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Editorial article, editorial: artificial intelligence for education.

• 1 Institute for Educational Technology, National Research Council of Italy, Palermo, Italy
• 2 EA4023 Laboratoire d'Informatique de l'Université du Mans (LIUM), Le Mans, France
• 3 Department of Computing Science, Umeå University, Umeå, Sweden

Editorial on the Research Topic Artificial intelligence for education

When the Research Topic “ Artificial intelligence for education ” was launched in June 2021, the impact that advances in artificial intelligence would have on the education sector was not entirely predictable.

However, the long and close relationship between research in the two fields of AI and Education was common knowledge. Indeed, since understanding how people learn is closely related to the idea of intelligence, or given that knowledge representation has been one of the most prominent Research Topics in AI, a natural connection between the areas of knowledge concerning Artificial Intelligence and Education emerged even before the term “Artificial Intelligence” was coined ( Turing, 1950 ).

Scholars in the field of artificial intelligence have always looked to the field of education as one of their favorite application areas. From the realization of the logic theorist ( Newell and Simon, 1956 ) to the emergence in the 1990s of cognitive architectures ( Laird et al., 1987 ; Newell, 1990 ), many of the innovations in the field of AI have found a direct application in the field of education, in the realization of tools such as expert systems to support learning processes and intelligent tutor systems ( Anderson et al., 1985 ; Bidarra et al., 2020 ).

The new renaissance of AI, marked by recent innovations in the field of deep learning, has in recent years outlined a landscape in which a strong impact could also be expected in education. However, the disruption caused by the market introduction of ChatGPT in November 2022 coincided with the final part of the call for papers for this Research Topic. This timing has therefore cut off, from many of the studies presented in this Research Topic, all the latest research, especially that related to generative AI and large language models (LLM).

Nevertheless, the topic we have been supervising for the past 2 years has allowed us to closely monitor this rapid change, collecting contributions that have proposed and analyzed various topics related to AI in Education. Two recent contributions to the topic by Mallik and Gangopadhyay and Gentile et al. provide an overview of the trend.

Mallik and Gangopadhyay examine how AI, machine learning and deep learning methods are currently used to support the educational process. They conduct this examination by analyzing the involvement of AI-driven methods in the educational process considered as a whole. Based on the analysis of a large set of papers, the authors outline the main trends of future research concerning the use of AI in Education with particular reference to some paradigmatic shifts in the approaches analyzed.

Gentile et al. analyze one of the most exciting topics about AI and Education: the impact of AI on teachers' roles through a systematic literature review. Teachers have always been called upon to change their practices by attempting to integrate new technologies rather than rejecting them. However, even at first glance, the potential changes introduced by AI signal a radical change, what can be called a genuine paradigm shift in teachers' role in Education. According to the authors, the literature analysis reveals that full awareness of the urgency with which the challenges imposed by AI in Education must be addressed has yet to be achieved. Moreover, the study proposes a manifesto to guide the evolution of teachers' roles according to the paradigm shift proposed by Kuhn in the scientific field.

To be managed adequately and avoid causing discomfort in education systems, the assumed changes in the teacher's role should be accompanied by appropriate professional development programmes. In this regard, Sáiz-Manzanares et al. address the topic of designing teacher training programmes that combine the use of technology and instructional design to promote the development of Self-Regulated Learning and automatic feedback systems. Through a study involving 23 secondary school teachers in a training programme delivered with Moodle, the authors investigated the differences in the behavior of experienced and inexperienced teachers, the consistency of the behavior patterns extracted during the study, with the respective type of teacher being modeled, and the teachers' level of satisfaction with the training activity on digital didactics.

The development of assessment tools is one of the preferred areas of application of AI in Education, and, in this respect, AI-based learning analytics will play a key role.

Student-generated texts represent an essential but often unexplored source of information for gaining deeper insights into learners' cognition and ensuring better compliance with students' real needs. To this regard, Berding et al. present a new approach based on applying item response theory concepts to content analysis for the analysis of the textual data generated by the student. They present the results of three studies conducted to make textual information usable in the context of learning analytics. By producing a new content analysis measure, simulating a content analysis process and analyzing the performance of different AI approaches for interpreting textual data, they show that AI can reliably interpret textual information for learning purposes and also provide recommendations for an optimal configuration of AI.

Fleckenstein et al. present a systematic review to explore the effectiveness of AI-based Automated writing evaluation (AWE) tools in realizing systems capable of assessing students' writing skills and providing them with timely feedback with a view to formative assessment. The results confirm a medium-size effect and highlight how it is necessary to continue the exploration by identifying groups of interventions that are more homogeneous among themselves, trying to identify those factors that distinguish these interventions.

Cloude et al. propose an analysis and interpretation framework of real-time multimodal data to support students' Self Regulated Learning (SRL) processes. Specifically, their paper thematises the issues researchers and instructors face when using the data collected through innovative technologies. By recalling a specific procedure through which a researcher/instructor can standardize, process, analyze, recognize and conceptualize multimodal data, they discuss various implications for constructing valid and effective AI algorithms to foster students' SRL.

Cheng et al. address the topic of personalisation of learning using dynamic learning data to track the state of students' knowledge over time. Specifically, the authors propose a context-aware attentive knowledge query network model that can combine flexible neural network models with interpretable model components inspired by psychometric theory to analyze the exercise data.

Chichekian and Benteux propose an exploratory review to describe how the effectiveness of AI-based technologies is measured, the roles attributed to teachers and both theoretical and practical contributions. From the research conducted, it emerges, according to the authors, that the role of teachers is underestimated and that the optimisation of AI systems is still nested exclusively in a strictly IT perspective.

The conscious and informed use of AI and tools that make use of AI is a critical indicator of the maturity of the community that benefits from these instruments. On the contrary, conscious use allows all the potential that can be found in AI to be turned into concrete gains. In this regard, Zammit et al. emphasize the importance of the diffusion and understanding of AI and Machine Learning and the associated ethical implications. To this end, the authors exploit a digital game designed and developed to teach AI and ML core concepts and to promote critical thinking about their functionalities and shortcomings in everyday life.

The paper by Ninaus and Sailer also fits into the groove of critical and aware use of AI in Education. The authors explore humans' role in decision-making in designing and implementing artificial intelligence in Education. Considering the essential role of users in decision-making in educational contexts and emphasizing the need to balance human- and AI-driven decision-making and mutual monitoring, they address both cases in which some AI implementations might make decisions autonomously and cases in which students and teachers, having received information from an AI, are enabled to make reasoned decisions.

Much remains to be done to understand how AI is changing educational practices and how the key stakeholders in the educational community (i.e., students, teachers, faculty, and families) perceive this ongoing change. Nevertheless, the Research Topic provides a broad picture of ongoing changes and a starting point in a research path that will develop over the coming years involving many experts in AI and Education fields.

We believe it is important to renew this Research Topic so that the most recent findings can be shared and systematically analyzed in order to support the progress of this field.

Author contributions

MG: Writing—original draft, Writing—review & editing, Conceptualization, Validation. GC: Writing—original draft, Writing—review & editing, Validation. IM-S: Validation, Writing—review & editing. FD: Validation, Writing—review & editing. MA: Validation, Writing—review & editing.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Anderson, J. R., Boyle, C. F., and Reiser, B. J. (1985). Intelligent tutoring systems. Science 228, 456–462. doi: 10.1126/science.228.4698.456

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Bidarra, J., Simonsen, H. K., and Holmes, W. (2020). “Artificial Intelligence in Teaching (AIT): A road map for future developments,” in Empower EADTU, Webinar week: Artificial Intelligence in Online Education. doi: 10.13140/RG.2.2.25824.51207

Laird, J. E., Newell, A., and Rosenbloom, P. S. (1987). SOAR: an architecture for general intelligence. Artif. Intell . 33, 1–64. doi: 10.1016/0004-3702(87)90050-6

Newell, A. (1990). Unified Theories of Cognition . Cambridge, MA: Harvard University Press.

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Newell, A. and Simon, H. (1956). The logic theory machine-a complex information processing system. IEEE Trans. Inform. Theory 2, 61–79. doi: 10.1109/TIT.1956.1056797

Turing, A. M. (1950). Computing machinery and intelligence. Mind 49, 433–460. doi: 10.1093/mind/LIX.236.433

Keywords: Artificial Intelligence and Education (AIED), education, generative AI, learning processes, intelligent tutor systems

Citation: Gentile M, Città G, Marfisi-Schottman I, Dignum F and Allegra M (2023) Editorial: Artificial intelligence for education. Front. Educ. 8:1276546. doi: 10.3389/feduc.2023.1276546

Received: 12 August 2023; Accepted: 25 October 2023; Published: 07 November 2023.

Reviewed by:

Copyright © 2023 Gentile, Città, Marfisi-Schottman, Dignum and Allegra. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Giuseppe Città, giuseppe.citta@itd.cnr.it

This article is part of the Research Topic

Artificial Intelligence for Education

Artificial Intelligence in Education: A Review

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• Review article
• Open access
• Published: 28 October 2019

Systematic review of research on artificial intelligence applications in higher education – where are the educators?

• Olaf Zawacki-Richter   ORCID: orcid.org/0000-0003-1482-8303 1 ,
• Victoria I. Marín   ORCID: orcid.org/0000-0002-4673-6190 1 ,
• Melissa Bond   ORCID: orcid.org/0000-0002-8267-031X 1 &
• Franziska Gouverneur 1  

International Journal of Educational Technology in Higher Education volume  16 , Article number:  39 ( 2019 ) Cite this article

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According to various international reports, Artificial Intelligence in Education (AIEd) is one of the currently emerging fields in educational technology. Whilst it has been around for about 30 years, it is still unclear for educators how to make pedagogical advantage of it on a broader scale, and how it can actually impact meaningfully on teaching and learning in higher education. This paper seeks to provide an overview of research on AI applications in higher education through a systematic review. Out of 2656 initially identified publications for the period between 2007 and 2018, 146 articles were included for final synthesis, according to explicit inclusion and exclusion criteria. The descriptive results show that most of the disciplines involved in AIEd papers come from Computer Science and STEM, and that quantitative methods were the most frequently used in empirical studies. The synthesis of results presents four areas of AIEd applications in academic support services, and institutional and administrative services: 1. profiling and prediction, 2. assessment and evaluation, 3. adaptive systems and personalisation, and 4. intelligent tutoring systems. The conclusions reflect on the almost lack of critical reflection of challenges and risks of AIEd, the weak connection to theoretical pedagogical perspectives, and the need for further exploration of ethical and educational approaches in the application of AIEd in higher education.

Introduction

Artificial intelligence (AI) applications in education are on the rise and have received a lot of attention in the last couple of years. AI and adaptive learning technologies are prominently featured as important developments in educational technology in the 2018 Horizon report (Educause, 2018 ), with a time to adoption of 2 or 3 years. According to the report, experts anticipate AI in education to grow by 43% in the period 2018–2022, although the Horizon Report 2019 Higher Education Edition (Educause, 2019 ) predicts that AI applications related to teaching and learning are projected to grow even more significantly than this. Contact North, a major Canadian non-profit online learning society, concludes that “there is little doubt that the [AI] technology is inexorably linked to the future of higher education” (Contact North, 2018 , p. 5). With heavy investments by private companies such as Google, which acquired European AI start-up Deep Mind for $400 million, and also non-profit public-private partnerships such as the German Research Centre for Artificial Intelligence Footnote 1 (DFKI), it is very likely that this wave of interest will soon have a significant impact on higher education institutions (Popenici & Kerr, 2017 ). The Technical University of Eindhoven in the Netherlands, for example, recently announced that they will launch an Artificial Intelligence Systems Institute with 50 new professorships for education and research in AI. Footnote 2

The application of AI in education (AIEd) has been the subject of research for about 30 years. The International AIEd Society (IAIED) was launched in 1997, and publishes the International Journal of AI in Education (IJAIED), with the 20th annual AIEd conference being organised this year. However, on a broader scale, educators have just started to explore the potential pedagogical opportunities that AI applications afford for supporting learners during the student life cycle.

Despite the enormous opportunities that AI might afford to support teaching and learning, new ethical implications and risks come in with the development of AI applications in higher education. For example, in times of budget cuts, it might be tempting for administrators to replace teaching by profitable automated AI solutions. Faculty members, teaching assistants, student counsellors, and administrative staff may fear that intelligent tutors, expert systems and chat bots will take their jobs. AI has the potential to advance the capabilities of learning analytics, but on the other hand, such systems require huge amounts of data, including confidential information about students and faculty, which raises serious issues of privacy and data protection. Some institutions have recently been established, such as the Institute for Ethical AI in Education Footnote 3 in the UK, to produce a framework for ethical governance for AI in education, and the Analysis & Policy Observatory published a discussion paper in April 2019 to develop an AI ethics framework for Australia. Footnote 4

Russel and Norvig ( 2010 ) remind us in their leading textbook on artificial intelligence, “All AI researchers should be concerned with the ethical implications of their work” (p. 1020). Thus, we would like to explore what kind of fresh ethical implications and risks are reflected by the authors in the field of AI enhanced education. The aim of this article is to provide an overview for educators of research on AI applications in higher education. Given the dynamic development in recent years, and the growing interest of educators in this field, a review of the literature on AI in higher education is warranted.

Specifically, this paper addresses the following research questions in three areas, by means of a systematic review (see Gough, Oliver, & Thomas, 2017 ; Petticrew & Roberts, 2006 ):

How have publications on AI in higher education developed over time, in which journals are they published, and where are they coming from in terms of geographical distribution and the author’s disciplinary affiliations?

How is AI in education conceptualised and what kind of ethical implications, challenges and risks are considered?

What is the nature and scope of AI applications in the context of higher education?

The field AI originates from computer science and engineering, but it is strongly influenced by other disciplines such as philosophy, cognitive science, neuroscience, and economics. Given the interdisciplinary nature of the field, there is little agreement among AI researchers on a common definition and understanding of AI – and intelligence in general (see Tegmark, 2018 ). With regard to the introduction of AI-based tools and services in higher education, Hinojo-Lucena, Aznar-Díaz, Cáceres-Reche, and Romero-Rodríguez ( 2019 ) note that “this technology [AI] is already being introduced in the field of higher education, although many teachers are unaware of its scope and, above all, of what it consists of” (p. 1). For the purpose of our analysis of artificial intelligence in higher education, it is desirable to clarify terminology. Thus, in the next section, we explore definitions of AI in education, and the elements and methods that AI applications might entail in higher education, before we proceed with the systematic review of the literature.

AI in education (AIEd)

The birth of AI goes back to the 1950s when John McCarthy organised a two-month workshop at Dartmouth College in the USA. In the workshop proposal, McCarthy used the term artificial intelligence for the first time in 1956 (Russel & Norvig, 2010 , p. 17):

The study [of artificial intelligence] is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.

Baker and Smith ( 2019 ) provide a broad definition of AI: “Computers which perform cognitive tasks, usually associated with human minds, particularly learning and problem-solving” (p. 10). They explain that AI does not describe a single technology. It is an umbrella term to describe a range of technologies and methods, such as machine learning, natural language processing, data mining, neural networks or an algorithm.

AI and machine learning are often mentioned in the same breath. Machine learning is a method of AI for supervised and unsupervised classification and profiling, for example to predict the likelihood of a student to drop out from a course or being admitted to a program, or to identify topics in written assignments. Popenici and Kerr ( 2017 ) define machine learning “as a subfield of artificial intelligence that includes software able to recognise patterns, make predictions, and apply newly discovered patterns to situations that were not included or covered by their initial design” (p. 2).

The concept of rational agents is central to AI: “An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators” (Russel & Norvig, 2010 , p. 34). The vacuum-cleaner robot is a very simple form of an intelligent agent, but things become very complex and open-ended when we think about an automated taxi.

Experts in the field distinguish between weak and strong AI (see Russel & Norvig, 2010 , p. 1020) or narrow and general AI (see Baker & Smith, 2019 , p. 10). A philosophical question remains whether machines will be able to actually think or even develop consciousness in the future, rather than just simulating thinking and showing rational behaviour. It is unlikely that such strong or general AI will exist in the near future. We are therefore dealing here with GOFAI (“ good old-fashioned AI ”, a term coined by the philosopher John Haugeland, 1985 ) in higher education – in the sense of agents and information systems that act as if they were intelligent.

Given this understanding of AI, what are potential areas of AI applications in education, and higher education in particular? Luckin, Holmes, Griffiths, and Forcier ( 2016 ) describe three categories of AI software applications in education that are available today: a) personal tutors, b) intelligent support for collaborative learning, and c) intelligent virtual reality.

Intelligent tutoring systems (ITS) can be used to simulate one-to-one personal tutoring. Based on learner models, algorithms and neural networks, they can make decisions about the learning path of an individual student and the content to select, provide cognitive scaffolding and help, to engage the student in dialogue. ITS have enormous potential, especially in large-scale distance teaching institutions, which run modules with thousands of students, where human one-to-one tutoring is impossible. A vast array of research shows that learning is a social exercise; interaction and collaboration are at the heart of the learning process (see for example Jonassen, Davidson, Collins, Campbell, & Haag, 1995 ). However, online collaboration has to be facilitated and moderated (Salmon, 2000 ). AIEd can contribute to collaborative learning by supporting adaptive group formation based on learner models, by facilitating online group interaction or by summarising discussions that can be used by a human tutor to guide students towards the aims and objectives of a course. Finally, also drawing on ITS, intelligent virtual reality (IVR) is used to engage and guide students in authentic virtual reality and game-based learning environments. Virtual agents can act as teachers, facilitators or students’ peers, for example, in virtual or remote labs (Perez et al., 2017 ).

With the advancement of AIEd and the availability of (big) student data and learning analytics, Luckin et al. ( 2016 ) claim a “[r] enaissance in assessment” (p. 35). AI can provide just-in-time feedback and assessment. Rather than stop-and-test, AIEd can be built into learning activities for an ongoing analysis of student achievement. Algorithms have been used to predict the probability of a student failing an assignment or dropping out of a course with high levels of accuracy (e.g. Bahadır, 2016 ).

In their recent report, Baker and Smith ( 2019 ) approach educational AI tools from three different perspectives; a) learner-facing, b) teacher-facing, and c) system-facing AIEd. Learner-facing AI tools are software that students use to learn a subject matter, i.e. adaptive or personalised learning management systems or ITS. Teacher-facing systems are used to support the teacher and reduce his or her workload by automating tasks such as administration, assessment, feedback and plagiarism detection. AIEd tools also provide insight into the learning progress of students so that the teacher can proactively offer support and guidance where needed. System-facing AIEd are tools that provide information for administrators and managers on the institutional level, for example to monitor attrition patterns across faculties or colleges.

In the context of higher education, we use the concept of the student life-cycle (see Reid, 1995 ) as a framework to describe the various AI based services on the broader institutional and administrative level, as well as for supporting the academic teaching and learning process in the narrower sense.

The purpose of a systematic review is to answer specific questions, based on an explicit, systematic and replicable search strategy, with inclusion and exclusion criteria identifying studies to be included or excluded (Gough, Oliver & Thomas, 2017 ). Data is then coded and extracted from included studies, in order to synthesise findings and to shine light on their application in practice, as well as on gaps or contradictions. This contribution maps 146 articles on the topic of artificial intelligence in higher education.

Search strategy

The initial search string (see Table  1 ) and criteria (see Table  2 ) for this systematic review included peer-reviewed articles in English, reporting on artificial intelligence within education at any level, and indexed in three international databases; EBSCO Education Source, Web of Science and Scopus (covering titles, abstracts, and keywords). Whilst there are concerns about peer-review processes within the scientific community (e.g., Smith, 2006 ), articles in this review were limited to those published in peer-reviewed journals, due to their general trustworthiness in academia and the rigorous review processes undertaken (Nicholas et al., 2015 ). The search was undertaken in November 2018, with an initial 2656 records identified.

After duplicates were removed, it was decided to limit articles to those published during or after 2007, as this was the year that iPhone’s Siri was introduced; an algorithm-based personal assistant, started as an artificial intelligence project funded by the US Defense Advanced Research Projects Agency (DARPA) in 2001, turned into a company that was acquired by Apple Inc. It was also decided that the corpus would be limited to articles discussing applications of artificial intelligence in higher education only.

Screening and inter-rater reliability

The screening of 1549 titles and abstracts was carried out by a team of three coders and at this first screening stage, there was a requirement of sensitivity rather than specificity, i.e. papers were included rather than excluded. In order to reach consensus, the reasons for inclusion and exclusion for the first 80 articles were discussed at regular meetings. Twenty articles were randomly selected to evaluate the coding decisions of the three coders (A, B and C) to determine inter-rater reliability using Cohen’s kappa (κ) (Cohen, 1960 ), which is a coefficient for the degree of consistency among raters, based on the number of codes in the coding scheme (Neumann, 2007 , p. 326). Kappa values of .40–.60 are characterised as fair, .60 to .75 as good, and over .75 as excellent (Bakeman & Gottman, 1997 ; Fleiss, 1981 ). Coding consistency for inclusion or exclusion of articles between rater A and B was κ = .79, between rater A and C it was κ = .89, and between rater B and C it was κ = .69 (median = .79). Therefore, inter-rater reliability can be considered as excellent for the coding of inclusion and exclusion criteria.

After initial screening, 332 potential articles remained for screening on full text (see Fig.  1 ). However, 41 articles could not be retrieved, either through the library order scheme or by contacting authors. Therefore, 291 articles were retrieved, screened and coded, and following the exclusion of 149 papers, 146 articles remained for synthesis. Footnote 5

PRISMA diagram (slightly modified after Brunton & Thomas, 2012 , p. 86; Moher, Liberati, Tetzlaff, & Altman, 2009 , p. 8)

Coding, data extraction and analysis

In order to extract the data, all articles were uploaded into systematic review software EPPI Reviewer Footnote 6 and a coding system was developed. Codes included article information (year of publication, journal name, countries of authorship, discipline of first author), study design and execution (empirical or descriptive, educational setting) and how artificial intelligence was used (applications in the student life cycle, specific applications and methods). Articles were also coded on whether challenges and benefits of AI were present, and whether AI was defined. Descriptive data analysis was carried out with the statistics software R using the tidyr package (Wickham & Grolemund, 2016 ).

Limitations

Whilst this systematic review was undertaken as rigorously as possible, each review is limited by its search strategy. Although the three educational research databases chosen are large and international in scope, by applying the criteria of peer-reviewed articles published only in English or Spanish, research published on AI in other languages were not included in this review. This also applies to research in conference proceedings, book chapters or grey literature, or those articles not published in journals that are indexed in the three databases searched. In addition, although Spanish peer-reviewed articles were added according to inclusion criteria, no specific search string in the language was included, which narrows down the possibility of including Spanish papers that were not indexed with the chosen keywords. Future research could consider using a larger number of databases, publication types and publication languages, in order to widen the scope of the review. However, serious consideration would then need to be given to project resources and the manageability of the review (see Authors, in press).

Journals, authorship patterns and methods

Articles per year.

There was a noticeable increase in the papers published from 2007 onwards. The number of included articles grew from six in 2007 to 23 in 2018 (see Fig.  2 ).

Number of included articles per year ( n  = 146)

The papers included in the sample were published in 104 different journals. The greatest number of articles were published in the International Journal of Artificial Intelligence in Education ( n  = 11) , followed by Computers & Education ( n  = 8) , and the International Journal of Emerging Technologies in Learning ( n  = 5) . Table  3 lists 19 journals that published at least two articles on AI in higher education from 2007 to 2018.

For the geographical distribution analysis of articles, the country of origin of the first author was taken into consideration ( n  = 38 countries). Table 4 shows 19 countries that contributed at least two papers, and it reveals that 50% of all articles come from only four countries: USA, China, Taiwan, and Turkey.

Author affiliations

Again, the affiliation of the first author was taken into consideration (see Table 5 ). Researchers working in departments of Computer Science contributed by far the greatest number of papers ( n  = 61) followed by Science, Technology, Engineering and Mathematics (STEM) departments ( n  = 29). Only nine first authors came from an Education department, some reported dual affiliation with Education and Computer Science ( n  = 2), Education and Psychology ( n  = 1), or Education and STEM ( n  = 1).

Thus, 13 papers (8.9%) were written by first authors with an Education background. It is noticeable that three of them were contributed by researchers from the Teachers College at Columbia University, New York, USA (Baker, 2016 ; Paquette, Lebeau, Beaulieu, & Mayers, 2015 ; Perin & Lauterbach, 2018 ) – and they were all published in the same journal, i.e. the International Journal of Artificial Intelligence in Education .

Thirty studies (20.5%) were coded as being theoretical or descriptive in nature. The vast majority of studies (73.3%) applied quantitative methods, whilst only one (0.7%) was qualitative in nature and eight (5.5%) followed a mixed-methods approach. The purpose of the qualitative study, involving interviews with ESL students, was to explore the nature of written feedback coming from an automated essay scoring system compared to a human teacher (Dikli, 2010 ). In many cases, authors employed quasi-experimental methods, being an intentional sample divided into the experimental group, where an AI application (e.g. an intelligent tutoring system) was applied, and the control group without the intervention, followed by pre- and posttest (e.g. Adamson, Dyke, Jang, & Rosé, 2014 ).

Understanding of AI and critical reflection of challenges and risks

There are many different types and levels of AI mentioned in the articles, however only five out of 146 included articles (3.4%) provide an explicit definition of the term “Artificial Intelligence”. The main characteristics of AI, described in all five studies, are the parallels between the human brain and artificial intelligence. The authors conceptualise AI as intelligent computer systems or intelligent agents with human features, such as the ability to memorise knowledge, to perceive and manipulate their environment in a similar way as humans, and to understand human natural language (see Huang, 2018 ; Lodhi, Mishra, Jain, & Bajaj, 2018 ; Welham, 2008 ). Dodigovic ( 2007 ) defines AI in her article as follows (p. 100):

Artificial intelligence (AI) is a term referring to machines which emulate the behaviour of intelligent beings [ … ] AI is an interdisciplinary area of knowledge and research, whose aim is to understand how the human mind works and how to apply the same principles in technology design. In language learning and teaching tasks, AI can be used to emulate the behaviour of a teacher or a learner [ … ] . (p. 100)

Dodigovic is the only author who gives a definition of AI, and comes from an Arts, Humanities and Social Science department, taking into account aspects of AI and intelligent tutors in second language learning.

A stunningly low number of authors, only two out of 146 articles (1.4%), critically reflect upon ethical implications, challenges and risks of applying AI in education. Li ( 2007 ) deals with privacy concerns in his article about intelligent agent supported online learning:

Privacy is also an important concern in applying agent-based personalised education. As discussed above, agents can autonomously learn many of students’ personal information, like learning style and learning capability. In fact, personal information is private. Many students do not want others to know their private information, such as learning styles and/or capabilities. Students might show concern over possible discrimination from instructors in reference to learning performance due to special learning needs. Therefore, the privacy issue must be resolved before applying agent-based personalised teaching and learning technologies. (p. 327)

Another challenge of applying AI is mentioned by Welham ( 2008 , p. 295) concerning the costs and time involved in developing and introducing AI-based methods that many public educational institutions cannot afford.

AI applications in higher education

As mentioned before, we used the concept of the student life-cycle (see Reid, 1995 ) as a framework to describe the various AI based services at the institutional and administrative level (e.g. admission, counselling, library services), as well as at the academic support level for teaching and learning (e.g. assessment, feedback, tutoring). Ninety-two studies (63.0%) were coded as relating to academic support services and 48 (32.8%) as administrative and institutional services; six studies (4.1%) covered both levels. The majority of studies addressed undergraduate students ( n  = 91, 62.3%) compared to 11 (7.5%) focussing on postgraduate students, and another 44 (30.1%) that did not specify the study level.

The iterative coding process led to the following four areas of AI applications with 17 sub-categories, covered in the publications: a) adaptive systems and personalisation, b) assessment and evaluation, c) profiling and prediction, and d) intelligent tutoring systems. Some studies addressed AI applications in more than one area (see Table  6 ).

The nature and scope of the various AI applications in higher education will be described along the lines of these four application categories in the following synthesis.

Profiling and prediction

The basis for many AI applications are learner models or profiles that allow prediction, for example of the likelihood of a student dropping out of a course or being admitted to a programme, in order to offer timely support or to provide feedback and guidance in content related matters throughout the learning process. Classification, modelling and prediction are an essential part of educational data mining (Phani Krishna, Mani Kumar, & Aruna Sri, 2018 ).

Most of the articles (55.2%, n  = 32) address issues related to the institutional and administrative level, many (36.2%, n  = 21) are related to academic teaching and learning at the course level, and five (8.6%) are concerned with both levels. Articles dealing with profiling and prediction were classified into three sub-categories; admission decisions and course scheduling ( n  = 7), drop-out and retention ( n  = 23), and student models and academic achievement ( n  = 27). One study that does not fall into any of these categories is the study by Ge and Xie ( 2015 ), which is concerned with forecasting the costs of a Chinese university to support management decisions based on an artificial neural network.

All of the 58 studies in this area applied machine learning methods, to recognise and classify patterns, and to model student profiles to make predictions. Thus, they are all quantitative in nature. Many studies applied several machine learning algorithms (e.g. ANN, SVM, RF, NB; see Table  7 ) Footnote 7 and compared their overall prediction accuracy with conventional logistic regression. Table 7 shows that machine learning methods outperformed logistic regression in all studies in terms of their classification accuracy in percent. To evaluate the performance of classifiers, the F1-score can also be used, which takes into account the number of positive instances correctly classified as positive, the number of negative instances incorrectly classified as positive, and the number of positive instances incorrectly classified as negative (Umer et al., 2017 ; for a brief overview of measures of diagnostic accuracy, see Šimundić, 2009 ). The F1-score ranges between 0 and 1 with its best value at 1 (perfect precision and recall). Yoo and Kim ( 2014 ) reported high F1-scores of 0.848, 0.911, and 0.914 for J48, NB, and SVM, in a study to predict student’s group project performance from online discussion participation.

Admission decisions and course scheduling

Chen and Do ( 2014 ) point out that “the accurate prediction of students’ academic performance is of importance for making admission decisions as well as providing better educational services” (p. 18). Four studies aimed to predict whether or not a prospective student would be admitted to university. For example, Acikkar and Akay ( 2009 ) selected candidates for a School of Physical Education and Sports in Turkey based on a physical ability test, their scores in the National Selection and Placement Examination, and their graduation grade point average (GPA). They used the support vector machine (SVM) technique to classify the students and where able to predict admission decisions on a level of accuracy of 97.17% in 2006 and 90.51% in 2007. SVM was also applied by Andris, Cowen, and Wittenbach ( 2013 ) to find spatial patterns that might favour prospective college students from certain geographic regions in the USA. Feng, Zhou, and Liu ( 2011 ) analysed enrolment data from 25 Chinese provinces as the training data to predict registration rates in other provinces using an artificial neural network (ANN) model. Machine learning methods and ANN are also used to predict student course selection behaviour to support course planning. Kardan, Sadeghi, Ghidary, and Sani ( 2013 ) investigated factors influencing student course selection, such as course and instructor characteristics, workload, mode of delivery and examination time, to develop a model to predict course selection with an ANN in two Computer Engineering and Information Technology Masters programs. In another paper from the same author team, a decision support system for course offerings was proposed (Kardan & Sadeghi, 2013 ). Overall, the research shows that admission decisions can be predicted at high levels of accuracy, so that an AI solution could relieves the administrative staff and allows them to focus on the more difficult cases.

Drop-out and retention

Studies pertaining to drop-out and retention are intended to develop early warning systems to detect at-risk students in their first year (e.g., Alkhasawneh & Hargraves, 2014 ; Aluko, Adenuga, Kukoyi, Soyingbe, & Oyedeji, 2016 ; Hoffait & Schyns, 2017 ; Howard, Meehan, & Parnell, 2018 ) or to predict the attrition of undergraduate students in general (e.g., Oztekin, 2016 ; Raju & Schumacker, 2015 ). Delen ( 2011 ) used institutional data from 25,224 students enrolled as Freshmen in an American university over 8 years. In this study, three classification techniques were used to predict dropout: ANN, decision trees (DT) and logistic regression. The data contained variables related to students’ demographic, academic, and financial characteristics (e.g. age, sex, ethnicity, GPA, TOEFL score, financial aid, student loan, etc.). Based on a 10-fold cross validation, Delen ( 2011 ) found that the ANN model worked best with an accuracy rate of 81.19% (see Table 7 ) and he concluded that the most important predictors of student drop-out are related to the student’s past and present academic achievement, and whether they receive financial support. Sultana, Khan, and Abbas ( 2017 , p. 107) discussed the impact of cognitive and non-cognitive features of students for predicting academic performance of undergraduate engineering students. In contrast to many other studies, they focused on non-cognitive variables to improve prediction accuracy, i.e. time management, self-concept, self-appraisal, leadership, and community support.

Student models and academic achievement

Many more studies are concerned with profiling students and modelling learning behaviour to predict their academic achievements at the course level. Hussain et al. ( 2018 ) applied several machine learning algorithms to analyse student behavioural data from the virtual learning environment at the Open University UK, in order to predict student engagement, which is of particular importance at a large scale distance teaching university, where it is not possible to engage the majority of students in face-to-face sessions. The authors aim to develop an intelligent predictive system that enables instructors to automatically identify low-engaged students and then to make an intervention. Spikol, Ruffaldi, Dabisias, and Cukurova ( 2018 ) used face and hand tracking in workshops with engineering students to estimate success in project-based learning. They concluded that results generated from multimodal data can be used to inform teachers about key features of project-based learning activities. Blikstein et al. ( 2014 ) investigated patterns of how undergraduate students learn computer programming, based on over 150,000 code transcripts that the students created in software development projects. They found that their model, based on the process of programming, had better predictive power than the midterm grades. Another example is the study of Babić ( 2017 ), who developed a model to predict student academic motivation based on their behaviour in an online learning environment.

The research on student models is an important foundation for the design of intelligent tutoring systems and adaptive learning environments.

• Intelligent tutoring systems

All of the studies investigating intelligent tutoring systems (ITS) ( n  = 29) are only concerned with the teaching and learning level, except for one that is contextualised at the institutional and administrative level. The latter presents StuA , an interactive and intelligent student assistant that helps newcomers in a college by answering queries related to faculty members, examinations, extra curriculum activities, library services, etc. (Lodhi et al., 2018 ).

The most common terms for referring to ITS described in the studies are intelligent (online) tutors or intelligent tutoring systems (e.g., in Dodigovic, 2007 ; Miwa, Terai, Kanzaki, & Nakaike, 2014 ), although they are also identified often as intelligent (software) agents (e.g., Schiaffino, Garcia, & Amandi, 2008 ), or intelligent assistants (e.g., in Casamayor, Amandi, & Campo, 2009 ; Jeschike, Jeschke, Pfeiffer, Reinhard, & Richter, 2007 ). According to Welham ( 2008 ), the first ITS reported was the SCHOLAR system, launched in 1970, which allowed the reciprocal exchange of questions between teacher and student, but not holding a continuous conversation.

Huang and Chen ( 2016 , p. 341) describe the different models that are usually integrated in ITS: the student model (e.g. information about the student’s knowledge level, cognitive ability, learning motivation, learning styles), the teacher model (e.g. analysis of the current state of students, select teaching strategies and methods, provide help and guidance), the domain model (knowledge representation of both students and teachers) and the diagnosis model (evaluation of errors and defects based on domain model).

The implementation and validation of the ITS presented in the studies usually took place over short-term periods (a course or a semester) and no longitudinal studies were identified, except for the study by Jackson and Cossitt ( 2015 ). On the other hand, most of the studies showed (sometimes slightly) positive / satisfactory preliminary results regarding the performance of the ITS, but they did not take into account the novelty effect that a new technological development could have in an educational context. One study presented negative results regarding the type of support that the ITS provided (Adamson et al., 2014 ), which could have been more useful if it was more adjusted to the type of (in this case, more advanced) learners.

Overall, more research is needed on the effectiveness of ITS. The last meta-analysis of 39 ITS studies was published over 5 years ago: Steenbergen-Hu and Cooper ( 2014 ) found that ITS had a moderate effect of students’ learning, and that ITS were less effective that human tutoring, but ITS outperformed all other instruction methods (such as traditional classroom instruction, reading printed or digital text, or homework assignments).

The studies addressing various ITS functions were classified as follows: teaching course content ( n  = 12), diagnosing strengths or gaps in students’ knowledge and providing automated feedback ( n  = 7), curating learning materials based on students’ needs ( n  = 3), and facilitating collaboration between learners ( n  = 2).

Teaching course content

Most of the studies ( n  = 4) within this group focused on teaching Computer Science content (Dobre, 2014 ; Hooshyar, Ahmad, Yousefi, Yusop, & Horng, 2015 ; Howard, Jordan, di Eugenio, & Katz, 2017 ; Shen & Yang, 2011 ). Other studies included ITS teaching content for Mathematics (Miwa et al., 2014 ), Business Statistics and Accounting (Jackson & Cossitt, 2015 ; Palocsay & Stevens, 2008 ), Medicine (Payne et al., 2009 ) and writing and reading comprehension strategies for undergraduate Psychology students (Ray & Belden, 2007 ; Weston-Sementelli, Allen, & McNamara, 2018 ). Overall, these ITS focused on providing teaching content to students and, at the same time, supporting them by giving adaptive feedback and hints to solve questions related to the content, as well as detecting students’ difficulties/errors when working with the content or the exercises. This is made possible by monitoring students’ actions with the ITS.

In the study by Crown, Fuentes, Jones, Nambiar, and Crown ( 2011 ), a combination of teaching content through dialogue with a chatbot, that at the same time learns from this conversation - defined as a text-based conversational agent -, is described, which moves towards a more active, reflective and thinking student-centred learning approach. Duffy and Azevedo ( 2015 ) present an ITS called MetaTutor, which is designed to teach students about the human circulatory system, but it also puts emphasis on supporting students’ self-regulatory processes assisted by the features included in the MetaTutor system (a timer, a toolbar to interact with different learning strategies, and learning goals, amongst others).

Diagnosing strengths or gaps in student knowledge, and providing automated feedback

In most of the studies ( n  = 4) of this group, ITS are presented as a rather one-way communication from computer to student, concerning the gaps in students’ knowledge and the provision of feedback. Three examples in the field of STEM have been found: two of them where the virtual assistance is presented as a feature in virtual laboratories by tutoring feedback and supervising student behaviour (Duarte, Butz, Miller, & Mahalingam, 2008 ; Ramírez, Rico, Riofrío-Luzcando, Berrocal-Lobo, & Antonio, 2018 ), and the third one is a stand-alone ITS in the field of Computer Science (Paquette et al., 2015 ). One study presents an ITS of this kind in the field of second language learning (Dodigovic, 2007 ).

In two studies, the function of diagnosing mistakes and the provision of feedback is accomplished by a dialogue between the student and the computer. For example, with an interactive ubiquitous teaching robot that bases its speech on question recognition (Umarani, Raviram, & Wahidabanu, 2011 ), or with the tutoring system, based on a tutorial dialogue toolkit for introductory college Physics (Chi, VanLehn, Litman, & Jordan, 2011 ). The same tutorial dialogue toolkit (TuTalk) is the core of the peer dialogue agent presented by Howard et al. ( 2017 ), where the ITS engages in a one-on-one problem-solving peer interaction with a student and can interact verbally, graphically and in a process-oriented way, and engage in collaborative problem solving instead of tutoring. This last study could be considered as part of a new category regarding peer-agent collaboration.

Curating learning materials based on student needs

Two studies focused on this kind of ITS function (Jeschike et al., 2007 ; Schiaffino et al., 2008 ), and a third one mentions it in a more descriptive way as a feature of the detection system presented (Hall Jr & Ko, 2008 ). Schiaffino et al. ( 2008 ) present eTeacher as a system for personalised assistance to e-learning students by observing their behaviour in the course and generating a student’s profile. This enables the system to provide specific recommendations regarding the type of reading material and exercises done, as well as personalised courses of action. Jeschike et al. ( 2007 ) refers to an intelligent assistant contextualised in a virtual laboratory of statistical mechanics, where it presents exercises and the evaluation of the learners’ input to content, and interactive course material that adapts to the learner.

Facilitating collaboration between learners

Within this group we can identify only two studies: one focusing on supporting online collaborative learning discussions by using academically productive talk moves (Adamson et al., 2014 ); and the second one, on facilitating collaborative writing by providing automated feedback, generated automatic questions, and the analysis of the process (Calvo, O’Rourke, Jones, Yacef, & Reimann, 2011 ). Given the opportunities that the applications described in these studies afford for supporting collaboration among students, more research in this area would be desireable.

The teachers’ perspective

As mentioned above, Baker and Smith ( 2019 , p.12) distinguish between student and teacher-facing AI. However, only two included articles in ITS focus on the teacher’s perspective. Casamayor et al. ( 2009 ) focus on assisting teachers with the supervision and detection of conflictive cases in collaborative learning. In this study, the intelligent assistant provides the teachers with a summary of the individual progress of each group member and the type of participation each of them have had in their work groups, notification alerts derived from the detection of conflict situations, and information about the learning style of each student-logging interactions, so that the teachers can intervene when they consider it convenient. The other study put the emphasis on the ITS sharing teachers’ tutoring tasks by providing immediate feedback (automating tasks), and leaving the teachers the role of providing new hints and the correct solution to the tasks (Chou, Huang, & Lin, 2011 ). The study of Chi et al. ( 2011 ) also mentions the ITS purpose to share teacher’s tutoring tasks. The main aim in any of these cases is to reduce teacher’s workload. Furthermore, many of the learner-facing studies deal with the teacher-facing functions too, although they do not put emphasis on the teacher’s perspective.

Assessment and evaluation

Assessment and evaluation studies also largely focused on the level of teaching and learning (86%, n  = 31), although five studies described applications at the institutional level. In order to gain an overview of student opinion about online and distance learning at their institution, academics at Anadolu University (Ozturk, Cicek, & Ergul, 2017 ) used sentiment analysis to analyse mentions by students on Twitter, using Twitter API Twython and terms relating to the system. This analysis of publicly accessible data, allowed researchers insight into student opinion, which otherwise may not have been accessible through their institutional LMS, and which can inform improvements to the system. Two studies used AI to evaluate student Prior Learning and Recognition (PLAR); Kalz et al. ( 2008 ) used Latent Semantic Analysis and ePortfolios to inform personalised learning pathways for students, and Biletska, Biletskiy, Li, and Vovk ( 2010 ) used semantic web technologies to convert student credentials from different institutions, which could also provide information from course descriptions and topics, to allow for easier granting of credit. The final article at the institutional level (Sanchez et al., 2016 ) used an algorithm to match students to professional competencies and capabilities required by companies, in order to ensure alignment between courses and industry needs.

Overall, the studies show that AI applications can perform assessment and evaluation tasks at very high accuracy and efficiency levels. However, due to the need to calibrate and train the systems (supervised machine learning), they are more applicable to courses or programs with large student numbers.

Articles focusing on assessment and evaluation applications of AI at the teaching and learning level, were classified into four sub-categories; automated grading ( n  = 13), feedback ( n  = 8), evaluation of student understanding, engagement and academic integrity ( n  = 5), and evaluation of teaching ( n  = 5).

Automated grading

Articles that utilised automated grading, or Automated Essay Scoring (AES) systems, came from a range of disciplines (e.g. Biology, Medicine, Business Studies, English as a Second Language), but were mostly focused on its use in undergraduate courses ( n  = 10), including those with low reading and writing ability (Perin & Lauterbach, 2018 ). Gierl, Latifi, Lai, Boulais, and Champlain’s ( 2014 ) use of open source Java software LightSIDE to grade postgraduate medical student essays resulted in an agreement between the computer classification and human raters between 94.6% and 98.2%, which could enable reducing cost and the time associated with employing multiple human assessors for large-scale assessments (Barker, 2011 ; McNamara, Crossley, Roscoe, Allen, & Dai, 2015 ). However, they stressed that not all writing genres may be appropriate for AES and that it would be impractical to use in most small classrooms, due to the need to calibrate the system with a large number of pre-scored assessments. The benefits of using algorithms that find patterns in text responses, however, has been found to lead to encouraging more revisions by students (Ma & Slater, 2015 ) and to move away from merely measuring student knowledge and abilities by multiple choice tests (Nehm, Ha, & Mayfield, 2012 ). Continuing issues persist, however, in the quality of feedback provided by AES (Dikli, 2010 ), with Barker ( 2011 ) finding that the more detailed the feedback provided was, the more likely students were to question their grades, and a question was raised over the benefits of this feedback for beginning language students (Aluthman, 2016 ).

Articles concerned with feedback included a range of student-facing tools, including intelligent agents that provide students with prompts or guidance when they are confused or stalled in their work (Huang, Chen, Luo, Chen, & Chuang, 2008 ), software to alert trainee pilots when they are losing situation awareness whilst flying (Thatcher, 2014 ), and machine learning techniques with lexical features to generate automatic feedback and assist in improving student writing (Chodorow, Gamon, & Tetreault, 2010 ; Garcia-Gorrostieta, Lopez-Lopez, & Gonzalez-Lopez, 2018 ; Quixal & Meurers, 2016 ), which can help reduce students cognitive overload (Yang, Wong, & Yeh, 2009 ). The automated feedback system based on adaptive testing reported by Barker ( 2010 ), for example, not only determines the most appropriate individual answers according to Bloom’s cognitive levels, but also recommends additional materials and challenges.

Evaluation of student understanding, engagement and academic integrity

Three articles reported on student-facing tools that evaluate student understanding of concepts (Jain, Gurupur, Schroeder, & Faulkenberry, 2014 ; Zhu, Marquez, & Yoo, 2015 ) and provide personalised assistance (Samarakou, Fylladitakis, Früh, Hatziapostolou, & Gelegenis, 2015 ). Hussain et al. ( 2018 ) used machine learning algorithms to evaluate student engagement in a social science course at the Open University, including final results, assessment scores and the number of clicks that students make in the VLE, which can alert instructors to the need for intervention, and Amigud, Arnedo-Moreno, Daradoumis, and Guerrero-Roldan ( 2017 ) used machine learning algorithms to check academic integrity, by assessing the likelihood of student work being similar to their other work. With a mean accuracy of 93%, this opens up possibilities of reducing the need for invigilators or to access student accounts, thereby reducing concerns surrounding privacy.

Evaluation of teaching

Four studies used data mining algorithms to evaluate lecturer performance through course evaluations (Agaoglu, 2016 ; Ahmad & Rashid, 2016 ; DeCarlo & Rizk, 2010 ; Gutierrez, Canul-Reich, Ochoa Zezzatti, Margain, & Ponce, 2018 ), with Agaoglu ( 2016 ) finding, through using four different classification techniques, that many questions in the evaluation questionnaire were irrelevant. The application of an algorithm to evaluate the impact of teaching methods in a differential equations class, found that online homework with immediate feedback was more effective than clickers (Duzhin & Gustafsson, 2018 ). The study also found that, whilst previous exam results are generally good predictors for future exam results, they say very little about students’ expected performance in project-based tasks.

Adaptive systems and personalisation

Most of the studies on adaptive systems (85%, n  = 23) are situated at the teaching and learning level, with four cases considering the institutional and administrative level. Two studies explored undergraduate students’ academic advising (Alfarsi, Omar, & Alsinani, 2017 ; Feghali, Zbib, & Hallal, 2011 ), and Nguyen et al. ( 2018 ) focused on AI to support university career services. Ng, Wong, Lee, and Lee ( 2011 ) reported on the development of an agent-based distance LMS, designed to manage resources, support decision making and institutional policy, and assist with managing undergraduate student study flow (e.g. intake, exam and course management), by giving users access to data across disciplines, rather than just individual faculty areas.

There does not seem to be agreement within the studies on a common term for adaptive systems, and that is probably due to the diverse functions they carry out, which also supports the classification of studies. Some of those terms coincide in part with the ones used for ITS, e.g. intelligent agents (Li, 2007 ; Ng et al., 2011 ). The most general terms used are intelligent e-learning system (Kose & Arslan, 2016 ), adaptive web-based learning system (Lo, Chan, & Yeh, 2012 ), or intelligent teaching system (Yuanyuan & Yajuan, 2014 ). As in ITS, most of the studies either describe the system or include a pilot study but no longer-term results are reported. Results from these pilot studies are usually reported as positive, except in Vlugter, Knott, McDonald, and Hall ( 2009 ), where the experimental group that used the dialogue-based computer assisted language-system scored lower than the control group in the delayed post-tests.

The 23 studies focused on teaching and learning can be classified into five sub-categories; teaching course content ( n  = 7), recommending/providing personalised content ( n  = 5), supporting teachers in learning and teaching design ( n  = 3), using academic data to monitor and guide students ( n  = 2), and supporting representation of knowledge using concept maps ( n  = 2). However, some studies were difficult to classify, due to their specific and unique functions; helping to organise online learning groups with similar interests (Yang, Wang, Shen, & Han, 2007 ), supporting business decisions through simulation (Ben-Zvi, 2012 ), or supporting changes in attitude and behaviour for patients with Anorexia Nervosa, through embodied conversational agents (Sebastian & Richards, 2017 ). Aparicio et al. ( 2018 ) present a study where no adaptive system application was analysed, rather students’ perceptions of the use of information systems in education in general - and biomedical education in particular - were analysed, including intelligent information access systems .

The disciplines that are taught through adaptive systems are diverse, including environmental education (Huang, 2018 ), animation design (Yuanyuan & Yajuan, 2014 ), language learning (Jia, 2009 ; Vlugter et al., 2009 ), Computer Science (Iglesias, Martinez, Aler, & Fernandez, 2009 ) and Biology (Chaudhri et al., 2013 ). Walsh, Tamjidul, and Williams ( 2017 ), however, present an adaptive system based on machine learning-human machine learning symbiosis from a descriptive perspective, without specifying any discipline.

Recommending/providing personalised content

This group refers to adaptive systems that deliver customised content, materials and exercises according to students’ behaviour profiling in Business and Administration studies (Hall Jr & Ko, 2008 ) and Computer Science (Kose & Arslan, 2016 ; Lo et al., 2012 ). On the other hand, Tai, Wu, and Li ( 2008 ) present an e-learning recommendation system for online students to help them choose among courses, and Torres-Díaz, Infante Moro, and Valdiviezo Díaz ( 2014 ) emphasise the usefulness of (adaptive) recommendation systems in MOOCs to suggest actions, new items and users, according to students’ personal preferences.

Supporting teachers in learning and teaching design

In this group, three studies were identified. One study puts the emphasis on a hybrid recommender system of pedagogical patterns, to help teachers define their teaching strategies, according to the context of a specific class (Cobos et al., 2013 ), and another study presents a description of a metadata-based model to implement automatic learning designs that can solve detected problems (Camacho & Moreno, 2007 ). Li’s ( 2007 ) descriptive study argues that intelligent agents save time for online instructors, by leaving the most repetitive tasks to the systems, so that they can focus more on creative work.

Using academic data to monitor and guide students

The adaptive systems within this category focus on the extraction of student academic information to perform diagnostic tasks, and help tutors to offer a more proactive personal guidance (Rovira, Puertas, & Igual, 2017 ); or, in addition to that task, include performance evaluation and personalised assistance and feedback, such as the Learner Diagnosis, Assistance, and Evaluation System based on AI (StuDiAsE) for engineering learners (Samarakou et al., 2015 ).

Supporting representation of knowledge in concept maps

To help build students’ self-awareness of conceptual structures, concept maps can be quite useful. In the two studies of this group, an expert system was included, e.g. in order to accommodate selected peer ideas in the integrated concept maps and allow teachers to flexibly determine in which ways the selected concept maps are to be merged ( ICMSys ) (Kao, Chen, & Sun, 2010 ), or to help English as a Foreign Language college students to develop their reading comprehension through mental maps of referential identification (Yang et al., 2009 ). This latter system also includes system-guided instruction, practice and feedback.

Conclusions and implications for further educational research

In this paper, we have explored the field of AIEd research in terms of authorship and publication patterns. It is evident that US-American, Chinese, Taiwanese and Turkish colleagues (accounting for 50% of the publications as first authors) from Computer Science and STEM departments (62%) dominate the field. The leading journals are the International Journal of Artificial Intelligence in Education , Computers & Education , and the International Journal of Emerging Technologies in Learning .

More importantly, this study has provided an overview of the vast array of potential AI applications in higher education to support students, faculty members, and administrators. They were described in four broad areas (profiling and prediction, intelligent tutoring systems, assessment and evaluation, and adaptive systems and personalisation) with 17 sub-categories. This structure, which was derived from the systematic review, contributes to the understanding and conceptualisation of AIEd practice and research.

On the other hand, the lack of longitudinal studies and the substantial presence of descriptive and pilot studies from the technological perspective, as well as the prevalence of quantitative methods - especially quasi-experimental methods - in empirical studies, shows that there is still substantial room for educators to aim at innovative and meaningful research and practice with AIEd that could have learning impact within higher education, e.g. adopting design-based approaches (Easterday, Rees Lewis, & Gerber, 2018 ). A recent systematic literature review on personalisation in educational technology coincided with the predominance of experiences in technological developments, which also often used quantitative methods (Bartolomé, Castañeda, & Adell, 2018 ). Misiejuk and Wasson ( 2017 , p. 61) noted in their systematic review on Learning Analytics that “there are very few implementation studies and impact studies” (p. 61), which is also similar to the findings in the present article.

The full consequences of AI development cannot yet be foreseen today, but it seems likely that AI applications will be a top educational technology issue for the next 20 years. AI-based tools and services have a high potential to support students, faculty members and administrators throughout the student lifecycle. The applications that are described in this article provide enormous pedagogical opportunities for the design of intelligent student support systems, and for scaffolding student learning in adaptive and personalized learning environments. This applies in particular to large higher education institutions (such as open and distance teaching universities), where AIEd might help to overcome the dilemma of providing access to higher education for very large numbers of students (mass higher education). On the other hand, it might also help them to offer flexible, but also interactive and personalized learning opportunities, for example by relieving teachers from burdens, such as grading hundreds or even thousands of assignments, so that they can focus on their real task: empathic human teaching.

It is crucial to emphasise that educational technology is not (only) about technology – it is the pedagogical, ethical, social, cultural and economic dimensions of AIEd we should be concerned about. Selwyn ( 2016 , p. 106) writes:

The danger, of course, lies in seeing data and coding as an absolute rather than relative source of guidance and support. Education is far too complex to be reduced solely to data analysis and algorithms. As with digital technologies in general, digital data do not offer a neat technical fix to education dilemmas – no matter how compelling the output might be.

We should not strive for what is technically possible, but always ask ourselves what makes pedagogical sense. In China, systems are already being used to monitor student participation and expressions via face recognition in classrooms (so called Intelligent Classroom Behavior Management System, Smart Campus Footnote 8 ) and display them to the teacher on a dashboard. This is an example of educational surveillance, and it is highly questionable whether such systems provide real added value for a good teacher who should be able to capture the dynamics in a learning group (online and in an on-campus setting) and respond empathically and in a pedagogically meaningful way. In this sense, it is crucial to adopt an ethics of care (Prinsloo, 2017 ) to start thinking on how we are exploring the potential of algorithmic decision-making systems that are embedded in AIEd applications. Furthermore, we should also always remember that AI systems “first and foremost, require control by humans. Even the smartest AI systems can make very stupid mistakes. […] AI Systems are only as smart as the date used to train them” (Kaplan & Haenlein, 2019 , p. 25). Some critical voices in educational technology remind us that we should go beyond the tools, and talk again about learning and pedagogy, as well as acknowledging the human aspects of digital technology use in education (Castañeda & Selwyn, 2018 ). The new UNESCO report on challenges and opportunities of AIEd for sustainable development deals with various areas, all of which have an important pedagogical, social and ethical dimension, e.g. ensuring inclusion and equity in AIEd, preparing teachers for AI-powered education, developing quality and inclusive data systems, or ethics and transparency in data collection, use and dissemination (Pedró, Subosa, Rivas, & Valverde, 2019 ).

That being said, a stunning result of this review is the dramatic lack of critical reflection of the pedagogical and ethical implications as well as risks of implementing AI applications in higher education. Concerning ethical implications, privacy issues were also noted to be rarely addressed in empirical studies in a recent systematic review on Learning Analytics (Misiejuk & Wasson, 2017 ). More research is needed from educators and learning designers on how to integrate AI applications throughout the student lifecycle, to harness the enormous opportunities that they afford for creating intelligent learning and teaching systems. The low presence of authors affiliated with Education departments identified in our systematic review is evidence of the need for educational perspectives on these technological developments.

The lack of theory might be a syndrome within the field of educational technology in general. In a recent study, Hew, Lan, Tang, Jia, and Lo ( 2019 ) found that more than 40% of articles in three top educational technology journals were wholly a-theoretical. The systematic review by Bartolomé et al. ( 2018 ) also revealed this lack of explicit pedagogical perspectives in the studies analysed. The majority of research included in this systematic review is merely focused on analysing and finding patterns in data to develop models, and to make predictions that inform student and teacher facing applications, or to support administrative decisions using mathematical theories and machine learning methods that were developed decades ago (see Russel & Norvig, 2010 ). This kind of research is now possible through the growth of computing power and the vast availability of big digital student data. However, at this stage, there is very little evidence for the advancement of pedagogical and psychological learning theories related to AI driven educational technology. It is an important implication of this systematic review, that researchers are encouraged to be explicit about the theories that underpin empirical studies about the development and implementation of AIEd projects, in order to expand research to a broader level, helping us to understand the reasons and mechanisms behind this dynamic development that will have an enormous impact on higher education institutions in the various areas we have covered in this review.

Availability of data and materials

The datasets used and/or analysed during the current study (the bibliography of included studies) are available from the corresponding author upon request.

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Artificial intelligence in education: Addressing ethical challenges in K-12 settings

Selin akgun.

Michigan State University, East Lansing, MI USA

Christine Greenhow

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Artificial intelligence (AI) is a field of study that combines the applications of machine learning, algorithm productions, and natural language processing. Applications of AI transform the tools of education. AI has a variety of educational applications, such as personalized learning platforms to promote students’ learning, automated assessment systems to aid teachers, and facial recognition systems to generate insights about learners’ behaviors. Despite the potential benefits of AI to support students’ learning experiences and teachers’ practices, the ethical and societal drawbacks of these systems are rarely fully considered in K-12 educational contexts. The ethical challenges of AI in education must be identified and introduced to teachers and students. To address these issues, this paper (1) briefly defines AI through the concepts of machine learning and algorithms; (2) introduces applications of AI in educational settings and benefits of AI systems to support students’ learning processes; (3) describes ethical challenges and dilemmas of using AI in education; and (4) addresses the teaching and understanding of AI by providing recommended instructional resources from two providers—i.e., the Massachusetts Institute of Technology’s (MIT) Media Lab and Code.org. The article aims to help practitioners reap the benefits and navigate ethical challenges of integrating AI in K-12 classrooms, while also introducing instructional resources that teachers can use to advance K-12 students’ understanding of AI and ethics.

Introduction

“Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last, unless we learn how to avoid the risks.” — Stephen Hawking.

We may not think about artificial intelligence (AI) on a daily basis, but it is all around us, and we have been using it for years. When we are doing a Google search, reading our emails, getting a doctor’s appointment, asking for driving directions, or getting movie and music recommendations, we are constantly using the applications of AI and its assistance in our lives. This need for assistance and our dependence on AI systems has become even more apparent during the COVID-19 pandemic. The growing impact and dominance of AI systems reveals itself in healthcare, education, communications, transportation, agriculture, and more. It is almost impossible to live in a modern society without encountering applications powered by AI  [ 10 , 32 ].

Artificial intelligence (AI) can be defined briefly as the branch of computer science that deals with the simulation of intelligent behavior in computers and their capacity to mimic, and ideally improve, human behavior [ 43 ]. AI dominates the fields of science, engineering, and technology, but also is present in education through machine-learning systems and algorithm productions [ 43 ]. For instance, AI has a variety of algorithmic applications in education, such as personalized learning systems to promote students’ learning, automated assessment systems to support teachers in evaluating what students know, and facial recognition systems to provide insights about learners’ behaviors [ 49 ]. Besides these platforms, algorithm systems are prominent in education through different social media outlets, such as social network sites, microblogging systems, and mobile applications. Social media are increasingly integrated into K-12 education [ 7 ] and subordinate learners’ activities to intelligent algorithm systems [ 17 ]. Here, we use the American term “K–12 education” to refer to students’ education in kindergarten (K) (ages 5–6) through 12th grade (ages 17–18) in the United States, which is similar to primary and secondary education or pre-college level schooling in other countries. These AI systems can increase the capacity of K-12 educational systems and support the social and cognitive development of students and teachers [ 55 , 8 ]. More specifically, applications of AI can support instruction in mixed-ability classrooms; while personalized learning systems provide students with detailed and timely feedback about their writing products, automated assessment systems support teachers by freeing them from excessive workloads [ 26 , 42 ].

Despite the benefits of AI applications for education, they pose societal and ethical drawbacks. As the famous scientist, Stephen Hawking, pointed out that weighing these risks is vital for the future of humanity. Therefore, it is critical to take action toward addressing them. The biggest risks of integrating these algorithms in K-12 contexts are: (a) perpetuating existing systemic bias and discrimination, (b) perpetuating unfairness for students from mostly disadvantaged and marginalized groups, and (c) amplifying racism, sexism, xenophobia, and other forms of injustice and inequity [ 40 ]. These algorithms do not occur in a vacuum; rather, they shape and are shaped by ever-evolving cultural, social, institutional and political forces and structures [ 33 , 34 ]. As academics, scientists, and citizens, we have a responsibility to educate teachers and students to recognize the ethical challenges and implications of algorithm use. To create a future generation where an inclusive and diverse citizenry can participate in the development of the future of AI, we need to develop opportunities for K-12 students and teachers to learn about AI via AI- and ethics-based curricula and professional development [ 2 , 58 ]

Toward this end, the existing literature provides little guidance and contains a limited number of studies that focus on supporting K-12 students and teachers’ understanding of social, cultural, and ethical implications of AI [ 2 ]. Most studies reflect university students’ engagement with ethical ideas about algorithmic bias, but few addresses how to promote students’ understanding of AI and ethics in K-12 settings. Therefore, this article: (a) synthesizes ethical issues surrounding AI in education as identified in the educational literature, (b) reflects on different approaches and curriculum materials available for teaching students about AI and ethics (i.e., featuring materials from the MIT Media Lab and Code.org), and (c) articulates future directions for research and recommendations for practitioners seeking to navigate AI and ethics in K-12 settings.

Next, we briefly define the notion of artificial intelligence (AI) and its applications through machine-learning and algorithm systems. As educational and educational technology scholars working in the United States, and at the risk of oversimplifying, we provide only a brief definition of AI below, and recognize that definitions of AI are complex, multidimensional, and contested in the literature [ 9 , 16 , 38 ]; an in-depth discussion of these complexities, however, is beyond the scope of this paper. Second, we describe in more detail five applications of AI in education, outlining their potential benefits for educators and students. Third, we describe the ethical challenges they raise by posing the question: “how and in what ways do algorithms manipulate us?” Fourth, we explain how to support students’ learning about AI and ethics through different curriculum materials and teaching practices in K-12 settings. Our goal here is to provide strategies for practitioners to reap the benefits while navigating the ethical challenges. We acknowledge that in centering this work within U.S. education, we highlight certain ethical issues that educators in other parts of the world may see as less prominent. For example, the European Union (EU) has highlighted ethical concerns and implications of AI, emphasized privacy protection, surveillance, and non-discrimination as primary areas of interest, and provided guidelines on how trustworthy AI should be [ 3 , 15 , 23 ]. Finally, we reflect on future directions for educational and other research that could support K-12 teachers and students in reaping the benefits while mitigating the drawbacks of AI in education.

Definition and applications of artificial intelligence

The pursuit of creating intelligent machines that replicate human behavior has accelerated with the realization of artificial intelligence. With the latest advancements in computer science, a proliferation of definitions and explanations of what counts as AI systems has emerged. For instance, AI has been defined as “the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings” [ 49 ]. This particular definition highlights the mimicry of human behavior and consciousness. Furthermore, AI has been defined as “the combination of cognitive automation, machine learning, reasoning, hypothesis generation and analysis, natural language processing, and intentional algorithm mutation producing insights and analytics at or above human capability” [ 31 ]. This definition incorporates the different sub-fields of AI together and underlines their function while reaching at or above human capability.

Combining these definitions, artificial intelligence can be described as the technology that builds systems to think and act like humans with the ability of achieving goals . AI is mainly known through different applications and advanced computer programs, such as recommender systems (e.g., YouTube, Netflix), personal assistants (e.g., Apple’s Siri), facial recognition systems (e.g., Facebook’s face detection in photographs), and learning apps (e.g., Duolingo) [ 32 ]. To build on these programs, different sub-fields of AI have been used in a diverse range of applications. Evolutionary algorithms and machine learning are most relevant to AI in K-12 education.

Algorithms are the core elements of AI. The history of AI is closely connected to the development of sophisticated and evolutionary algorithms. An algorithm is a set of rules or instructions that is to be followed by computers in problem-solving operations to achieve an intended end goal. In essence, all computer programs are algorithms. They involve thousands of lines of codes which represent mathematical instructions that the computer follows to solve the intended problems (e.g., as computing numerical calculation, processing an image, and grammar-checking in an essay). AI algorithms are applied to fields that we might think of as essentially human behavior—such as speech and face recognition, visual perception, learning, and decision-making and learning. In that way, algorithms can provide instructions for almost any AI system and application we can conceive [ 27 ].

Machine learning

Machine learning is derived from statistical learning methods and uses data and algorithms to perform tasks which are typically performed by humans [ 43 ]. Machine learning is about making computers act or perform without being given any line-by-line step [ 29 ]. The working mechanism of machine learning is the learning model’s exposure to ample amounts of quality data [ 41 ]. Machine-learning algorithms first analyze the data to determine patterns and to build a model and then predict future values through these models. In other words, machine learning can be considered a three-step process. First, it analyzes and gathers the data, and then, it builds a model to excel for different tasks, and finally, it undertakes the action and produces the desired results successfully without human intervention [ 29 , 56 ]. The widely known AI applications such as recommender or facial recognition systems have all been made possible through the working principles of machine learning.

Benefits of AI applications in education

Personalized learning systems, automated assessments, facial recognition systems, chatbots (social media sites), and predictive analytics tools are being deployed increasingly in K-12 educational settings; they are powered by machine-learning systems and algorithms [ 29 ]. These applications of AI have shown promise to support teachers and students in various ways: (a) providing instruction in mixed-ability classrooms, (b) providing students with detailed and timely feedback on their writing products, (c) freeing teachers from the burden of possessing all knowledge and giving them more room to support their students while they are observing, discussing, and gathering information in their collaborative knowledge-building processes [ 26 , 50 ]. Below, we outline benefits of each of these educational applications in the K-12 setting before turning to a synthesis of their ethical challenges and drawbacks.

Personalized learning systems

Personalized learning systems, also known as adaptive learning platforms or intelligent tutoring systems, are one of the most common and valuable applications of AI to support students and teachers. They provide students access to different learning materials based on their individual learning needs and subjects [ 55 ]. For example, rather than practicing chemistry on a worksheet or reading a textbook, students may use an adaptive and interactive multimedia version of the course content [ 39 ]. Comparing students’ scores on researcher-developed or standardized tests, research shows that the instruction based on personalized learning systems resulted in higher test scores than traditional teacher-led instruction [ 36 ]. Microsoft’s recent report (2018) of over 2000 students and teachers from Singapore, the U.S., the UK, and Canada shows that AI supports students’ learning progressions. These platforms promise to identify gaps in students’ prior knowledge by accommodating learning tools and materials to support students’ growth. These systems generate models of learners using their knowledge and cognition; however, the existing platforms do not yet provide models for learners’ social, emotional, and motivational states [ 28 ]. Considering the shift to remote K-12 education during the COVID-19 pandemic, personalized learning systems offer a promising form of distance learning that could reshape K-12 instruction for the future [ 35 ].

Automated assessment systems

Automated assessment systems are becoming one of the most prominent and promising applications of machine learning in K-12 education [ 42 ]. These scoring algorithm systems are being developed to meet the need for scoring students’ writing, exams and assignments, and tasks usually performed by the teacher. Assessment algorithms can provide course support and management tools to lessen teachers’ workload, as well as extend their capacity and productivity. Ideally, these systems can provide levels of support to students, as their essays can be graded quickly [ 55 ]. Providers of the biggest open online courses such as Coursera and EdX have integrated automated scoring engines into their learning platforms to assess the writings of hundreds of students [ 42 ]. On the other hand, a tool called “Gradescope” has been used by over 500 universities to develop and streamline scoring and assessment [ 12 ]. By flagging the wrong answers and marking the correct ones, the tool supports instructors by eliminating their manual grading time and effort. Thus, automated assessment systems deal very differently with marking and giving feedback to essays compared to numeric assessments which analyze right or wrong answers on the test. Overall, these scoring systems have the potential to deal with the complexities of the teaching context and support students’ learning process by providing them with feedback and guidance to improve and revise their writing.

Facial recognition systems and predictive analytics

Facial recognition software is used to capture and monitor students’ facial expressions. These systems provide insights about students’ behaviors during learning processes and allow teachers to take action or intervene, which, in turn, helps teachers develop learner-centered practices and increase student’s engagement [ 55 ]. Predictive analytics algorithm systems are mainly used to identify and detect patterns about learners based on statistical analysis. For example, these analytics can be used to detect university students who are at risk of failing or not completing a course. Through these identifications, instructors can intervene and get students the help they need [ 55 ].

Social networking sites and chatbots

Social networking sites (SNSs) connect students and teachers through social media outlets. Researchers have emphasized the importance of using SNSs (such as Facebook) to expand learning opportunities beyond the classroom, monitor students’ well-being, and deepen student–teacher relations [ 5 ]. Different scholars have examined the role of social media in education, describing its impact on student and teacher learning and scholarly communication [ 6 ]. They point out that the integration of social media can foster students’ active learning, collaboration skills, and connections with communities beyond the classroom [ 6 ]. Chatbots also take place in social media outlets through different AI systems [ 21 ]. They are also known as dialogue systems or conversational agents [ 26 , 52 ]. Chatbots are helpful in terms of their ability to respond naturally with a conversational tone. For instance, a text-based chatbot system called “Pounce” was used at Georgia State University to help students through the registration and admission process, as well as financial aid and other administrative tasks [ 7 ].

In summary, applications of AI can positively impact students’ and teachers’ educational experiences and help them address instructional challenges and concerns. On the other hand, AI cannot be a substitute for human interaction [ 22 , 47 ]. Students have a wide range of learning styles and needs. Although AI can be a time-saving and cognitive aide for teachers, it is but one tool in the teachers’ toolkit. Therefore, it is critical for teachers and students to understand the limits, potential risks, and ethical drawbacks of AI applications in education if they are to reap the benefits of AI and minimize the costs [ 11 ].

Ethical concerns and potential risks of AI applications in education

The ethical challenges and risks posed by AI systems seemingly run counter to marketing efforts that present algorithms to the public as if they are objective and value-neutral tools. In essence, algorithms reflect the values of their builders who hold positions of power [ 26 ]. Whenever people create algorithms, they also create a set of data that represent society’s historical and systemic biases, which ultimately transform into algorithmic bias. Even though the bias is embedded into the algorithmic model with no explicit intention, we can see various gender and racial biases in different AI-based platforms [ 54 ].

Considering the different forms of bias and ethical challenges of AI applications in K-12 settings, we will focus on problems of privacy, surveillance, autonomy, bias, and discrimination (see Fig.  1 ). However, it is important to acknowledge that educators will have different ethical concerns and challenges depending on their students’ grade and age of development. Where strategies and resources are recommended, we indicate the age and/or grade level of student(s) they are targeting (Fig. ​ (Fig.2 2 ).

Potential ethical and societal risks of AI applications in education

Student work from the activity of “Youtube Redesign” (MIT Media Lab, AI and Ethics Curriculum, p.1, [ 45 ])

One of the biggest ethical issues surrounding the use of AI in K-12 education relates to the privacy concerns of students and teachers [ 47 , 49 , 54 ]. Privacy violations mainly occur as people expose an excessive amount of personal information in online platforms. Although existing legislation and standards exist to protect sensitive personal data, AI-based tech companies’ violations with respect to data access and security increase people’s privacy concerns [ 42 , 54 ]. To address these concerns, AI systems ask for users’ consent to access their personal data. Although consent requests are designed to be protective measures and to help alleviate privacy concerns, many individuals give their consent without knowing or considering the extent of the information (metadata) they are sharing, such as the language spoken, racial identity, biographical data, and location [ 49 ]. Such uninformed sharing in effect undermines human agency and privacy. In other words, people’s agency diminishes as AI systems reduce introspective and independent thought [ 55 ]. Relatedly, scholars have raised the ethical issue of forcing students and parents to use these algorithms as part of their education even if they explicitly agree to give up privacy [ 14 , 48 ]. They really have no choice if these systems are required by public schools.

Another ethical concern surrounding the use of AI in K-12 education is surveillance or tracking systems which gather detailed information about the actions and preferences of students and teachers. Through algorithms and machine-learning models, AI tracking systems not only necessitate monitoring of activities but also determine the future preferences and actions of their users [ 47 ]. Surveillance mechanisms can be embedded into AI’s predictive systems to foresee students’ learning performances, strengths, weaknesses, and learning patterns . For instance, research suggests that teachers who use social networking sites (SNSs) for pedagogical purposes encounter a number of problems, such as concerns in relation to boundaries of privacy, friendship authority, as well as responsibility and availability [ 5 ]. While monitoring and patrolling students’ actions might be considered part of a teacher’s responsibility and a pedagogical tool to intervene in dangerous online cases (such as cyber-bullying or exposure to sexual content), such actions can also be seen as surveillance systems which are problematic in terms of threatening students’ privacy. Monitoring and tracking students’ online conversations and actions also may limit their participation in the learning event and make them feel unsafe to take ownership for their ideas. How can students feel secure and safe, if they know that AI systems are used for surveilling and policing their thoughts and actions? [ 49 ].

Problems also emerge when surveillance systems trigger issues related to autonomy, more specifically, the person’s ability to act on her or his own interest and values. Predictive systems which are powered by algorithms jeopardize students and teachers’ autonomy and their ability to govern their own life [ 46 , 47 ]. Use of algorithms to make predictions about individuals’ actions based on their information raise questions about fairness and self-freedom [ 19 ]. Therefore, the risks of predictive analysis also include the perpetuation of existing bias and prejudices of social discrimination and stratification [ 42 ].

Finally, bias and discrimination are critical concerns in debates of AI ethics in K-12 education [ 6 ]. In AI platforms, the existing power structures and biases are embedded into machine-learning models [ 6 ]. Gender bias is one of the most apparent forms of this problem, as the bias is revealed when students in language learning courses use AI to translate between a gender-specific language and one that is less-so. For example, while Google Translate translated the Turkish equivalent of “S he/he is a nurse ” into the feminine form, it also translated the Turkish equivalent of “ She/he is a doctor ” into the masculine form [ 33 ]. This shows how AI models in language translation carry the societal biases and gender-specific stereotypes in the data [ 40 ]. Similarly, a number of problematic cases of racial bias are also associated with AI’s facial recognition systems. Research shows that facial recognition software has improperly misidentified a number of African American and Latino American people as convicted felons [ 42 ].

Additionally, biased decision-making algorithms reveal themselves throughout AI applications in K-12 education: personalized learning, automated assessment, SNSs, and predictive systems in education. Although the main promise of machine-learning models is increased accuracy and objectivity, current incidents have revealed the contrary. For instance, England’s A-level and GCSE secondary level examinations were cancelled due to the pandemic in the summer of 2020 [ 1 , 57 ]. An alternative assessment method was implemented to determine the qualification grades of students. The grade standardization algorithm was produced by the regulator Ofqual. With the assessment of Ofqual’s algorithm based on schools' previous examination results, thousands of students were shocked to receive unexpectedly low grades. Although a full discussion of the incident is beyond the scope of this article [ 51 ] it revealed how the score distribution favored students who attended private or independent schools, while students from underrepresented groups were hit hardest. Unfortunately, automated assessment algorithms have the potential to reconstruct unfair and inconsistent results by disrupting student’s final scores and future careers [ 53 ].

Teaching and understanding AI and ethics in educational settings

These ethical concerns suggest an urgent need to introduce students and teachers to the ethical challenges surrounding AI applications in K-12 education and how to navigate them. To meet this need, different research groups and nonprofit organizations offer a number of open-access resources based on AI and ethics. They provide instructional materials for students and teachers, such as lesson plans and hands-on activities, and professional learning materials for educators, such as open virtual learning sessions. Below, we describe and evaluate three resources: “AI and Ethics” curriculum and “AI and Data Privacy” workshop from the Massachusetts Institute of Technology (MIT) Media Lab as well as Code.org’s “AI and Oceans” activity. For readers who seek to investigate additional approaches and resources for K-12 level AI and ethics interaction, see: (a) The Chinese University of Hong Kong (CUHK)’s AI for the Future Project (AI4Future) [ 18 ]; (b) IBM’s Educator’s AI Classroom Kit [ 30 ], Google’s Teachable Machine [ 25 ], UK-based nonprofit organization Apps for Good [ 4 ], and Machine Learning for Kids [ 37 ].

"AI and Ethics Curriulum" for middle school students by MIT Media Lab

The MIT Media Lab team offers an open-access curriculum on AI and ethics for middle school students and teachers. Through a series of lesson plans and hand-on activities, teachers are guided to support students’ learning of the technical terminology of AI systems as well as the ethical and societal implications of AI [ 2 ]. The curriculum includes various lessons tied to learning objectives. One of the main learning goals is to introduce students to basic components of AI through algorithms, datasets, and supervised machine-learning systems all while underlining the problem of algorithmic bias [ 45 ]. For instance, in the activity “ AI Bingo” , students are given bingo cards with various AI systems, such as online search engine, customer service bot, and weather app. Students work with their partners collaboratively on these AI systems. In their AI Bingo chart, students try to identify what prediction the selected AI system makes and what dataset it uses. In that way, they become more familiar with the notions of dataset and prediction in the context of AI systems [ 45 ].

In the second investigation, “Algorithms as Opinions” , students think about algorithms as recipes, which are created by set of instructions that modify an input to produce an output [ 45 ]. Initially, students are asked to write an algorithm to make the “ best” jelly sandwich and peanut butter. They explore what it means to be “ best” and see how their opinions of best in their recipes are reflected in their algorithms. In this way, students are able to figure out that algorithms can have various motives and goals. Following this activity, students work on the “Ethical Matrix” , building on the idea of the algorithms as opinions [ 45 ]. During this investigation, students first refer back to their developed algorithms through their “best” jelly sandwich and peanut butter. They discuss what counts as the “best” sandwich for themselves (most healthy, practical, delicious, etc.). Then, through their ethical matrix (chart), students identify different stakeholders (such as their parents, teacher, or doctor) who care about their peanut butter and jelly sandwich algorithm. In this way, the values and opinions of those stakeholders also are embedded in the algorithm. Students fill out an ethical matrix and look for where those values conflict or overlap with each other. This matrix is a great tool for students to recognize different stakeholders in a system or society and how they are able to build and utilize the values of the algorithms in an ethical matrix.

The final investigation which teaches about the biased nature of algorithms is “Learning and Algorithmic Bias” [ 45 ]. During the investigation, students think further about the concept of classification. Using Google’s Teachable Machine tool [ 2 ], students explore the supervised machine-learning systems. Students train a cat–dog classifier using two different datasets. While the first dataset reflects the cats as the over-represented group, the second dataset indicates the equal and diverse representation between dogs and cats [ 2 ]. Using these datasets, students compare the accuracy between the classifiers and then discuss which dataset and outcome are fairer. This activity leads students into a discussion about the occurrence of bias in facial recognition algorithms and systems [ 2 ].

In the rest of the curriculum, similar to the AI Bingo investigation, students work with their partners to determine the various forms of AI systems in the YouTube platform (such as its recommender algorithm and advertisement matching algorithm). Through the investigation of “ YouTube Redesign”, students redesign YouTube’s recommender system. They first identify stakeholders and their values in the system, and then use an ethical matrix to reflect on the goals of their YouTube’s recommendation algorithm [ 45 ]. Finally, through the activity of “YouTube Socratic Seminar” , students read an abridged version of Wall Street Journal article by participating in a Socratic seminar. The article was edited to shorten the text and to provide more accessible language for middle school students. They discuss which stakeholders were most influential or significant in proposing changes in the YouTube Kids app and whether or not technologies like auto play should ever exist. During their discussion, students engage with the questions of: “Which stakeholder is making the most change or has the most power?”, “Have you ever seen an inappropriate piece of content on YouTube? What did you do?” [ 45 ].

Overall, the MIT Media Lab’s AI and Ethics curriculum is a high quality, open-access resource with which teachers can introduce middle school students to the risks and ethical implications of AI systems. The investigations described above involve students in collaborative, critical thinking activities that force them to wrestle with issues of bias and discrimination in AI, as well as surveillance and autonomy through the predictive systems and algorithmic bias.

“AI and Data Privacy” workshop series for K-9 students by MIT Media Lab

Another quality resource from the MIT Media Lab’s Personal Robots Group is a workshop series designed to teach students (between the ages 7 and 14) about data privacy and introduce them to designing and prototyping data privacy features. The group has made the content, materials, worksheets, and activities of the workshop series into an open-access online document, freely available to teachers [ 44 ].

The first workshop in the series is “ Mystery YouTube Viewer: A lesson on Data Privacy” . During the workshop, students engage with the question of what privacy and data mean [ 44 ]. They observe YouTube’s home page from the perspective of a mystery user. Using the clues from the videos, students make predictions about what the characters in the videos might look like or where they might live. In a way, students imitate YouTube algorithms’ prediction mode about the characters. Engaging with these questions and observations, students think further about why privacy and boundaries are important and how each algorithm will interpret us differently based on who creates the algorithm itself.

The second workshop in the series is “ Designing ads with transparency: A creative workshop” . Through this workshop, students are able to think further about the meaning, aim, and impact of advertising and the role of advertisements in our lives [ 44 ]. Students collaboratively create an advertisement using an everyday object. The objective is to make the advertisement as “transparent” as possible. To do that, students learn about notions of malware and adware, as well as the components of YouTube advertisements (such as sponsored labels, logos, news sections, etc.). By the end of the workshop, students design their ads as a poster, and they share with their peers.

The final workshop in MIT’s AI and data privacy series is “ Designing Privacy in Social Media Platforms”. This workshop is designed to teach students about YouTube, design, civics, and data privacy [ 44 ]. During the workshop, students create their own designs to solve one of the biggest challenges of the digital era: problems associated with online consent. The workshop allows students to learn more about the privacy laws and how they impact youth in terms of media consumption. Students consider YouTube within the lenses of the Children’s Online Privacy Protections Rule (COPPA). In this way, students reflect on one of the components of the legislation: how might students get parental permission (or verifiable consent)?

Such workshop resources seem promising in helping educate students and teachers about the ethical challenges of AI in education. Specifically, social media such as YouTube are widely used as a teaching and learning tool within K-12 classrooms and beyond them, in students’ everyday lives. These workshop resources may facilitate teachers’ and students’ knowledge of data privacy issues and support them in thinking further about how to protect privacy online. Moreover, educators seeking to implement such resources should consider engaging students in the larger question: who should own one’s data? Teaching students the underlying reasons for laws and facilitating debate on the extent to which they are just or not could help get at this question.

Investigation of “AI for Oceans” by Code.org

A third recommended resource for K-12 educators trying to navigate the ethical challenges of AI with their students comes from Code.org, a nonprofit organization focused on expanding students’ participation in computer science. Sponsored by Microsoft, Facebook, Amazon, Google, and other tech companies, Code.org aims to provide opportunities for K-12 students to learn about AI and machine-learning systems [ 20 ]. To support students (grades 3–12) in learning about AI, algorithms, machine learning, and bias, the organization offers an activity called “ AI for Oceans ”, where students are able to train their machine-learning models.

The activity is provided as an open-access tutorial for teachers to help their students explore how to train, model and classify data , as well as to understand how human bias plays a role in machine-learning systems. During the activity, students first classify the objects as either “fish” or “not fish” in an attempt to remove trash from the ocean. Then, they expand their training data set by including other sea creatures that belong underwater. Throughout the activity, students are also able to watch and interact with a number of visuals and video tutorials. With the support of their teachers, they discuss machine learning, steps and influences of training data, as well as the formation and risks of biased data [ 20 ].

Future directions for research and teaching on AI and ethics

In this paper, we provided an overview of the possibilities and potential ethical and societal risks of AI integration in education. To help address these risks, we highlighted several instructional strategies and resources for practitioners seeking to integrate AI applications in K-12 education and/or instruct students about the ethical issues they pose. These instructional materials have the potential to help students and teachers reap the powerful benefits of AI while navigating ethical challenges especially related to privacy concerns and bias. Existing research on AI in education provides insight on supporting students’ understanding and use of AI [ 2 , 13 ]; however, research on how to develop K-12 teachers’ instructional practices regarding AI and ethics is still in its infancy.

Moreover, current resources, as demonstrated above, mainly address privacy and bias-related ethical and societal concerns of AI. Conducting more exploratory and critical research on teachers’ and students’ surveillance and autonomy concerns will be important to designing future resources. In addition, curriculum developers and workshop designers might consider centering culturally relevant and responsive pedagogies (by focusing on students’ funds of knowledge, family background, and cultural experiences) while creating instructional materials that address surveillance, privacy, autonomy, and bias. In such student-centered learning environments, students voice their own cultural and contextual experiences while trying to critique and disrupt existing power structures and cultivate their social awareness [ 24 , 36 ].

Finally, as scholars in teacher education and educational technology, we believe that educating future generations of diverse citizens to participate in the ethical use and development of AI will require more professional development for K-12 teachers (both pre-service and in-service). For instance, through sustained professional learning sessions, teachers could engage with suggested curriculum resources and teaching strategies as well as build a community of practice where they can share and critically reflect on their experiences with other teachers. Further research on such reflective teaching practices and students’ sense-making processes in relation to AI and ethics lessons will be essential to developing curriculum materials and pedagogies relevant to a broad base of educators and students.

This work was supported by the Graduate School at Michigan State University, College of Education Summer Research Fellowship.

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Evaluating Artificial Intelligence in Education for Next Generation

Shubham Joshi 1 , Radha Krishna Rambola 1 and Prathamesh Churi 2

Published under licence by IOP Publishing Ltd Journal of Physics: Conference Series , Volume 1714 , 2nd International Conference on Smart and Intelligent Learning for Information Optimization (CONSILIO) 2020 24-25 October 2020, Goa, India Citation Shubham Joshi et al 2021 J. Phys.: Conf. Ser. 1714 012039 DOI 10.1088/1742-6596/1714/1/012039

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The use of Artificial Intelligence (AI) is now observed in almost all areas of our lives. Artificial intelligence is a thriving technology to transform all aspects of our social interaction. In education, AI will now develop new teaching and learning solutions that will be tested in different situations. Educational goals can be better achieved and managed by new educational technologies. First, this paper analyses how AI can use to improve outcomes in teaching, providing examples of how technology AI can help educators use data to enhance fairness and rank of education in developing countries. This study aims to examine teacher's and student's perceptions of the use and effectiveness of AI in education. Its curse and perceived as a good education system and human knowledge. The optimistic use of AI in class is strongly recommended by teachers and students. But every teacher is more adapted to new technological changes than students. Further research on generational and geographical diversity on perceptions of teachers and students can contribute to the more effective implementation of AI in Education (AIED).

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Mapping the Contours

Utopic and dystopic perspectives on the use of ai in higher education.

• Samita Sarkar Brock University

This paper explores the impact of artificial intelligence (AI) on education, with a focus on assessment and academic integrity in higher education. We conducted a thematic analysis of literature on AI and academic integrity, framed by possible utopic and dystopic scenarios. We found that AI can be used to generate text, summarize work, create outlines, and provide information and resources on a particular topic, saving time and money. We argue that effective institutional policies should be established around the use of AI technologies, such as ChatGPT, to better serve the fields of education and academic research. The paper also discusses the implications of AI for university students, including the potential for personalized learning, quick feedback on student work, and improved accessibility for students with disabilities. However, the use of AI in education raises concerns about academic integrity and the potential for cheating. We caution that ethical considerations under existing academic integrity frameworks must be considered when implementing AI in education. The article concludes by calling for further research on the impact of AI on education and the development of guidelines and policies to ensure that AI is used in a responsible and ethical manner.

Author Biography

Dr. rahul kumar, brock university.

Assistant Professor, 

Department of Educational Studies

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Juan Carlos Niebles

Vanessa Parli

Yoav Shoham

Russell Wald

Staff members.

Loredana Fattorini

Nestor Maslej

Letter from the co-directors.

A decade ago, the best AI systems in the world were unable to classify objects in images at a human level. AI struggled with language comprehension and could not solve math problems. Today, AI systems routinely exceed human performance on standard benchmarks.

Progress accelerated in 2023. New state-of-the-art systems like GPT-4, Gemini, and Claude 3 are impressively multimodal: They can generate fluent text in dozens of languages, process audio, and even explain memes. As AI has improved, it has increasingly forced its way into our lives. Companies are racing to build AI-based products, and AI is increasingly being used by the general public. But current AI technology still has significant problems. It cannot reliably deal with facts, perform complex reasoning, or explain its conclusions.

AI faces two interrelated futures. First, technology continues to improve and is increasingly used, having major consequences for productivity and employment. It can be put to both good and bad uses. In the second future, the adoption of AI is constrained by the limitations of the technology. Regardless of which future unfolds, governments are increasingly concerned. They are stepping in to encourage the upside, such as funding university R&D and incentivizing private investment. Governments are also aiming to manage the potential downsides, such as impacts on employment, privacy concerns, misinformation, and intellectual property rights.

As AI rapidly evolves, the AI Index aims to help the AI community, policymakers, business leaders, journalists, and the general public navigate this complex landscape. It provides ongoing, objective snapshots tracking several key areas: technical progress in AI capabilities, the community and investments driving AI development and deployment, public opinion on current and potential future impacts, and policy measures taken to stimulate AI innovation while managing its risks and challenges. By comprehensively monitoring the AI ecosystem, the Index serves as an important resource for understanding this transformative technological force.

On the technical front, this year’s AI Index reports that the number of new large language models released worldwide in 2023 doubled over the previous year. Two-thirds were open-source, but the highest-performing models came from industry players with closed systems. Gemini Ultra became the first LLM to reach human-level performance on the Massive Multitask Language Understanding (MMLU) benchmark; performance on the benchmark has improved by 15 percentage points since last year. Additionally, GPT-4 achieved an impressive 0.97 mean win rate score on the comprehensive Holistic Evaluation of Language Models (HELM) benchmark, which includes MMLU among other evaluations.

Although global private investment in AI decreased for the second consecutive year, investment in generative AI skyrocketed. More Fortune 500 earnings calls mentioned AI than ever before, and new studies show that AI tangibly boosts worker productivity. On the policymaking front, global mentions of AI in legislative proceedings have never been higher. U.S. regulators passed more AI-related regulations in 2023 than ever before. Still, many expressed concerns about AI’s ability to generate deepfakes and impact elections. The public became more aware of AI, and studies suggest that they responded with nervousness.

Ray Perrault Co-director, AI Index

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How we can prepare for the future with foundational policy ideas for AI in education

Integrating AI in education effectively should be done with responsible and AI use in mind. Image:  Unsplash/Yogendra Singh

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Stay up to date:, artificial intelligence.

• How artificial intelligence (AI) is integrated is critical as it should enhance human interaction and decision-making rather than replace them through responsible and equitable application in education.
• Students and educators must learn about AI to prepare them for a future increasingly intertwined with AI technologies, including understanding AI’s principles, applications and ethical implications.
• Policy recommendations for integrating AI in education effectively include creating AI-focused task forces, promoting AI literacy, establishing responsible AI guidelines, professional development support and AI research and development.

Education leaders and policymakers face the challenge of leading their communities when artificial intelligence (AI), including education, is becoming increasingly prominent in society. Understanding AI is critical for crafting effective policies that promote responsibility and equity in how AI tools can be accessed, how AI-enabled learning experiences are designed and how students use AI in the classroom. Integrating AI into society should augment, not replace, human interaction and decision-making.

“Leveraging AI’s transformative power, we can drive human progress by revolutionizing education globally, democratizing access and preparing future generations for the challenges and opportunities of a rapidly evolving world,” says Narmeen Makhani from the Education Testing Service.

Innovative teaching with and about AI provides an opportunity to improve equity and help students remain competitive in a changing labour market. For example, the International Monetary Fund estimates that 40% of global jobs will be complemented or in extreme cases, replaced by AI.

“Today’s students can anticipate a future where they will be working with or alongside AI,” according to Joseph South, a chief learning officer at the Association for Supervision and Curriculum Development and International Society for Technology in Education. “It is essential that educators and students understand its present power and potential impact. Our schools must prepare the next generation of AI designers.”

Building AI literacy is essential for understanding AI’s principles, concepts, applications, limitations and ethical implications. It prepares individuals to engage responsibly with AI in various aspects of life, including education and the workforce.

In education, AI offers benefits such as personalized learning, effective feedback and operational efficiency. However, it also poses risks, such as misinformation and loss of critical thinking skills.

Key policy approaches

The Khan Academy’s chief learning officer, Kristen DiCerbo, believes AI holds the promise to tackle many of the persistent problems we see in education, including unfinished learning and teacher burnout.

“By providing access to 1:1 learning support and true teacher assistance, including using data to drive recommendations, we can improve learning outcomes for all,” she says.

Policymakers and education leaders can prepare the future workforce by implementing five foundational policies to help realize the potential benefits of AI in education while mitigating the risks.

• Foster leadership: Create an AI in education task force with experts, educators, students, community members and policymakers to guide policy and oversee implementation. These task forces can drive innovation and ensure AI aligns with educational goals.
• Promote AI literacy: Integrate AI concepts into curricula and teach students to evaluate AI and its outputs critically. Students can become informed consumers and creators of AI-powered technologies by understanding AI’s potential and limitations.
• Provide guidance: Establish clear guidelines for the safe and responsible use of AI in education. We must ensure that AI tools are used ethically, focusing on student privacy and responsible usage. By providing guidance, we can ensure that AI enhances learning experiences without compromising safety or privacy.
• Build capacity: Support educators and staff in integrating AI into teaching, learning, and school management and operations. Professional development programmes can help staff understand AI, its limitations and ethical considerations. By building capacity, we can ensure that leaders develop the expertise to serve their communities and that all staff are equipped to use AI responsibly and effectively throughout the education system.
• Support innovation: Fund research and development to advance AI in education pedagogy, curriculum and tools. By supporting innovation, we can drive the development of new AI technologies that enhance learning experiences and improve student outcomes.

“Transforming education requires the engagement of all those serving school systems. The TeachAI initiative is honoured to bring education leaders, policymakers, technology creators, researchers, and civil society together to create resources such as these foundational policy ideas,” says Pat Yongpradit, chief academic officer of Code.org.

Working together to foster leadership, promote AI literacy, provide guidance, build capacity and support innovation, we can ensure that AI enhances education for all students.

As the World Economic Forum’s Saadia Zahidi says: “As we navigate the uncertainties of AI in education, TeachAI’s guidance in this swiftly evolving environment becomes indispensable. Embracing a multistakeholder approach to developing and implementing these foundational policies will be paramount for optimizing AI’s benefits while mitigating associated risks.”

Let’s seize the opportunity to harness the power of AI and create a brighter future for education.

Have you read?

Unesco releases a new roadmap for using ai in education, without universal ai literacy, ai will fail us.

To learn more about TeachAI’s latest resource, Foundational Policy Ideas for AI in Education, visit teachai.org/policy .

TeachAI is an initiative uniting education and technology leaders to assist governments and education authorities in teaching with and about AI. It is led by Code.org , the Educational Testing Service , the International Society for Technology in Education , Khan Academy and the World Economic Forum . It is advised by a diverse group of more than 100 organizations, governments and individuals. TeachAI’s goals include providing policy guidance, increasing awareness, and building community and capacity.

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Artificial Intelligence in Science Education (2013–2023): Research Trends in Ten Years

• Published: 06 October 2023
• Volume 33 , pages 94–117, ( 2024 )

Cite this article

• Fenglin Jia   ORCID: orcid.org/0000-0002-6233-9873 1 ,
• Daner Sun   ORCID: orcid.org/0000-0002-9813-6306 2 &
• Chee-kit Looi   ORCID: orcid.org/0000-0001-9905-2713 1  

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The use of artificial intelligence has played an important role in science teaching and learning. The purpose of this study was to fill a gap in the current review of research on AI in science education (AISE) in the early stage of education by systematically reviewing existing research in this area. This systematic review examined the trends and research foci of AI in the science of early stages of education. This review study employed a bibliometric analysis and content analysis to examine the characteristics of 76 studies on Artificial Intelligence in Science Education (AISE) indexed in Web of Science and Scopus from 2013 to 2023. The analytical tool CiteSpace was utilized for the analysis. The study aimed to provide an overview of the development level of AISE and identify major research trends, keywords, research themes, high-impact journals, institutions, countries/regions, and the impact of AISE studies. The results, based on econometric analyses, indicate that AISE has experienced increasing influence over the past decade. Cluster and timeline analyses of the retrieved keywords revealed that AI in primary and secondary science education can be categorized into 11 main themes, and the chronology of their emergence was identified. Among the most prolific journals in this field are the International Journal of Social Robotics, Educational Technology Research and Development, and others. Furthermore, the analysis identified that institutions and countries/regions located primarily in the United States have made the most significant contributions to AISE research. To explore the learning outcomes and overall impact of AI technologies on learners in primary and secondary schools, content analysis was conducted, identifying five main categories of technology applications. This study provides valuable insights into the advancements and implications of AI in science education at the primary and secondary levels.

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Jia, F., Sun, D. & Looi, Ck. Artificial Intelligence in Science Education (2013–2023): Research Trends in Ten Years. J Sci Educ Technol 33 , 94–117 (2024). https://doi.org/10.1007/s10956-023-10077-6

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About 1 in 5 U.S. teens who’ve heard of ChatGPT have used it for schoolwork

Roughly one-in-five teenagers who have heard of ChatGPT say they have used it to help them do their schoolwork, according to a new Pew Research Center survey of U.S. teens ages 13 to 17. With a majority of teens having heard of ChatGPT, that amounts to 13% of all U.S. teens who have used the generative artificial intelligence (AI) chatbot in their schoolwork.

Teens in higher grade levels are particularly likely to have used the chatbot to help them with schoolwork. About one-quarter of 11th and 12th graders who have heard of ChatGPT say they have done this. This share drops to 17% among 9th and 10th graders and 12% among 7th and 8th graders.

There is no significant difference between teen boys and girls who have used ChatGPT in this way.

The introduction of ChatGPT last year has led to much discussion about its role in schools , especially whether schools should integrate the new technology into the classroom or ban it .

Pew Research Center conducted this analysis to understand American teens’ use and understanding of ChatGPT in the school setting.

The Center conducted an online survey of 1,453 U.S. teens from Sept. 26 to Oct. 23, 2023, via Ipsos. Ipsos recruited the teens via their parents, who were part of its KnowledgePanel . The KnowledgePanel is a probability-based web panel recruited primarily through national, random sampling of residential addresses. The survey was weighted to be representative of U.S. teens ages 13 to 17 who live with their parents by age, gender, race and ethnicity, household income, and other categories.

This research was reviewed and approved by an external institutional review board (IRB), Advarra, an independent committee of experts specializing in helping to protect the rights of research participants.

Here are the  questions used for this analysis , along with responses, and its  methodology .

Teens’ awareness of ChatGPT

Overall, two-thirds of U.S. teens say they have heard of ChatGPT, including 23% who have heard a lot about it. But awareness varies by race and ethnicity, as well as by household income:

• 72% of White teens say they’ve heard at least a little about ChatGPT, compared with 63% of Hispanic teens and 56% of Black teens.
• 75% of teens living in households that make $75,000 or more annually have heard of ChatGPT. Much smaller shares in households with incomes between $30,000 and $74,999 (58%) and less than $30,000 (41%) say the same.

Teens who are more aware of ChatGPT are more likely to use it for schoolwork. Roughly a third of teens who have heard a lot about ChatGPT (36%) have used it for schoolwork, far higher than the 10% among those who have heard a little about it.

When do teens think it’s OK for students to use ChatGPT?

For teens, whether it is – or is not – acceptable for students to use ChatGPT depends on what it is being used for.

There is a fair amount of support for using the chatbot to explore a topic. Roughly seven-in-ten teens who have heard of ChatGPT say it’s acceptable to use when they are researching something new, while 13% say it is not acceptable.

However, there is much less support for using ChatGPT to do the work itself. Just one-in-five teens who have heard of ChatGPT say it’s acceptable to use it to write essays, while 57% say it is not acceptable. And 39% say it’s acceptable to use ChatGPT to solve math problems, while a similar share of teens (36%) say it’s not acceptable.

Some teens are uncertain about whether it’s acceptable to use ChatGPT for these tasks. Between 18% and 24% say they aren’t sure whether these are acceptable use cases for ChatGPT.

Those who have heard a lot about ChatGPT are more likely than those who have only heard a little about it to say it’s acceptable to use the chatbot to research topics, solve math problems and write essays. For instance, 54% of teens who have heard a lot about ChatGPT say it’s acceptable to use it to solve math problems, compared with 32% among those who have heard a little about it.

Note: Here are the  questions used for this analysis , along with responses, and its  methodology .

• Artificial Intelligence
• Technology Adoption
• Teens & Tech

Many Americans think generative AI programs should credit the sources they rely on

Americans’ use of chatgpt is ticking up, but few trust its election information, q&a: how we used large language models to identify guests on popular podcasts, striking findings from 2023, what the data says about americans’ views of artificial intelligence, most popular.

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