requirements.

Below is an example of how ChatGPT can be used to generate potential reseach questions and research method ideas.

Example: Using ChatGPT to generate research questions and methods

Develop three possible research questions on the following topic: “The influence of technology on second language acquisition.”
What is the impact of technology-assisted language learning (TALL) on the acquisition of a second language?
How do different types of technology, such as mobile applications, virtual reality, and online platforms, affect second language acquisition outcomes?
How do individual learner characteristics, such as age, proficiency level, and motivation, interact with the use of technology in second language acquisition?
Suggest three quantitative research methods appropriate to the second question.
Experimental research: This research method involves manipulating an independent variable (in this case, the type of technology used) to observe its effect on a dependent variable (second language acquisition outcomes). Participants would be randomly assigned to different groups using different types of technology to learn a second language, and then their language acquisition outcomes would be measured and compared.
Survey research: This research method involves using questionnaires to gather data from a large group of participants. In this case, a survey could be designed to gather information on participants’ use of different types of technology to learn a second language, as well as their language acquisition outcomes. This would allow for a large-scale investigation of how different types of technology are being used, and what their impact might be.
Correlational research: This research method involves examining the relationship between two or more variables. In this case, a correlational study could be conducted to investigate whether there is a relationship between the type of technology used and language acquisition outcomes. Data could be collected from participants using different types of technology to learn a second language, and then the correlation between the type of technology and language acquisition outcomes could be calculated.

Paraphrasing text

AI tools like ChatGPT and Scribbr’s free paraphrasing tool can help you paraphrase text to express your ideas more clearly, avoid repetition, and maintain a consistent tone throughout your writing.

They can also help you incorporate scholarly sources in your writing in a more concise and fluent way, without the need for direct quotations. However, it’s important to correctly cite all sources to avoid accidental plagiarism.

Summarizing text

AI writing tools can help condense a text to its most important and relevant ideas. This can help you understand complex information more easily. You can also use summarizer tools on your own work to summarize your central argument, clarify your research question, and form conclusions.

You can do this using generative AI tools or more specialized tools like Scribbr’s free text-summarizer .

Proofreading text

AI writing tools can be used to identify spelling, grammar, and punctuation mistakes and suggest corrections. These tools can help to improve the clarity of your writing and avoid common mistakes .

While AI tools like ChatGPT offer useful suggestions, they can also potentially miss some mistakes or even introduce new grammatical errors into your writing.

We advise using Scribbr’s proofreading and editing service or a tool like Scribbr’s free grammar checker , which is designed specifically for this purpose.

Translating text

AI translation tools like Google Translate can be used to translate text from a source language into various target languages. While the quality of these tools tend to vary depending on the languages used, they’re constantly developing and are increasingly accurate.

While there are many benefits to using AI writing tools, some commentators have emphasized the limitations of AI tools and the potential disadvantages of using them. These drawbacks are discussed below.

Impact on learning

One of the potential pitfalls of using AI writing tools is the effect they might have on a student’s learning and skill set. Using AI tools to generate a paper, thesis , or dissertation , for example, may impact a student’s research, critical thinking, and writing skills.

However, other commentators argue that AI tools can be used to promote critical thinking (e.g., by having a student evaluate a tool’s output and refine it).

Consistency and accuracy

Generative AI tools (such as ChatGPT) are not always trustworthy and sometimes produce results that are inaccurate or factually incorrect. Although these tools are programmed to answer questions, they can’t judge the accuracy of the information they provide and may generate incorrect answers or contradict themselves.

It’s important to verify AI-generated information against a credible source .

Grammatical mistakes

While generative AI tools can produce written text, they don’t actually understand what they’re saying and sometimes produce grammar, spelling, and punctuation mistakes.

You can combine the use of generative AI tools with Scribbr’s grammar checker , which is designed to catch these mistakes.

Ethics and plagiarism

As AI writing tools are trained on large sets of data, they may produce content that is similar to existing content (which they usually cannot cite correctly), which can be considered plagiarism.

Furthermore, passing off AI-generated text as your own work is usually considered a form of plagiarism and is likely to be prohibited by your university. This offense may be recognized by your university’s plagiarism checker or AI detector .

If you want more tips on using AI tools , understanding plagiarism , and citing sources , make sure to check out some of our other articles with explanations, examples, and formats.

Citing ChatGPT
Best grammar checker
Best paraphrasing tool
ChatGPT in your studies
Is ChatGPT trustworthy?
Types of plagiarism
Self-plagiarism
Avoiding plagiarism
Academic integrity
Best plagiarism checker

Citing sources

Citation styles
In-text citation
Citation examples
Annotated bibliography

AI writing tools can be used to perform a variety of tasks.

Generative AI writing tools (like ChatGPT ) generate text based on human inputs and can be used for interactive learning, to provide feedback, or to generate research questions or outlines.

These tools can also be used to paraphrase or summarize text or to identify grammar and punctuation mistakes. Y ou can also use Scribbr’s free paraphrasing tool , summarizing tool , and grammar checker , which are designed specifically for these purposes.

Using AI writing tools (like ChatGPT ) to write your essay is usually considered plagiarism and may result in penalization, unless it is allowed by your university . Text generated by AI tools is based on existing texts and therefore cannot provide unique insights. Furthermore, these outputs sometimes contain factual inaccuracies or grammar mistakes.

However, AI writing tools can be used effectively as a source of feedback and inspiration for your writing (e.g., to generate research questions ). Other AI tools, like grammar checkers, can help identify and eliminate grammar and punctuation mistakes to enhance your writing.

You can access ChatGPT by signing up for a free account:

Follow this link to the ChatGPT website.
Click on “Sign up” and fill in the necessary details (or use your Google account). It’s free to sign up and use the tool.
Type a prompt into the chat box to get started!

A ChatGPT app is also available for iOS, and an Android app is planned for the future. The app works similarly to the website, and you log in with the same account for both.

Yes, ChatGPT is currently available for free. You have to sign up for a free account to use the tool, and you should be aware that your data may be collected to train future versions of the model.

To sign up and use the tool for free, go to this page and click “Sign up.” You can do so with your email or with a Google account.

A premium version of the tool called ChatGPT Plus is available as a monthly subscription. It currently costs $20 and gets you access to features like GPT-4 (a more advanced version of the language model). But it’s optional: you can use the tool completely free if you’re not interested in the extra features.

ChatGPT was publicly released on November 30, 2022. At the time of its release, it was described as a “research preview,” but it is still available now, and no plans have been announced so far to take it offline or charge for access.

ChatGPT continues to receive updates adding more features and fixing bugs. The most recent update at the time of writing was on May 24, 2023.

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Thesis: A strategic perspective on the commercialization of artificial intelligence

Submitted by Siddhartha Ray Barua.

Abstract: Many companies are increasing their focus on Artificial Intelligence as they incorporate Machine Learning and Cognitive technologies into their current offerings. Industries ranging from healthcare, pharmaceuticals, finance, automotive, retail, manufacturing and so many others are all trying to deploy and scale enterprise Al systems while reducing their risk. Companies regularly struggle with finding appropriate and applicable use cases around Artificial Intelligence and Machine Learning projects. The field of Artificial Intelligence has a rich set of literature for modeling of technical systems that implement Machine Learning and Deep Learning methods. This thesis attempts to connect the literature for business and technology and for evolution and adoption of technology to the emergent properties of Artificial Intelligence systems. The aim of this research is to identify high and low value market segments and use cases within the industries, prognosticate the evolution of different Al technologies and begin to outline the implications of commercialization of such technologies for various stakeholders. This thesis also provides a framework to better prepare business owners to commercialize Artificial Intelligence technologies to satisfy their strategic goals.

To read the complete article, visit DSpace at the MIT Libraries .

Policy Content
Policy Status

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Guidelines on student use of generative artificial intelligence.

TABLE OF CONTENTS

These guidelines supplement the VinUniversity Academic Integrity Policy but do not supersede it. They give more specificity on the principles and regulations of the ethical and responsible use of generative Artificial Intelligence (GenAI) in academic work at VinUniversity. These guidelines aim to provide clear direction on how students can leverage these tools while maintaining academic integrity.

These guidelines apply to all VinUniversity students including undergraduate and graduate students. Faculty members are expected to incorporate these guidelines into their course design and to clearly communicate the specific GenAI expectations to students in their syllabus, assignment instructions, and discussions with students throughout the course.

3. Guiding Principles

GenAI has the potential to enhance learning, creativity, and productivity. VinUniversity believes that GenAI should support, not undermine, learning outcomes and students should always respect academic integrity. VinUniversity supports these principles for the ethical and responsible use of GenAI:

Alignment with Learning Objectives : While VinUniversity embraces the potential of GenAI, it is committed to developing students’ holistic academic skills such as critical thinking, original thought, and creative expression. GenAI should enhance learning, not replace it. The use of GenAI tools must align with, and support, the learning outcomes of each course, when appropriate.
Transparency : The extent to which GenAI is permitted in any assessment or learning activity is at the discretion of the course faculty. Faculty should clearly communicate their expectations and limits of GenAI use in major assessments through the AI Assessment Scale (AIAS) explained later in these guidelines, providing explicit guidelines on acceptable GenAI use.
Honest Disclosure : When GenAI use is permitted, students must be transparent about their use of these tools and clearly acknowledge how GenAI was used. Submitting GenAI content as one’s own work without proper acknowledgment is a violation of academic integrity.
Critical Engagement : Students should use GenAI tools with a critical lens to evaluate the quality, relevance, accuracy, and bias of AI-generated content. Students should use GenAI to enhance critical thinking and problem-solving skills rather than replace them.
Faculty, Program, and Discipline Variations : Faculty, programs, and disciplines each have their own levels of comfort, familiarity, and use cases of GenAI. Tolerances of GenAI will vary, so students should proactively consult with their faculty on the appropriate use of GenAI in their courses. Faculty and students are encouraged to explore and share ethical and effective ways of using GenAI in academic work to create a culture of experimentation and openness.
Continuous Refinement : GenAI technology is rapidly evolving, so these guidelines and the broader Academic Integrity Policy will be reviewed periodically and updated to ensure that they remain relevant and effective.

4. Institutional Statement on the Ethical and Responsible Use of GenAI

VinUniversity supports the ethical and responsible integration of generative AI and other innovations in teaching and learning. We encourage VinUniversity students, faculty, teaching assistants, and staff to purposefully utilize generative AI through a critical lens to spark creativity and deepen understanding. However, submitting largely AI-generated work as one’s original work without formal acknowledgement fundamentally violates the principles of academic integrity. Generative AI has the potential to supplement and assist the learning process but must not replace the invaluable role of original human thought, analysis, and expression which are the core tenets of all academic endeavors. As an institution, VinUniversity is committed to upholding the highest ethical standards of scholarly work while leveraging the potential of the purposeful, discerning, and responsible application of generative AI to enhance learning. Each faculty may have their own specific thresholds of acceptable generative AI use, so students should carefully review and adhere to the policies set by their instructors for a more nuanced understanding in each course. When in doubt, students should discuss with their instructor for clarity and confirmation.

5. Definitions

In addition to the definitions of the Academic Integrity Policy, some specific terms in the context of GenAI are defined here:

AI detector tools: Software applications designed to analyze text and identify content that may have been generated by artificial intelligence systems, such as large language models.

Acknowledgment: The practice of openly disclosing the use of GenAI tools or AI-generated content in academic work. This typically involves clearly indicating whether any AI assistance was used and, if so, which tool(s) and how they were used.

Generative Artificial Intelligence (GenAI) : Artificial intelligence systems which can produce various types of content including text, code, images, audio, and video. Examples include (but are not limited to) large language models (LLMs) like ChatGPT, Claude, Gemini, or Llama; image or video generators such as DALL-E, Flux, Midjourney, or Firefly; code producing tools like GitHub Copilot, CodeWhisperer, or Cursor; and other AI-powered tools that can create or manipulate content.

Prompt: A natural language text input which instructs an AI model to perform a task and generate an output. It is a question, command, or set of parameters for the AI model to generate relevant content or perform a particular task.

6. Permitted Use

Students are expected to adhere to the guidelines set by the faculty of each course. The use of GenAI in violation of course or assignment policy is considered a breach of academic integrity.

If there is any doubt about whether a specific use of GenAI is permitted or appropriate for an assignment or a course, students are responsible for discussing with the course instructor prior to using the tool for any academic work.

6.1. AI Assessment Scale

As there are a wide range of courses and instructors at VinUniversity with differing perspectives of the integration of GenAI in education, faculty may have course-specific policies that differ. To provide more specificity of the permitted use of GenAI tools at the course level, clear instructions for major assessments will be provided to students regarding acceptable use of GenAI tools. This will be communicated through an AI Assessment Scale (AIAS) which explicitly declares the thresholds of acceptable GenAI use for each major assignment and is clearly communicated to students. The AIAS empowers faculty to select the appropriate level of GenAI usage for their own contexts, based on their intended learning outcomes. The AIAS offers clarity and transparency for students and facilitates a nuanced approach which embraces the opportunities of GenAI while recognizing that there are instances where such tools may not be pedagogically appropriate or necessary.

6.2. Acknowledgement of Generative AI Use

When permitted, any use of GenAI must be formally acknowledged in submitted work. The specific requirements for this acknowledgment will be outlined in each course syllabus or assignment instructions. Failure to properly disclose AI use or submitting AI-generated content as one’s own original work constitutes academic dishonesty and will be treated as a violation under the VinUniversity Academic Integrity Policy.

Acknowledgement of GenAI use typically includes a formal acknowledgment in the assignment submission stating which tool(s) were used, the specific prompts input into the tool(s), and how the AI-generated output was integrated into students’ work. The exact requirements and format of this acknowledgement will be determined by the course instructor.

Students may also be required to sign an honor code on every major assignment submission. This helps ensure that students are consistently aware of and accountable for GenAI usage in their academic work.

An example acknowledgement and honor code are shown in Appendix B. Faculty are welcome to adapt, as needed.

6.3. Responsibility for Fact Checking

Students are responsible for ensuring the accuracy of any content in their academic work, whether human- or AI-generated. Being mindful of the tendency of GenAI to hallucinate (where the AI model presents false or misleading information as fact), students should fact check and cross-reference with credible sources to critically assess GenAI output. Students are responsible for the final product that they submit and should be prepared to justify and explain any aspects of their assignments to demonstrate a thorough understanding of the work.

7. Detection Tools and Evidencing Originality

Faculty or teaching assistants may utilize AI detector tools at their discretion. Students agree to the use of these tools under the existing privacy policy of VinUniversity and the tool providers. In some cases, students may be asked to submit additional evidence of their work process, such as drafts, research notes, or other evidence. When AI use is suspected, students may be asked to explain their work process or recreate parts of their submission under supervised conditions. These measures are designed to ensure the authenticity of student work while allowing for the responsible use of AI tools.

8. Managing Suspected Violations

Any incidents related to the suspected misuse of GenAI in academic work will be handled according to the procedures outlined in the VinUniversity Academic Integrity Policy. Reporting procedures follow the existing VinUniversity Academic Integrity Policy. Repeated violations may result in escalation to a higher tier, according to the VinUniversity Academic Integrity Policy.

Refer to Appendix A for the full list of tiers, example violations, possible outcomes, and actions to take. The examples provided are not exhaustive and in case of doubt, faculty should consult with Office of the Registrar for guidance.

9. Intellectual Property

Using GenAI has intellectual property (IP) implications. Uploading any content to these tools could result in that content becoming part of the training model, which may accidentally be visible to others and violate the rights of the IP owner.

Students are responsible for ensuring they have written permission from the instructor to upload any IP from the course, such as lecture slides, readings, recordings, supplementary materials, or assessment information. Making audio or video recordings of lectures and uploading them to GenAI meeting summarizing tools is strictly prohibited unless students have direct consent from the instructor.

10. Appendices

10.1. appendix a: tiers of violations and sanctions.

Tier 1: Minor Violations – Verbal warning and remedial action

Description: Minor misuse of GenAI due to misunderstanding or lack of awareness. A Tier 1 violation occurs when a student’s misuse of GenAI (1) appears unintentional, or (2) can be reasonably attributed to a lack of understanding of instructor expectations or academic conventions, or (3) or considered minor or developmental in nature.

Partially, but not completely, acknowledging AI-generated content.
Using GenAI in an unauthorized way in some parts of a minor assignment, not having a serious impact on the learning outcomes.
Unintentionally exceeding the permitted level of AI use due to a legitimate misunderstanding of assignment instructions.

Potential Outcomes:

Verbal warning
Educational intervention on ethical and responsible GenAI use (i.e. training course, reading and follow-up task, etc.)
Re-read the VinUniversity Academic Integrity policy / Guidelines on Student use of Generative Artificial Intelligence and undertake a reflective task
Redo the specific assignment or activity, potentially in a controlled environment

Action to take: For Tier 1 violations, the faculty should:

Schedule a face-to-face or online meeting with the student.
Explain the suspected misuse of GenAI and why it is considered a violation.
Listen to the student’s perspective and explanation of how GenAI was used.
Educate the student on appropriate GenAI use / lack of use and the importance of transparency.
If use is inappropriate, issue a verbal warning and explain how to avoid similar mistakes in the future.
Assign any follow-up tasks, as relevant.
Document the conversation for internal records.

Example starting conversation: “It looks like you used AI for brainstorming in our recent assignment without disclosing it. Can you tell me more about how you used AI in this assignment? I’d like to explain why this is a concern and how we can ensure more ethical AI use in the future.”

Reporting: Tier 1 cases are recorded by the instructor but not reported to the Office of the Registrar unless multiple Tier 1 violations occur within the same course and is escalated to a Tier 2 violation.

Tier 2: Moderate Violations – Instructor-imposed Sanctions

Description: Intentional misuse of GenAI tools that compromises the assessment’s integrity. A Tier 2 violation occurs when (1) a student’s dishonest use of GenAI is intentional, significant, and the student can reasonably be expected to know that the act violated the Guidelines on Student use of Generative Artificial Intelligence / Academic Integrity Policy, or (2) if the student has committed multiple Tier 1 violations in the course and the instructor decides that the repeated incidents constitute a Tier 2 violation.

Submitting mostly or entirely AI-generated content as one’s own work without proper acknowledgment.
Using AI for drafting in a Level 2 (“AI for Planning”) assignment where only ideation and research are permitted.
Failing to critically evaluate and modify AI-generated content in a Level 3 (“AI for Collaboration”) assignment.
Failing to acknowledge GenAI use and not providing any acceptable evidence of appropriate AI usage when required by the instructor.

Possible Outcomes:

Reduced grade in the assessment.
Failing grade in the assessment.
Additional projects/assignments to be completed to get credit.
Written action plan or learning plan required by instructor

Action to take:

For Tier 2 violations, the faculty should:

Schedule a formal face-to-face or online meeting with the student.
Clearly outline the suspected GenAI misuse and its implications.
Allow the student to explain their actions and provide any relevant context or evidence.
Discuss the importance of academic integrity and ethical GenAI use.
If inappropriate use, explain the potential consequences and the need for corrective action.
If inappropriate use, decide on appropriate sanctions (e.g., grade reduction, resubmission).
Document the meeting and outcomes and submit to the Registrar using the required e-form.

Example starting conversation: “In your recent assignment, I stated that this is a Level 2 assignment (“AI for Planning”), but it appears that you used AI for drafting the assignment. I’d like to discuss this with you, understand your perspective, give you a chance to provide any evidence, and then explain why this is a concern. We’ll also need to talk about the consequences and how to prevent this in the future.”

Tier 2 cases must be reported to the Registrar using this e-form .
Tier 2 cases are recorded on students’ VinUniversity Student Record on the Student Information System.

Tier 3: Serious Violations – Program-imposed Sanctions or Suspension

Description: Deliberate and extensive misuse of GenAI tools that undermines the purpose of a major assessment task in such a way that it causes gross inequality to students who completed the task honestly.

Extensive unauthorized use of GenAI in a significant assessment such as a capstone, thesis, or major project.
Extensively using GenAI in a Level 3 assignment without any evidence of critical evaluation or modification.
Intentionally and significantly misrepresenting the extent of GenAI use in the assignment acknowledgment form with the attempt to hide or mask usage.
Using GenAI without authorization in a Level 1 (“No AI”) assignment.
Failing grade in the course
Immediate suspension from the course
The Program Director and instructor may require other remedial actions before the student can enroll in further courses at VinUniversity

Actions to take: For Tier 3 violations, the faculty should follow the process outlined in the VinUniversity Academic Integrity Policy, ensuring that the Program Director is consulted and involved in the process.

Tier 4: Severe Violations which Warrant Suspension or Dismissal

Description: Repeated or egregious misuse of GenAI tools or misusing GenAI which causes significant harm to VinUniversity members or the reputation of VinUniversity. A Tier 4 violation is when the student’s act of academic dishonesty is so severe or egregious that it warrants a suspension, dismissal or other disciplinary actions of the highest order. All Tier 4 violations are referred to and decided by the Academic Integrity Council.

Multiple instances of submitting AI-generated work as one’s own across different courses in major assessments.
Using AI to impersonate another student or VinUniversity member (such as ‘deepfakes’ or voice cloning).
Hacking or manipulating AI tools to bypass university detection methods or to generate restricted content.
Dismissal from VinUniversity
Reduction or revoking of financial aid or scholarship
Ban from certain activities or access to certain resources or facilities
Rescinding admission into VinUniversity, a department, program, or internship
Withdrawing or revoking a credential issued by VinUniversity
Other sanction(s) as deemed appropriate by the Council

Actions to Take: For Tier 4 violations, the faculty should follow the process outlined in the VinUniversity Academic Integrity Policy, ensuring that the Program Director, Dean, and other relevant leaders are consulted and involved in the process.

For detailed information on reporting procedures, sanctioning processes, and appeals, please refer to the overarching VinUniversity Academic Integrity Policy .

10.2. Appendix B: Sample Acknowledgement Statement and Honor Code

I, [full name], affirm that:

I fully understand and have adhered to the course policy on generative AI use for this specific assignment.
I have not used generative AI in any manner that violates the stated policy for this assignment.
I have accurately and completely disclosed all instances of generative AI use in the accompanying acknowledgment table.
I take full responsibility for the integrity and originality of the work I am submitting.

I understand that any violation of this honor code may result in disciplinary action as outlined in the VinUniversity Academic Integrity Policy.

Signature: [type your full name here] Date: [type the date of submission here]

Acknowledgement Table

▢ I have not used any AI tools in the creation or revision of this submission.

▢ I have used AI tools responsibly in this submission, in accordance with the course/assignment guidelines, AI Assessment Scale, and VinUniversity Policy on Student use of Generative Artificial Intelligence. I have summarized how I used them below (add/delete rows, if needed) . I take full responsibility for the final content of this submission.

(if required)
Google Gemini	In the ideation phase of the assignment to suggest some possible topics for my paper.	“Generate a list of potential topics for an undergraduate research paper on sustainable business practices in the fashion industry. Ask me any questions before you begin.” I followed up with two additional prompts: 1. I answered the questions that Gemini asked me. 2. “I will focus on labor and worker rights and use the first idea as is (“The impact of fast fashion on garment worker wages and working conditions”). Adapt the third bullet point (“The effectiveness of ethical sourcing initiatives in fast fashion supply chains”) to focus more on ethical HR practices, not sourcing initiatives.”	I used the first idea (“the impact of fast fashion on garment worker wages and working conditions”) as-is and adapted the second idea (“the effectiveness of ethical sourcing initiatives in fast fashion supply chains”) to focus on ethical HR practices, not sourcing initiatives. I researched all the supporting information and wrote the paper by myself.
ChatGPT	To identify grammatical mistakes and stylistic issues in my writing but not correct them for me.	“I will paste my HR case study assignment on ethical practices in fast fashion. Highlight the grammar mistakes in my essay using bold and italic markdown but do not correct the mistakes for me. Suggest some improvements to make my text more academic in style. Also suggest some better collocations. Include specific examples from my text along with your suggestions, but do not re-write my text for me. Ask me any questions before you begin.”	I agreed with most of the suggestions – especially the refined collocations – and made changes in my assignment before submitting. All the changes were written by me – I did not have ChatGPT re-write anything for me. The revision history in my Google Doc clearly shows the changes I made.

(if required)

Google Gemini

In the ideation phase of the assignment to suggest some possible topics for my paper.

“Generate a list of potential topics for an undergraduate research paper on sustainable business practices in the fashion industry. Ask me any questions before you begin.”

I followed up with two additional prompts:

1. I answered the questions that Gemini asked me.
2. “I will focus on labor and worker rights and use the first idea as is (“The impact of fast fashion on garment worker wages and working conditions”). Adapt the third bullet point (“The effectiveness of ethical sourcing initiatives in fast fashion supply chains”) to focus more on ethical HR practices, not sourcing initiatives.”

I used the first idea (“the impact of fast fashion on garment worker wages and working conditions”) as-is and adapted the second idea (“the effectiveness of ethical sourcing initiatives in fast fashion supply chains”) to focus on ethical HR practices, not sourcing initiatives. I researched all the supporting information and wrote the paper by myself.

ChatGPT

To identify grammatical mistakes and stylistic issues in my writing but not correct them for me.

“I will paste my HR case study assignment on ethical practices in fast fashion. Highlight the grammar mistakes in my essay using bold and italic markdown but do not correct the mistakes for me. Suggest some improvements to make my text more academic in style. Also suggest some better collocations. Include specific examples from my text along with your suggestions, but do not re-write my text for me. Ask me any questions before you begin.”

I agreed with most of the suggestions – especially the refined collocations – and made changes in my assignment before submitting. All the changes were written by me – I did not have ChatGPT re-write anything for me. The revision history in my Google Doc clearly shows the changes I made.

Status and Details

Reference number:, document type:, issuing by:.

Teaching and Learning Excellence Center

Issuing Date:

Sep 05, 2024

Applying for:

All VinUniversity faculty, teaching assistants, staff, and students

Security Classification:

Record of Changes

Revision	Date	Author / Editor	Description
V1.0	Sep 05, 2024	Developed by: Teaching and Learning Excellence Center Reviewed by: Educational Affairs Committee Approved by: Provost	First release

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BMJ Journals

Volume 14, Issue 9
Evaluating the impact of artificial intelligence-assisted image analysis on the diagnostic accuracy of front-line clinicians in detecting fractures on plain X-rays (FRACT-AI): protocol for a prospective observational study
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http://orcid.org/0000-0002-5880-8235 Alex Novak 1 ,
Max Hollowday 2 ,
http://orcid.org/0000-0003-0967-3554 Abdala Trinidad Espinosa Morgado 1 ,
Jason Oke 3 ,
http://orcid.org/0000-0001-6642-9967 Susan Shelmerdine 4 , 5 , 6 ,
http://orcid.org/0000-0001-9598-189X Nick Woznitza 7 , 8 ,
David Metcalfe 2 ,
Matthew L Costa 2 , 3 , 9 ,
http://orcid.org/0000-0003-3964-0809 Sarah Wilson 10 ,
Jian Shen Kiam 2 ,
http://orcid.org/0000-0002-0513-7220 James Vaz 2 ,
http://orcid.org/0000-0002-6123-9838 Nattakarn Limphaibool 2 ,
Jeanne Ventre 11 ,
Daniel Jones 11 ,
Lois Greenhalgh 12 ,
Fergus Gleeson 13 ,
Nick Welch 12 ,
Alpesh Mistry 14 , 15 ,
Natasa Devic 2 ,
James Teh 16 ,
http://orcid.org/0000-0001-9614-5033 Sarim Ather 2
1 Emergency Medicine Research Oxford , Oxford University Hospitals NHS Foundation Trust , Oxford , UK
2 Oxford University Hospitals NHS Foundation Trust , Oxford , UK
3 Nuffield Department of Primary Care Health Sciences , University of Oxford , Oxford , UK
4 Clinical Radiology , Great Ormond Street Hospital for Children , London , UK
5 Radiology , UCL GOSH ICH , London , UK
6 NIHR Great Ormond Street Hospital Biomedical Research Centre , London , UK
7 Radiology , University College London Hospitals NHS Foundation Trust , London , UK
8 Canterbury Christ Church University , Canterbury Christ Church University , Canterbury , UK
9 Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), Oxford Trauma & Emergency Care (OxTEC) , University of Oxford , Oxford , UK
10 Frimley Health NHS Foundation Trust , Frimley , UK
11 Gleamer SAS , Paris , France
12 Patient and Public Involvement Member , Oxford , UK
13 Department of Oncology , University of Oxford , Oxford , UK
14 Liverpool University Hospitals NHS Foundation Trust , Liverpool , UK
15 North West MSK Imaging , Liverpool , UK
16 Nuffield Orthopaedic Centre , Oxford University Hospitals NHS Foundation Trust , Oxford , UK
Correspondence to Dr Alex Novak; Alex.Novak{at}ouh.nhs.uk

Introduction Missed fractures are the most frequent diagnostic error attributed to clinicians in UK emergency departments and a significant cause of patient morbidity. Recently, advances in computer vision have led to artificial intelligence (AI)-enhanced model developments, which can support clinicians in the detection of fractures. Previous research has shown these models to have promising effects on diagnostic performance, but their impact on the diagnostic accuracy of clinicians in the National Health Service (NHS) setting has not yet been fully evaluated.

Methods and analysis A dataset of 500 plain radiographs derived from Oxford University Hospitals (OUH) NHS Foundation Trust will be collated to include all bones except the skull, facial bones and cervical spine. The dataset will be split evenly between radiographs showing one or more fractures and those without. The reference ground truth for each image will be established through independent review by two senior musculoskeletal radiologists. A third senior radiologist will resolve disagreements between two primary radiologists. The dataset will be analysed by a commercially available AI tool, BoneView (Gleamer, Paris, France), and its accuracy for detecting fractures will be determined with reference to the ground truth diagnosis. We will undertake a multiple case multiple reader study in which clinicians interpret all images without AI support, then repeat the process with access to AI algorithm output following a 4-week washout. 18 clinicians will be recruited as readers from four hospitals in England, from six distinct clinical groups, each with three levels of seniority (early-stage, mid-stage and later-stage career). Changes in the accuracy, confidence and speed of reporting will be compared with and without AI support. Readers will use a secure web-based DICOM (Digital Imaging and Communications in Medicine) viewer ( www.raiqc.com ), allowing radiograph viewing and abnormality identification. Pooled analyses will be reported for overall reader performance as well as for subgroups including clinical role, level of seniority, pathological finding and difficulty of image.

Ethics and dissemination The study has been approved by the UK Healthcare Research Authority (IRAS 310995, approved on 13 December 2022). The use of anonymised retrospective radiographs has been authorised by OUH NHS Foundation Trust. The results will be presented at relevant conferences and published in a peer-reviewed journal.

Trial registration numbers This study is registered with ISRCTN ( ISRCTN19562541 ) and ClinicalTrials.gov ( NCT06130397 ). The paper reports the results of a substudy of STEDI2 (Simulation Training for Emergency Department Imaging Phase 2).

Artificial Intelligence
Diagnostic Imaging
Fractures, Closed
Emergency Service, Hospital
RADIOLOGY & IMAGING

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/bmjopen-2024-086061

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STRENGTHS AND LIMITATIONS OF THIS STUDY

This study uses a detailed artificial intelligence-assisted fracture detection algorithm with a National Health Service-derived dataset.

A broad set of health professionals will be recruited as participants, including under-represented groups such as nurse practitioners and physiotherapists.

The enhanced dataset will allow evaluation of a broad range of pathologies, including rare but significant fractures.

The dataset will have an abnormally high disease prevalence (50%) to include a broad range of pathologies.

The small number of readers may reduce the statistical power for comparison between professional groups.

Introduction

Missed fractures are a source of serious harm for patients attending the emergency departments (EDs) and represent the most common diagnostic error in that clinical setting. 1 Almost 2 million fractures occur annually in the UK with a lifetime prevalence of nearly 40%, 2 while 5.1% of all ED attendances are for fractures or dislocations. National Health Service (NHS) Resolution has identified that misinterpretation of plain radiographs was the most common error leading to a successful claim for negligent ED care, leading to significant impacts on the lives of affected patients. 3 Reported consequences include death, disability, deformity, need for further or prolonged treatments, chronic pain, emotional distress and loss of trust in the health service. 4 Furthermore, the need for further attendances and prolonged or corrective treatment leads to significant excess healthcare costs. 5

Most acute fractures are diagnosed by ED clinicians using plain radiographs as the first-line imaging investigation (National Clinical Guideline Centre, 2016), a task which requires time, skill and expertise. However, few of the clinicians fulfilling this role have any formal image interpretation training, and they vary significantly in experience. 6 Furthermore, a workforce shortage of radiologists in the UK means that they are rarely able to undertake the primary evaluation of plain radiographs in ED. 7 The high service pressures in UK EDs combined with a highly transient workforce results in a busy and distracting clinical environment that predispose to error and missing fractures on plain radiographs. An estimated 3.3% of fractures are missed on initial interpretation by ED staff. 8 The error rate is higher on radiographs interpreted outside daytime working hours, which suggests that fatigue, workload and shift patterns may impact clinician performance. 9

Over the last decade, advances in computer vision and machine learning have been used to augment interpretation of medical imaging. 10 Several artificial intelligence (AI) algorithms have been developed that are able to detect fractures on plain radiographs with a high degree of accuracy. 11 One such algorithm is the Gleamer BoneView (Gleamer, Paris, France) (see figure 1 ), which is currently the mostly widely used fracture detection algorithm in the NHS as well as worldwide (>800 sites in 30 countries). This algorithm estimates the likelihood of a fracture being present on a radiograph and provides users with three outcomes: fracture , no fracture and uncertain . If the likelihood has been estimated to be above a designated cut-off value, the area of abnormality is highlighted as a region of interest on a secondary image, which is made available to clinicians via their picture archive and communication system. If no abnormality is detected, this is also stated on the secondary image. 12 13 Prior studies have demonstrated that the algorithm is highly accurate at detecting abnormalities, and it is already in use in a number of European centres, having received regulatory approval for use to support clinicians interpreting plain radiographs. Previous research has suggested that the algorithm is highly accurate at detecting abnormalities, and it is already in use in a number of European centres, having received regulatory approval for use to support clinicians interpreting X-rays. Moreover, recent studies have suggested that the use of AI software for detecting bone fractures 14 15 can drastically decrease the rate of missed fractures. However, this software has not yet been fully tested in a UK setting using a locally derived dataset, and it is unclear to what degree such systems would affect the diagnostic performance of certain staff groups specific to the NHS, such as reporting radiographers and specialist nurse practitioners.

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Image of Gleamer Boneview showing artificial intelligence-assisted overlay.

This study will evaluate the impacts of a commercially available AI-assisted image interpretation tool (Gleamer BoneView) on the diagnostic performance of the full range of clinicians (including nurses and allied health professionals) who routinely diagnose fractures in the NHS. It will address this evidence gap in the current evidence base, in line with the NICE (National Institute for Health and Care Excellence) Evidence Standards Framework for Digital Health Technologies, and recent Early Value Assessments which highlight the dearth of prospective evidence to support the use of AI-assisted image interpretation algorithms in the UK healthcare setting. Automation bias (the propensity for humans to favour suggestions from automated decision-making systems) is a known source of error in human-machine interaction 16 and has been one of a number of causes for concern regarding the increasing usage of AI in radiology. 17 A recent reader study in mammography, 18 suggested significant automation bias presence across all levels of experience, noting that it was only the high-experienced reporters that consistently picked up on AI error. During our study, we will also assess the impact of incorrect advice given by the algorithm on the clinical end users. 19

To evaluate the impact of AI-enhanced imaging on the diagnostic performance, efficiency and confidence of clinicians in detecting fractures on plain radiographs (primary).

To determine the stand-alone diagnostic accuracy of the BoneView AI tool with respect to the reference standard (secondary).

To determine associations between professional background and level of experience when determining the impact of AI support on clinician fracture detection (secondary).

To explore which imaging factors influence clinicians’ reporting accuracy and efficiency, and algorithm performance, for example, category of abnormality, size of abnormality, image quality, presence of multiple abnormalities (secondary).

To measure whether clinicians are more likely to make a mistake when AI provides an incorrect diagnosis (secondary).

Methods and analysis

Study design.

This study employs a multiple reader multiple case (MRMC) methodology. This approach involves multiple readers of various specialties and experience levels interpreting a large set of radiographs with and without AI assistance. The study processes are summarised in the flowchart in figure 2 , with the dataflows represented in figure 3 . The study design encompasses several key elements, including participant selection, case reading procedures, ground truthing process, case selection and AI algorithm inference on cases, which will be described in detail in the following subtitles.

Study flowchart for artificial intelligence-assisted image analysis on the diagnostic accuracy of front-line clinicians in detecting fractures on plain X-rays multicase multireader study. AI, artificial intelligence; XRs, X-rays.

Artificial intelligence-assisted image analysis on the diagnostic accuracy of front-line clinicians in detecting fractures on plain X-rays study dataflows. AI, artificial intelligence; RAIQC, Report and Image Quality Control; XR, X-rays.

Participants

In order to explore the effects of using the algorithm on the full range of clinicians who diagnose fractures in routine practice and minimise selection bias, we have created a balanced matrix of readers in terms of specialty and seniority. 18 readers will be recruited from the following specialties (six specialities with three readers from each):

Emergency physicians.

Trauma and orthopaedic surgeons.

Emergency nurses practitioners.

Physiotherapists.

General radiologists.

Reporting radiographers.

Each specialty group will consist of a reader each fulfilling one of the following three levels of seniority:

Consultant/senior/equivalent: >10 years experience.

Registrar/equivalent: 5–10 years experience.

Senior house officer/equivalent: <5 years experience.

Each specialty reader group will include one reader at each level of experience. Readers will be excluded if they have significant radiology experience in excess of their current specialty or grade. Prior use of fracture detection software does not exclude participation, as it is not expected in itself to confer a change in performance unless actively used during interpretation.

Readers will be recruited from across four NHS organisations that comprise the Thames Valley Emergency Medicine Research Network ( www.TaVERNresearch.org ):

Oxford University Hospitals (OUH) NHS Foundation Trust.

Royal Berkshire NHS Foundation Trust.

Frimley Health NHS Foundation Trust.

Milton Keynes University Hospital NHS Foundation Trust.

Participants will be recruited through a structured invitation process coordinated by the research team. A designated team member will collaborate with clinical leads and research coordinators at each participating site within the Thames Valley Emergency Medicine Research Network to identify potential participants based on predetermined criteria. These criteria include fulfilment of the required specialty and experience level categories, demonstrated commitment to professional development and research, and ability to commit to the full duration of the study.

All invitations will be extended based on the aforementioned criteria, and participation will be voluntary, maintaining objectivity throughout the recruitment process.

The reads will be performed using a secure web-based DICOM viewer ( www.raiqc.com ). The platform allows readers to view radiographs and identify the site of an abnormality with a mouse click. The images will be viewable through a web browser on desktop or laptop devices, reflecting standard real-world hospital practice in which radiographs are typically interpreted by clinicians without dedicated high-resolution viewing stations.

Prior to beginning each phase of the study, the readers will undergo a training module that includes reading 5 practice images (not part of the 500-image dataset) to familiarise themselves with the use of the study platform and the output of the AI tool.

Case selection and composition

The image dataset will include anonymised radiographs of adult patients (≥18 years) who presented to the EDs of OUH NHS Foundation Trust with a suspicion of fracture after injury to the limbs, pelvis or thoracolumbar spine. As CT is the investigation of choice for skull and many cervical spine injuries, these will be excluded from the study. Paediatric patients will be excluded from the dataset as their fracture types differ from those in adults, and there is an ongoing study evaluating this aspect (FRACTURE study; Fast Reporting using Artificial Intelligence for Children's TraUmaticRadiology Examinations 12 ). Obvious fractures (defined as fractures including any of the following: displacement>5 mm, shortening>5 mm or angulation>5°) will also be excluded.

To constitute the dataset, radiology reports will be screened from the radiology information system to develop an enriched dataset of the 500 standard clinical examinations evenly split between normal and abnormal, with one or more fractures. The ratio of radiographs from each anatomical location has been informed by the proportion of missed fractures mentioned in the NHS Resolution report ( table 1 ).

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Proportion of radiographs of each anatomical location, based on the proportion of missed fractures mentioned in the National Health Service Resolution report

To ensure a like-for-like comparison, image finding for abnormal cases will be performed first. The normal images will be age and sex matched per body part. We will aim to include representation of the different image views, system type (mobile or fixed), system vendors and patient demographics (eg, age, sex) without any prespecified quota.

The dataset will then be anonymised and uploaded to the Report and Image Quality Control platform under an existing data governance approval from the OUH NHS Foundation Trust Caldicott guardian.

Case inclusion and exclusion summary

Plain radiographs of adult patients (age>18 years) presenting to the OUH ED with a suspected fracture.

Plain skull radiographs.

Plain cervical spine radiographs.

Follow-up radiographs for known fracture.

Paediatric radiographs (age<18).

Obvious fractures defined as:

Displacement>5 mm.

Shortening>5 mm.

Angulation>5°.

Inferencing the image dataset

The entire dataset of images will then be separately analysed using BoneView, creating a duplicate dataset of radiographs with alerts and regions of interest indicated.

Radiographic interpretation

All readers will review all 500 radiographs individually across 2 reporting rounds.

In the first round, they will interpret the images as per clinical practice without any AI assistance. After a washout period of a month to mitigate the effects of recall bias, they will review the same 500 radiographs a second time with the assistance of the algorithm, which will contribute its suggestions as to abnormality presence and location. In both sessions, clinicians will be blinded to the ground truth established by the MSK (musculoskeletal) radiologists.

Clinician readers will be asked to identify the presence or absence of fracture by placing a marker on the image at the location of the fracture (if present) and to rank their confidence for fracture identification. Confidence rating will take the form of a Likert scale from 1 to 5 with 1 being least confident and 5 most confident.

Ground truthing

The gold standard reference process will be conducted by two experienced musculoskeletal radiologists (>10 years’ experience) who will independently review and annotate each of the 500 radiographs in the dataset. They will draw bounding boxes around each detected fracture and grade the images on both image quality and difficulty of abnormality detection using a 5-point Likert scale.

In cases of disagreement between the two primary radiologists regarding the presence or absence of abnormalities, a third senior musculoskeletal radiologist will review the contentious images and make a final decision.

All annotations, gradings and arbitration decisions will be documented within the secure web-based DICOM viewer platform, establishing a reliable reference standard for evaluating both human reader performance and AI assistance.

In the event of significant discrepancies persisting after the initial arbitration process, a consensus meeting will be agreed. This meeting will include the primary ground truth radiologists, the arbitrator and key members of the research team. The purpose of this meeting will be to review and resolve any remaining discrepancies, ensuring the integrity and consistency of the final reference standard. This collaborative approach will be employed only for cases where substantial disagreement remains, thereby maintaining the overall objectivity of the ground truth process while addressing complex or ambiguous cases.

Study timeline

This study commenced on 8 February 2024 and is actively collecting data. The data collection and analysis phase is projected to finish by the end of September 2024 with write up and publication anticipated later in the year.

Outcome measures

Reader and AI performance will be evaluated using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and area under receiver operating characteristic curve (AUC). Reader performance will be evaluated with and without AI assistance.

Reader speed will be evaluated as the mean review time per scan, with and without AI assistance.

Reader confidence will be evaluated as self-reported diagnostic confidence on a 5-point Likert scale, with and without AI assistance.

Data statement and management

Radiographs selected for the study will be anonymised in accordance with OUH NHS Foundation Trust information governance protocol and uploaded to the secure image viewing platform ( www.raiqc.com ). Access to the radiographs will be controlled via the study platform using separate user accounts for each reader.

All study data will be entered into a password-protected and secure database. Individual reader accuracy scores will be anonymised, and the study team will not have access to the identifying link between the participants’ personal details and the data. Data about the participants’ seniority level and professional group will be retained to allow group comparisons.

Sample size and power calculation

The study’s sample size of 500 images, evenly split between normal and abnormal cases, was determined using the Multi-Reader Sample Size Program for Diagnostic Studies. This tool, developed by Hillis, 20 is specifically designed for MRMC study power calculations. Based on parameters derived from our previous MRMC study on pneumothorax detection, the programme calculated that with 18 readers and 500 cases, our study will achieve 85% power to detect a 10% difference in accuracy between unassisted and AI-assisted readings, with a 5% type 1 error rate (See output from software below).

The chosen sample size of 500 images ensures sufficient statistical power and adequate representation of fracture types and anatomical locations. This robust sample size, combined with our substantial and diverse reader pool, should enable the detection of clinically significant improvements in fracture detection accuracy and allow for subgroup analyses across specialties and experience levels. By using this rigorously calculated sample size, we aim to produce statistically robust and clinically relevant results that can inform the potential integration of AI assistance in fracture detection across various clinical settings, while adequately addressing our study objectives and maintaining statistical validity.

Statistical analyses

The performance of the algorithm will be compared with the ground truth generated by the musculoskeletal radiologist panel. The continuous probability score from the algorithm will be used for the AUC analyses, while binary classification results with three different operating cut-offs will be used for evaluation of sensitivity, specificity, PPV and NPV. Sensitivity and specificity of readers with and without AI will be tested based on the Obuchowski-Rockette model for MRMC analysis which will model the data using a two-way mixed effects analysis of variance (ANOVA) model treating readers and cases (images) as random effects and effect of AI as a fixed effect with recommended adjustment to df by Hillis. 21

The difference in diagnostic characteristics (sensitivity, specificity, accuracy, area under the receiver operating characteristic (ROC) curve) of readers as compared with ground truth with and without AI assistance will be the primary outcome on a per image and per abnormality basis. The main analysis will be performed as a single pooled analysis including all groups and sites. Secondary outcomes will include comparison between the performance of subgroups by specialty (emergency medicine, trauma/orthopaedics, physiotherapy, nurse practitioner, radiologist, radiographer), level of seniority (senior, middle grade, junior), degree of difficulty of the image and by anatomical region. Reader-reported confidence with and without the AI assistance will be compared. Secondary outcomes include the diagnostic characteristics of the AI algorithm alone. Surveys will be conducted throughout the study to measure the satisfaction, adoption and confidence in the AI algorithm of the study participants. Per-patient sensitivity will be defined as the proportion of reads in which all true fractures were marked as a proportion of the reads having at least one fracture. Per-patient specificity will be defined as the proportion of reads in which no fracture was marked by the reader as a proportion of the reads that did not show a fracture. These definitions disregard the detection of multiple fractures thus we will define the fracture-wise sensitivity as the proportion of fractures correctly detected as a proportion of all fractures. The two coprimary outcomes will be patient-wise sensitivity and patient-wise specificity. The stand-alone algorithm performance will be assessed by calculating the area under the curve (AUC) of the ROC and free-response ROC curves plotted with their variance. To account for correlated errors arising from readers interpreting the same images with and without AI, the Obuchowski and Rockette, Dorfman-Berbaum-Metz 22 procedure; a modality-by-reader random effects ANOVA model will be used for estimation. Analyses will be carried out using R and the MRMCaov library.

Strengths and limitations

This study uses a CE (Conformité Européenne)-marked AI-assisted fracture detection algorithm with an NHS-derived dataset. The enhanced dataset will allow evaluation of a broad range of pathologies, including rare but significant fractures and its composition is mapped to mirror the proportions of missed fracture locations seen in the NHS Resolution report. A broad set of health professionals will be recruited as participants, including under-represented groups such as nurse practitioners and physiotherapists, from multiple hospital sites across the region—these reflect a reader group not yet explored in the literature, and one directly applicable to the NHS.

In terms of limitations, while the overall study group is large in comparison to other similar reader studies, the small number of readers in subgroups may reduce the statistical power for comparison between professional groups. The dataset will include an abnormally high disease prevalence (50%) to include a broad range of pathologies to facilitate meaningful statistical comparison, meaning that while the reader study will effectively explore the impact of the algorithm on readers interpreting a broad and detailed dataset, the results will not mirror the prevalence of pathologies encountered in normal clinical practice and further prospective study will be required to determine efficacy in this regard.

Patient and public involvement (PPI)

This protocol has been reviewed by the Oxford ACUTECare PPI group and PPI representatives on the artificial intelligence-assisted image analysis on the diagnostic accuracy of front-line clinicians in detecting fractures on plain X-rays steering group. They have supported the study and its aims, were involved in the grant application, design and data management stages and have advised on dissemination strategies.

Ethics and dissemination

The study has been approved by the UK Health Research Authority (IRAS number 310995, approved on 13 December 2022). The use of anonymised retrospective radiographs has been authorised by the Caldicott Guardian and information governance team at OUH NHS Foundation Trust. Readers will provide written informed consent and will be able to withdraw at any time.

The study is registered at Clinicaltrials.gov ( NCT06130397 ) and the ISRCTN ( ISRCTN19562541 ) registry (approval pending reference 44612). The results of the study will be presented at relevant conferences and published in peer-reviewed journals. The detailed study protocol will be freely available on request to the corresponding author. Further dissemination strategy will be strongly guided by our PPIE (Patient and Public Involvement and Engagement) activities. This will be based on co-productions between patient partners and academics and will involve media pieces (mainstream and social media) as well as communication through charity partners. Key target audiences will include non-specialist clinicians routinely involved in fracture detection, as well as hospital managers, health policy-makers and academics working in AI-assisted image analysis.

Ethics statements

Patient consent for publication.

Consent obtained directly from patient(s).

Acknowledgments

The authors would link to thank FRACT-AI steering committee: Matthew Costa, Natasa Devic, Fergus Gleeson, Divyansh Guilati, Daniel Jones, Jian Shen Kiam, Nattakarn Limphaibool, David Metcalfe, Jason Oke, Ravi Shashikala, Susan Shelmerdine, James Teh, Simon Triscott, Jeanne Ventre, James Vaz, Nick Welch, Sarah Wilson, Nicholas Woznitza.

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X @SusieShels, @xray_nick

Contributors AN and SA led the conception and overall study design, contributed to protocol review and are co-chief investigators of the study. Both will carry out the analysis and interpretation of the results, independently write up the findings and handle publication. AN led the NIHR grant application and provided specialist emergency medicine input. SA provided specialist radiology input. MH and ATEM contributed to protocol drafting, study registration and recruitment. JO will carry out the independent statistical analysis. FG, SS, NW, DM, MLC, SW, JSK, JVaz, NL, JVentre and DJ were involved in study design and provided steering group inputs. ND and AM serve as ground truthers for the study, and JT serves as the arbitrator. LG and NW are PPI representatives. All authors contributed to the writing of the protocol and reviewed the manuscript. The guarantor of the study is AN; accepts full responsibility for the finished work and/or the conduct of the study, had access to the data, and controlled the decision to publish.

Funding This work was supported by the NIHR Research for Patient Benefit in Health and Care Award (NIHR204982).

Competing interests JV and DJ of the Steering Committee are employees of Gleamer SAS, France. SA is a shareholder of RAIQC, UK. All other authors declare no competing interests.

Patient and public involvement Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods and analysis section for further details.

Provenance and peer review Not commissioned; externally peer reviewed.

Author note Transparency Declaration: The lead author, AN, affirms that this manuscript is an honest, accurate and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned have been explained.

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A sketch made from black marker of a man standing in front of a table with a blank sheet of paper.

Opinion Guest Essay

I’m Just a Human Sitting in Front of a Stack of Blank Paper, Sketching as Fast as I Can

Supported by

Text and Illustrations by Pablo Delcan

Pablo Delcan is a designer and visual artist and the creator of Prompt-Brush 1.0.

Sept. 5, 2024

Last December, I announced the creation of the very first non-artificial-intelligence generative art model.

The “model,” which I called Prompt-Brush, consisted of a simple text box that people could use to send prompts, and me at the other end, with a stack of blank paper. The experiment started as a post on social media; I didn’t expect it to go viral. But within weeks I was receiving thousands of prompts from all around the world, written in over a dozen languages. For the past nine months, I have been drawing and delivering these images every day.

The prompts have varied widely, from deeply personal stories of relationships, loss and regret to whimsical and surreal scenarios involving animals and pop culture references. I sketch them quickly, with a paintbrush and black ink: I aim for a rapid reaction, akin to how an A.I. might respond. But because I’m a human, the process is necessarily different. For me, each prompt offers a small view into the life of the prompter, and the drawings reflect what I think a good answer might be.

I’ve found single-word prompts, such as “forgiveness,” “loneliness,” “happiness,” “longing” and “victory,” challenging because they can mean so many different things to different people. I’ve found that the most truthful way for me to respond to these is to focus on my experiences and memories. The drawing for forgiveness, for example, ended up being a bare foot being stepped on by someone wearing a shoe; I had accidentally stepped on my daughter that morning. The prompt “victory” was a drawing of a hand with a squashed mosquito on its palm — sweet revenge.

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The Future of AI Research: 20 Thesis Ideas for Undergraduate Students in Machine Learning and Deep Learning for 2023!

1. Investigating the use of Generative Adversarial Networks (GANs) in medical imaging: A deep learning approach to improve the accuracy of medical diagnoses.

2. Exploring the use of deep learning in natural language generation (NLG): An analysis of the current state-of-the-art and future potential.

3. Development and evaluation of deep reinforcement learning (RL) for robotic navigation and control.

4. Investigating the use of deep learning for drug discovery and development.

5. Comparison of deep learning and traditional machine learning methods for anomaly detection in time series data.

6. Use of deep transfer learning in speech recognition and synthesis.

7. The use of deep learning for financial prediction.

8. Investigating the use of deep learning for computer vision in agriculture.

9. Development and evaluation of deep learning models for generative design in engineering and architecture.

10. Investigating the use of deep learning for natural language understanding.

11. Comparing deep learning and traditional machine learning methods for image compression.

12. Using deep learning for sentiment analysis in social media.

13. Investigating the use of deep learning for image generation.

14. Development and evaluation of deep learning models for anomaly detection in cybersecurity.

15. Investigating the use of deep learning for natural language summarization.

16. Development and evaluation of deep learning models for facial expression recognition.

17. Investigating the use of deep learning for generative models in music and audio.

18. Study the comparison of deep learning models with traditional algorithms for anomaly detection in network traffic.

19. Investigating the use of deep learning for improving recommender systems.

20. Development and evaluation of deep learning models for multi-modal data analysis.

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