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• How to Write a Literature Review | Guide, Examples, & Templates

How to Write a Literature Review | Guide, Examples, & Templates

Published on January 2, 2023 by Shona McCombes . Revised on September 11, 2023.

What is a literature review? A literature review is a survey of scholarly sources on a specific topic. It provides an overview of current knowledge, allowing you to identify relevant theories, methods, and gaps in the existing research that you can later apply to your paper, thesis, or dissertation topic .

There are five key steps to writing a literature review:

• Search for relevant literature
• Evaluate sources
• Identify themes, debates, and gaps
• Outline the structure
• Write your literature review

A good literature review doesn’t just summarize sources—it analyzes, synthesizes , and critically evaluates to give a clear picture of the state of knowledge on the subject.

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Table of contents

What is the purpose of a literature review, examples of literature reviews, step 1 – search for relevant literature, step 2 – evaluate and select sources, step 3 – identify themes, debates, and gaps, step 4 – outline your literature review’s structure, step 5 – write your literature review, free lecture slides, other interesting articles, frequently asked questions, introduction.

• Quick Run-through
• Step 1 & 2

When you write a thesis , dissertation , or research paper , you will likely have to conduct a literature review to situate your research within existing knowledge. The literature review gives you a chance to:

• Demonstrate your familiarity with the topic and its scholarly context
• Develop a theoretical framework and methodology for your research
• Position your work in relation to other researchers and theorists
• Show how your research addresses a gap or contributes to a debate
• Evaluate the current state of research and demonstrate your knowledge of the scholarly debates around your topic.

Writing literature reviews is a particularly important skill if you want to apply for graduate school or pursue a career in research. We’ve written a step-by-step guide that you can follow below.

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Writing literature reviews can be quite challenging! A good starting point could be to look at some examples, depending on what kind of literature review you’d like to write.

• Example literature review #1: “Why Do People Migrate? A Review of the Theoretical Literature” ( Theoretical literature review about the development of economic migration theory from the 1950s to today.)
• Example literature review #2: “Literature review as a research methodology: An overview and guidelines” ( Methodological literature review about interdisciplinary knowledge acquisition and production.)
• Example literature review #3: “The Use of Technology in English Language Learning: A Literature Review” ( Thematic literature review about the effects of technology on language acquisition.)
• Example literature review #4: “Learners’ Listening Comprehension Difficulties in English Language Learning: A Literature Review” ( Chronological literature review about how the concept of listening skills has changed over time.)

You can also check out our templates with literature review examples and sample outlines at the links below.

Download Word doc Download Google doc

Before you begin searching for literature, you need a clearly defined topic .

If you are writing the literature review section of a dissertation or research paper, you will search for literature related to your research problem and questions .

Make a list of keywords

Start by creating a list of keywords related to your research question. Include each of the key concepts or variables you’re interested in, and list any synonyms and related terms. You can add to this list as you discover new keywords in the process of your literature search.

• Social media, Facebook, Instagram, Twitter, Snapchat, TikTok
• Body image, self-perception, self-esteem, mental health
• Generation Z, teenagers, adolescents, youth

Search for relevant sources

Use your keywords to begin searching for sources. Some useful databases to search for journals and articles include:

• Your university’s library catalogue
• Google Scholar
• Project Muse (humanities and social sciences)
• Medline (life sciences and biomedicine)
• EconLit (economics)
• Inspec (physics, engineering and computer science)

You can also use boolean operators to help narrow down your search.

Make sure to read the abstract to find out whether an article is relevant to your question. When you find a useful book or article, you can check the bibliography to find other relevant sources.

You likely won’t be able to read absolutely everything that has been written on your topic, so it will be necessary to evaluate which sources are most relevant to your research question.

For each publication, ask yourself:

• What question or problem is the author addressing?
• What are the key concepts and how are they defined?
• What are the key theories, models, and methods?
• Does the research use established frameworks or take an innovative approach?
• What are the results and conclusions of the study?
• How does the publication relate to other literature in the field? Does it confirm, add to, or challenge established knowledge?
• What are the strengths and weaknesses of the research?

Make sure the sources you use are credible , and make sure you read any landmark studies and major theories in your field of research.

You can use our template to summarize and evaluate sources you’re thinking about using. Click on either button below to download.

Take notes and cite your sources

As you read, you should also begin the writing process. Take notes that you can later incorporate into the text of your literature review.

It is important to keep track of your sources with citations to avoid plagiarism . It can be helpful to make an annotated bibliography , where you compile full citation information and write a paragraph of summary and analysis for each source. This helps you remember what you read and saves time later in the process.

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To begin organizing your literature review’s argument and structure, be sure you understand the connections and relationships between the sources you’ve read. Based on your reading and notes, you can look for:

• Trends and patterns (in theory, method or results): do certain approaches become more or less popular over time?
• Themes: what questions or concepts recur across the literature?
• Debates, conflicts and contradictions: where do sources disagree?
• Pivotal publications: are there any influential theories or studies that changed the direction of the field?
• Gaps: what is missing from the literature? Are there weaknesses that need to be addressed?

This step will help you work out the structure of your literature review and (if applicable) show how your own research will contribute to existing knowledge.

• Most research has focused on young women.
• There is an increasing interest in the visual aspects of social media.
• But there is still a lack of robust research on highly visual platforms like Instagram and Snapchat—this is a gap that you could address in your own research.

There are various approaches to organizing the body of a literature review. Depending on the length of your literature review, you can combine several of these strategies (for example, your overall structure might be thematic, but each theme is discussed chronologically).

Chronological

The simplest approach is to trace the development of the topic over time. However, if you choose this strategy, be careful to avoid simply listing and summarizing sources in order.

Try to analyze patterns, turning points and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred.

If you have found some recurring central themes, you can organize your literature review into subsections that address different aspects of the topic.

For example, if you are reviewing literature about inequalities in migrant health outcomes, key themes might include healthcare policy, language barriers, cultural attitudes, legal status, and economic access.

Methodological

If you draw your sources from different disciplines or fields that use a variety of research methods , you might want to compare the results and conclusions that emerge from different approaches. For example:

• Look at what results have emerged in qualitative versus quantitative research
• Discuss how the topic has been approached by empirical versus theoretical scholarship
• Divide the literature into sociological, historical, and cultural sources

Theoretical

A literature review is often the foundation for a theoretical framework . You can use it to discuss various theories, models, and definitions of key concepts.

You might argue for the relevance of a specific theoretical approach, or combine various theoretical concepts to create a framework for your research.

Like any other academic text , your literature review should have an introduction , a main body, and a conclusion . What you include in each depends on the objective of your literature review.

The introduction should clearly establish the focus and purpose of the literature review.

Depending on the length of your literature review, you might want to divide the body into subsections. You can use a subheading for each theme, time period, or methodological approach.

As you write, you can follow these tips:

• Summarize and synthesize: give an overview of the main points of each source and combine them into a coherent whole
• Analyze and interpret: don’t just paraphrase other researchers — add your own interpretations where possible, discussing the significance of findings in relation to the literature as a whole
• Critically evaluate: mention the strengths and weaknesses of your sources
• Write in well-structured paragraphs: use transition words and topic sentences to draw connections, comparisons and contrasts

In the conclusion, you should summarize the key findings you have taken from the literature and emphasize their significance.

When you’ve finished writing and revising your literature review, don’t forget to proofread thoroughly before submitting. Not a language expert? Check out Scribbr’s professional proofreading services !

This article has been adapted into lecture slides that you can use to teach your students about writing a literature review.

Scribbr slides are free to use, customize, and distribute for educational purposes.

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If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.

There are several reasons to conduct a literature review at the beginning of a research project:

• To familiarize yourself with the current state of knowledge on your topic
• To ensure that you’re not just repeating what others have already done
• To identify gaps in knowledge and unresolved problems that your research can address
• To develop your theoretical framework and methodology
• To provide an overview of the key findings and debates on the topic

Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.

The literature review usually comes near the beginning of your thesis or dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .

A literature review is a survey of credible sources on a topic, often used in dissertations , theses, and research papers . Literature reviews give an overview of knowledge on a subject, helping you identify relevant theories and methods, as well as gaps in existing research. Literature reviews are set up similarly to other  academic texts , with an introduction , a main body, and a conclusion .

An  annotated bibliography is a list of  source references that has a short description (called an annotation ) for each of the sources. It is often assigned as part of the research process for a  paper .  

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• UConn Library
• Literature Review: The What, Why and How-to Guide
• Introduction

Literature Review: The What, Why and How-to Guide — Introduction

• Getting Started
• How to Pick a Topic
• Strategies to Find Sources
• Evaluating Sources & Lit. Reviews
• Tips for Writing Literature Reviews
• Writing Literature Review: Useful Sites
• Citation Resources
• Other Academic Writings

What are Literature Reviews?

So, what is a literature review? "A literature review is an account of what has been published on a topic by accredited scholars and researchers. In writing the literature review, your purpose is to convey to your reader what knowledge and ideas have been established on a topic, and what their strengths and weaknesses are. As a piece of writing, the literature review must be defined by a guiding concept (e.g., your research objective, the problem or issue you are discussing, or your argumentative thesis). It is not just a descriptive list of the material available, or a set of summaries." Taylor, D.  The literature review: A few tips on conducting it . University of Toronto Health Sciences Writing Centre.

Goals of Literature Reviews

What are the goals of creating a Literature Review?  A literature could be written to accomplish different aims:

• To develop a theory or evaluate an existing theory
• To summarize the historical or existing state of a research topic
• Identify a problem in a field of research 

Baumeister, R. F., & Leary, M. R. (1997). Writing narrative literature reviews .  Review of General Psychology , 1 (3), 311-320.

What kinds of sources require a Literature Review?

• A research paper assigned in a course
• A thesis or dissertation
• A grant proposal
• An article intended for publication in a journal

All these instances require you to collect what has been written about your research topic so that you can demonstrate how your own research sheds new light on the topic.

Types of Literature Reviews

What kinds of literature reviews are written?

Narrative review: The purpose of this type of review is to describe the current state of the research on a specific topic/research and to offer a critical analysis of the literature reviewed. Studies are grouped by research/theoretical categories, and themes and trends, strengths and weakness, and gaps are identified. The review ends with a conclusion section which summarizes the findings regarding the state of the research of the specific study, the gaps identify and if applicable, explains how the author's research will address gaps identify in the review and expand the knowledge on the topic reviewed.

• Example : Predictors and Outcomes of U.S. Quality Maternity Leave: A Review and Conceptual Framework:  10.1177/08948453211037398  

Systematic review : "The authors of a systematic review use a specific procedure to search the research literature, select the studies to include in their review, and critically evaluate the studies they find." (p. 139). Nelson, L. K. (2013). Research in Communication Sciences and Disorders . Plural Publishing.

• Example : The effect of leave policies on increasing fertility: a systematic review:  10.1057/s41599-022-01270-w

Meta-analysis : "Meta-analysis is a method of reviewing research findings in a quantitative fashion by transforming the data from individual studies into what is called an effect size and then pooling and analyzing this information. The basic goal in meta-analysis is to explain why different outcomes have occurred in different studies." (p. 197). Roberts, M. C., & Ilardi, S. S. (2003). Handbook of Research Methods in Clinical Psychology . Blackwell Publishing.

• Example : Employment Instability and Fertility in Europe: A Meta-Analysis:  10.1215/00703370-9164737

Meta-synthesis : "Qualitative meta-synthesis is a type of qualitative study that uses as data the findings from other qualitative studies linked by the same or related topic." (p.312). Zimmer, L. (2006). Qualitative meta-synthesis: A question of dialoguing with texts .  Journal of Advanced Nursing , 53 (3), 311-318.

• Example : Women’s perspectives on career successes and barriers: A qualitative meta-synthesis:  10.1177/05390184221113735

Literature Reviews in the Health Sciences

• UConn Health subject guide on systematic reviews Explanation of the different review types used in health sciences literature as well as tools to help you find the right review type
• << Previous: Getting Started
• Next: How to Pick a Topic >>
• Last Updated: Sep 21, 2022 2:16 PM
• URL: https://guides.lib.uconn.edu/literaturereview

Libraries | Research Guides

Literature reviews, what is a literature review, learning more about how to do a literature review.

• Planning the Review
• The Research Question
• Choosing Where to Search
• Organizing the Review
• Writing the Review

A literature review is a review and synthesis of existing research on a topic or research question. A literature review is meant to analyze the scholarly literature, make connections across writings and identify strengths, weaknesses, trends, and missing conversations. A literature review should address different aspects of a topic as it relates to your research question. A literature review goes beyond a description or summary of the literature you have read. 

• Sage Research Methods Core This link opens in a new window SAGE Research Methods supports research at all levels by providing material to guide users through every step of the research process. SAGE Research Methods is the ultimate methods library with more than 1000 books, reference works, journal articles, and instructional videos by world-leading academics from across the social sciences, including the largest collection of qualitative methods books available online from any scholarly publisher. – Publisher

• Next: Planning the Review >>
• Last Updated: Jul 8, 2024 11:22 AM
• URL: https://libguides.northwestern.edu/literaturereviews

State-of-the-art literature review methodology: A six-step approach for knowledge synthesis

• Original Article
• Open access
• Published: 05 September 2022
• Volume 11 , pages 281–288, ( 2022 )

Cite this article

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• Erin S. Barry   ORCID: orcid.org/0000-0003-0788-7153 1 , 2 ,
• Jerusalem Merkebu   ORCID: orcid.org/0000-0003-3707-8920 3 &
• Lara Varpio   ORCID: orcid.org/0000-0002-1412-4341 3  

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Introduction

Researchers and practitioners rely on literature reviews to synthesize large bodies of knowledge. Many types of literature reviews have been developed, each targeting a specific purpose. However, these syntheses are hampered if the review type’s paradigmatic roots, methods, and markers of rigor are only vaguely understood. One literature review type whose methodology has yet to be elucidated is the state-of-the-art (SotA) review. If medical educators are to harness SotA reviews to generate knowledge syntheses, we must understand and articulate the paradigmatic roots of, and methods for, conducting SotA reviews.

We reviewed 940 articles published between 2014–2021 labeled as SotA reviews. We (a) identified all SotA methods-related resources, (b) examined the foundational principles and techniques underpinning the reviews, and (c) combined our findings to inductively analyze and articulate the philosophical foundations, process steps, and markers of rigor.

In the 940 articles reviewed, nearly all manuscripts (98%) lacked citations for how to conduct a SotA review. The term “state of the art” was used in 4 different ways. Analysis revealed that SotA articles are grounded in relativism and subjectivism.

This article provides a 6-step approach for conducting SotA reviews. SotA reviews offer an interpretive synthesis that describes: This is where we are now. This is how we got here. This is where we could be going. This chronologically rooted narrative synthesis provides a methodology for reviewing large bodies of literature to explore why and how our current knowledge has developed and to offer new research directions.

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Literature reviews play a foundational role in scientific research; they support knowledge advancement by collecting, describing, analyzing, and integrating large bodies of information and data [ 1 , 2 ]. Indeed, as Snyder [ 3 ] argues, all scientific disciplines require literature reviews grounded in a methodology that is accurate and clearly reported. Many types of literature reviews have been developed, each with a unique purpose, distinct methods, and distinguishing characteristics of quality and rigor [ 4 , 5 ].

Each review type offers valuable insights if rigorously conducted [ 3 , 6 ]. Problematically, this is not consistently the case, and the consequences can be dire. Medical education’s policy makers and institutional leaders rely on knowledge syntheses to inform decision making [ 7 ]. Medical education curricula are shaped by these syntheses. Our accreditation standards are informed by these integrations. Our patient care is guided by these knowledge consolidations [ 8 ]. Clearly, it is important for knowledge syntheses to be held to the highest standards of rigor. And yet, that standard is not always maintained. Sometimes scholars fail to meet the review’s specified standards of rigor; other times the markers of rigor have never been explicitly articulated. While we can do little about the former, we can address the latter. One popular literature review type whose methodology has yet to be fully described, vetted, and justified is the state-of-the-art (SotA) review.

While many types of literature reviews amalgamate bodies of literature, SotA reviews offer something unique. By looking across the historical development of a body of knowledge, SotA reviews delves into questions like: Why did our knowledge evolve in this way? What other directions might our investigations have taken? What turning points in our thinking should we revisit to gain new insights? A SotA review—a form of narrative knowledge synthesis [ 5 , 9 ]—acknowledges that history reflects a series of decisions and then asks what different decisions might have been made.

SotA reviews are frequently used in many fields including the biomedical sciences [ 10 , 11 ], medicine [ 12 , 13 , 14 ], and engineering [ 15 , 16 ]. However, SotA reviews are rarely seen in medical education; indeed, a bibliometrics analysis of literature reviews published in 14 core medical education journals between 1999 and 2019 reported only 5 SotA reviews out of the 963 knowledge syntheses identified [ 17 ]. This is not to say that SotA reviews are absent; we suggest that they are often unlabeled. For instance, Schuwirth and van der Vleuten’s article “A history of assessment in medical education” [ 14 ] offers a temporally organized overview of the field’s evolving thinking about assessment. Similarly, McGaghie et al. published a chronologically structured review of simulation-based medical education research that “reviews and critically evaluates historical and contemporary research on simulation-based medical education” [ 18 , p. 50]. SotA reviews certainly have a place in medical education, even if that place is not explicitly signaled.

This lack of labeling is problematic since it conceals the purpose of, and work involved in, the SotA review synthesis. In a SotA review, the author(s) collects and analyzes the historical development of a field’s knowledge about a phenomenon, deconstructs how that understanding evolved, questions why it unfolded in specific ways, and posits new directions for research. Senior medical education scholars use SotA reviews to share their insights based on decades of work on a topic [ 14 , 18 ]; their junior counterparts use them to critique that history and propose new directions [ 19 ]. And yet, SotA reviews are generally not explicitly signaled in medical education. We suggest that at least two factors contribute to this problem. First, it may be that medical education scholars have yet to fully grasp the unique contributions SotA reviews provide. Second, the methodology and methods of SotA reviews are poorly reported making this form of knowledge synthesis appear to lack rigor. Both factors are rooted in the same foundational problem: insufficient clarity about SotA reviews. In this study, we describe SotA review methodology so that medical educators can explicitly use this form of knowledge synthesis to further advance the field.

We developed a four-step research design to meet this goal, illustrated in Fig.  1 .

Four-step research design process used for developing a State-of-the-Art literature review methodology

Step 1: Collect SotA articles

To build our initial corpus of articles reporting SotA reviews, we searched PubMed using the strategy (″state of the art review″[ti] OR ″state of the art review*″) and limiting our search to English articles published between 2014 and 2021. We strategically focused on PubMed, which includes MEDLINE, and is considered the National Library of Medicine’s premier database of biomedical literature and indexes health professions education and practice literature [ 20 ]. We limited our search to 2014–2021 to capture modern use of SotA reviews. Of the 960 articles identified, nine were excluded because they were duplicates, erratum, or corrigendum records; full text copies were unavailable for 11 records. All articles identified ( n  = 940) constituted the corpus for analysis.

Step 2: Compile all methods-related resources

EB, JM, or LV independently reviewed the 940 full-text articles to identify all references to resources that explained, informed, described, or otherwise supported the methods used for conducting the SotA review. Articles that met our criteria were obtained for analysis.

To ensure comprehensive retrieval, we also searched Scopus and Web of Science. Additionally, to find resources not indexed by these academic databases, we searched Google (see Electronic Supplementary Material [ESM] for the search strategies used for each database). EB also reviewed the first 50 items retrieved from each search looking for additional relevant resources. None were identified. Via these strategies, nine articles were identified and added to the collection of methods-related resources for analysis.

Step 3: Extract data for analysis

In Step 3, we extracted three kinds of information from the 940 articles papers identified in Step 1. First, descriptive data on each article were compiled (i.e., year of publication and the academic domain targeted by the journal). Second, each article was examined and excerpts collected about how the term state-of-the-art review was used (i.e., as a label for a methodology in-and-of itself; as an adjective qualifying another type of literature review; as a term included in the paper’s title only; or in some other way). Finally, we extracted excerpts describing: the purposes and/or aims of the SotA review; the methodology informing and methods processes used to carry out the SotA review; outcomes of analyses; and markers of rigor for the SotA review.

Two researchers (EB and JM) coded 69 articles and an interrater reliability of 94.2% was achieved. Any discrepancies were discussed. Given the high interrater reliability, the two authors split the remaining articles and coded independently.

Step 4: Construct the SotA review methodology

The methods-related resources identified in Step 2 and the data extractions from Step 3 were inductively analyzed by LV and EB to identify statements and research processes that revealed the ontology (i.e., the nature of reality that was reflected) and the epistemology (i.e., the nature of knowledge) underpinning the descriptions of the reviews. These authors studied these data to determine if the synthesis adhered to an objectivist or a subjectivist orientation, and to synthesize the purposes realized in these papers.

To confirm these interpretations, LV and EB compared their ontology, epistemology, and purpose determinations against two expectations commonly required of objectivist synthesis methods (e.g., systematic reviews): an exhaustive search strategy and an appraisal of the quality of the research data. These expectations were considered indicators of a realist ontology and objectivist epistemology [ 21 ] (i.e., that a single correct understanding of the topic can be sought through objective data collection {e.g., systematic reviews [ 22 ]}). Conversely, the inverse of these expectations were considered indicators of a relativist ontology and subjectivist epistemology [ 21 ] (i.e., that no single correct understanding of the topic is available; there are multiple valid understandings that can be generated and so a subjective interpretation of the literature is sought {e.g., narrative reviews [ 9 ]}).

Once these interpretations were confirmed, LV and EB reviewed and consolidated the methods steps described in these data. Markers of rigor were then developed that aligned with the ontology, epistemology, and methods of SotA reviews.

Of the 940 articles identified in Step 1, 98% ( n  = 923) lacked citations or other references to resources that explained, informed, or otherwise supported the SotA review process. Of the 17 articles that included supporting information, 16 cited Grant and Booth’s description [ 4 ] consisting of five sentences describing the overall purpose of SotA reviews, three sentences noting perceived strengths, and four sentences articulating perceived weaknesses. This resource provides no guidance on how to conduct a SotA review methodology nor markers of rigor. The one article not referencing Grant and Booth used “an adapted comparative effectiveness research search strategy that was adapted by a health sciences librarian” [ 23 , p. 381]. One website citation was listed in support of this strategy; however, the page was no longer available in summer 2021. We determined that the corpus was uninformed by a cardinal resource or a publicly available methodology description.

In Step 2 we identified nine resources [ 4 , 5 , 24 , 25 , 26 , 27 , 28 ]; none described the methodology and/or processes of carrying out SotA reviews. Nor did they offer explicit descriptions of the ontology or epistemology underpinning SotA reviews. Instead, these resources provided short overview statements (none longer than one paragraph) about the review type [ 4 , 5 , 24 , 25 , 26 , 27 , 28 ]. Thus, we determined that, to date, there are no available methodology papers describing how to conduct a SotA review.

Step 3 revealed that “state of the art” was used in 4 different ways across the 940 articles (see Fig.  2 for the frequency with which each was used). In 71% ( n  = 665 articles), the phrase was used only in the title, abstract, and/or purpose statement of the article; the phrase did not appear elsewhere in the paper and no SotA methodology was discussed. Nine percent ( n  = 84) used the phrase as an adjective to qualify another literature review type and so relied entirely on the methodology of a different knowledge synthesis approach (e.g., “a state of the art systematic review [ 29 ]”). In 5% ( n  = 52) of the articles, the phrase was not used anywhere within the article; instead, “state of the art” was the type of article within a journal. In the remaining 15% ( n  = 139), the phrase denoted a specific methodology (see ESM for all methodology articles). Via Step 4’s inductive analysis, the following foundational principles of SotA reviews were developed: (1) the ontology, (2) epistemology, and (3) purpose of SotA reviews.

Four ways the term “state of the art” is used in the corpus and how frequently each is used

Ontology of SotA reviews: Relativism

SotA reviews rest on four propositions:

The literature addressing a phenomenon offers multiple perspectives on that topic (i.e., different groups of researchers may hold differing opinions and/or interpretations of data about a phenomenon).

The reality of the phenomenon itself cannot be completely perceived or understood (i.e., due to limitations [e.g., the capabilities of current technologies, a research team’s disciplinary orientation] we can only perceive a limited part of the phenomenon).

The reality of the phenomenon is a subjective and inter-subjective construction (i.e., what we understand about a phenomenon is built by individuals and so their individual subjectivities shape that understanding).

The context in which the review was conducted informs the review (e.g., a SotA review of literature about gender identity and sexual function will be synthesized differently by researchers in the domain of gender studies than by scholars working in sex reassignment surgery).

As these propositions suggest, SotA scholars bring their experiences, expectations, research purposes, and social (including academic) orientations to bear on the synthesis work. In other words, a SotA review synthesizes the literature based on a specific orientation to the topic being addressed. For instance, a SotA review written by senior scholars who are experts in the field of medical education may reflect on the turning points that have shaped the way our field has evolved the modern practices of learner assessment, noting how the nature of the problem of assessment has moved: it was first a measurement problem, then a problem that embraced human judgment but needed assessment expertise, and now a whole system problem that is to be addressed from an integrated—not a reductionist—perspective [ 12 ]. However, if other scholars were to examine this same history from a technological orientation, learner assessment could be framed as historically constricted by the media available through which to conduct assessment, pointing to how artificial intelligence is laying the foundation for the next wave of assessment in medical education [ 30 ].

Given these foundational propositions, SotA reviews are steeped in a relativist ontology—i.e., reality is socially and experientially informed and constructed, and so no single objective truth exists. Researchers’ interpretations reflect their conceptualization of the literature—a conceptualization that could change over time and that could conflict with the understandings of others.

Epistemology of SotA reviews: Subjectivism

SotA reviews embrace subjectivism. The knowledge generated through the review is value-dependent, growing out of the subjective interpretations of the researcher(s) who conducted the synthesis. The SotA review generates an interpretation of the data that is informed by the expertise, experiences, and social contexts of the researcher(s). Furthermore, the knowledge developed through SotA reviews is shaped by the historical point in time when the review was conducted. SotA reviews are thus steeped in the perspective that knowledge is shaped by individuals and their community, and is a synthesis that will change over time.

Purpose of SotA reviews

SotA reviews create a subjectively informed summary of modern thinking about a topic. As a chronologically ordered synthesis, SotA reviews describe the history of turning points in researchers’ understanding of a phenomenon to contextualize a description of modern scientific thinking on the topic. The review presents an argument about how the literature could be interpreted; it is not a definitive statement about how the literature should or must be interpreted. A SotA review explores: the pivotal points shaping the historical development of a topic, the factors that informed those changes in understanding, and the ways of thinking about and studying the topic that could inform the generation of further insights. In other words, the purpose of SotA reviews is to create a three-part argument: This is where we are now in our understanding of this topic. This is how we got here. This is where we could go next.

The SotA methodology

Based on study findings and analyses, we constructed a six-stage SotA review methodology. This six-stage approach is summarized and guiding questions are offered in Tab.  1 .

Stage 1: Determine initial research question and field of inquiry

In Stage 1, the researcher(s) creates an initial description of the topic to be summarized and so must determine what field of knowledge (and/or practice) the search will address. Knowledge developed through the SotA review process is shaped by the context informing it; thus, knowing the domain in which the review will be conducted is part of the review’s foundational work.

Stage 2: Determine timeframe

This stage involves determining the period of time that will be defined as SotA for the topic being summarized. The researcher(s) should engage in a broad-scope overview of the literature, reading across the range of literature available to develop insights into the historical development of knowledge on the topic, including the turning points that shape the current ways of thinking about a topic. Understanding the full body of literature is required to decide the dates or events that demarcate the timeframe of now in the first of the SotA’s three-part argument: where we are now . Stage 2 is complete when the researcher(s) can explicitly justify why a specific year or event is the right moment to mark the beginning of state-of-the-art thinking about the topic being summarized.

Stage 3: Finalize research question(s) to reflect timeframe

Based on the insights developed in Stage 2, the researcher(s) will likely need to revise their initial description of the topic to be summarized. The formal research question(s) framing the SotA review are finalized in Stage 3. The revised description of the topic, the research question(s), and the justification for the timeline start year must be reported in the review article. These are markers of rigor and prerequisites for moving to Stage 4.

Stage 4: Develop search strategy to find relevant articles

In Stage 4, the researcher(s) develops a search strategy to identify the literature that will be included in the SotA review. The researcher(s) needs to determine which literature databases contain articles from the domain of interest. Because the review describes how we got here , the review must include literature that predates the state-of-the-art timeframe, determined in Stage 2, to offer this historical perspective.

Developing the search strategy will be an iterative process of testing and revising the search strategy to enable the researcher(s) to capture the breadth of literature required to meet the SotA review purposes. A librarian should be consulted since their expertise can expedite the search processes and ensure that relevant resources are identified. The search strategy must be reported (e.g., in the manuscript itself or in a supplemental file) so that others may replicate the process if they so choose (e.g., to construct a different SotA review [and possible different interpretations] of the same literature). This too is a marker of rigor for SotA reviews: the search strategies informing the identification of literature must be reported.

Stage 5: Analyses

The literature analysis undertaken will reflect the subjective insights of the researcher(s); however, the foundational premises of inductive research should inform the analysis process. Therefore, the researcher(s) should begin by reading the articles in the corpus to become familiar with the literature. This familiarization work includes: noting similarities across articles, observing ways-of-thinking that have shaped current understandings of the topic, remarking on assumptions underpinning changes in understandings, identifying important decision points in the evolution of understanding, and taking notice of gaps and assumptions in current knowledge.

The researcher(s) can then generate premises for the state-of-the-art understanding of the history that gave rise to modern thinking, of the current body of knowledge, and of potential future directions for research. In this stage of the analysis, the researcher(s) should document the articles that support or contradict their premises, noting any collections of authors or schools of thinking that have dominated the literature, searching for marginalized points of view, and studying the factors that contributed to the dominance of particular ways of thinking. The researcher(s) should also observe historical decision points that could be revisited. Theory can be incorporated at this stage to help shape insights and understandings. It should be highlighted that not all corpus articles will be used in the SotA review; instead, the researcher(s) will sample across the corpus to construct a timeline that represents the seminal moments of the historical development of knowledge.

Next, the researcher(s) should verify the thoroughness and strength of their interpretations. To do this, the researcher(s) can select different articles included in the corpus and examine if those articles reflect the premises the researcher(s) set out. The researcher(s) may also seek out contradictory interpretations in the literature to be sure their summary refutes these positions. The goal of this verification work is not to engage in a triangulation process to ensure objectivity; instead, this process helps the researcher(s) ensure the interpretations made in the SotA review represent the articles being synthesized and respond to the interpretations offered by others. This is another marker of rigor for SotA reviews: the authors should engage in and report how they considered and accounted for differing interpretations of the literature, and how they verified the thoroughness of their interpretations.

Stage 6: Reflexivity

Given the relativist subjectivism of a SotA review, it is important that the manuscript offer insights into the subjectivity of the researcher(s). This reflexivity description should articulate how the subjectivity of the researcher(s) informed interpretations of the data. These reflections will also influence the suggested directions offered in the last part of the SotA three-part argument: where we could go next. This is the last marker of rigor for SotA reviews: researcher reflexivity must be considered and reported.

SotA reviews have much to offer our field since they provide information on the historical progression of medical education’s understanding of a topic, the turning points that guided that understanding, and the potential next directions for future research. Those future directions may question the soundness of turning points and prior decisions, and thereby offer new paths of investigation. Since we were unable to find a description of the SotA review methodology, we inductively developed a description of the methodology—including its paradigmatic roots, the processes to be followed, and the markers of rigor—so that scholars can harness the unique affordances of this type of knowledge synthesis.

Given their chronology- and turning point-based orientation, SotA reviews are inherently different from other types of knowledge synthesis. For example, systematic reviews focus on specific research questions that are narrow in scope [ 32 , 33 ]; in contrast, SotA reviews present a broader historical overview of knowledge development and the decisions that gave rise to our modern understandings. Scoping reviews focus on mapping the present state of knowledge about a phenomenon including, for example, the data that are currently available, the nature of that data, and the gaps in knowledge [ 34 , 35 ]; conversely, SotA reviews offer interpretations of the historical progression of knowledge relating to a phenomenon centered on significant shifts that occurred during that history. SotA reviews focus on the turning points in the history of knowledge development to suggest how different decisions could give rise to new insights. Critical reviews draw on literature outside of the domain of focus to see if external literature can offer new ways of thinking about the phenomenon of interest (e.g., drawing on insights from insects’ swarm intelligence to better understand healthcare team adaptation [ 36 ]). SotA reviews focus on one domain’s body of literature to construct a timeline of knowledge development, demarcating where we are now, demonstrating how this understanding came to be via different turning points, and offering new research directions. Certainly, SotA reviews offer a unique kind of knowledge synthesis.

Our six-stage process for conducting these reviews reflects the subjectivist relativism that underpins the methodology. It aligns with the requirements proposed by others [ 24 , 25 , 26 , 27 ], what has been written about SotA reviews [ 4 , 5 ], and the current body of published SotA reviews. In contrast to existing guidance [ 4 , 5 , 20 , 21 , 22 , 23 ], our description offers a detailed reporting of the ontology, epistemology, and methodology processes for conducting the SotA review.

This explicit methodology description is essential since many academic journals list SotA reviews as an accepted type of literature review. For instance, Educational Research Review [ 24 ], the American Academy of Pediatrics [ 25 ], and Thorax all lists SotA reviews as one of the types of knowledge syntheses they accept [ 27 ]. However, while SotA reviews are valued by academia, guidelines or specific methodology descriptions for researchers to follow when conducting this type of knowledge synthesis are conspicuously absent. If academics in general, and medical education more specifically, are to take advantage of the insights that SotA reviews can offer, we need to rigorously engage in this synthesis work; to do that, we need clear descriptions of the methodology underpinning this review. This article offers such a description. We hope that more medical educators will conduct SotA reviews to generate insights that will contribute to further advancing our field’s research and scholarship.

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Acknowledgements

We thank Rhonda Allard for her help with the literature review and compiling all available articles. We also want to thank the PME editors who offered excellent development and refinement suggestions that greatly improved this manuscript.

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Department of Anesthesiology, F. Edward Hébert School of Medicine, Uniformed Services University, Bethesda, MD, USA

Erin S. Barry

School of Health Professions Education (SHE), Maastricht University, Maastricht, The Netherlands

Department of Medicine, F. Edward Hébert School of Medicine, Uniformed Services University, Bethesda, MD, USA

Jerusalem Merkebu & Lara Varpio

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Correspondence to Erin S. Barry .

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E.S. Barry, J. Merkebu and L. Varpio declare that they have no competing interests.

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40037_2022_725_moesm1_esm.docx.

For information regarding the search strategy to develop the corpus and search strategy for confirming capture of any available State of the Art review methodology descriptions. Additionally, a list of the methodology articles found through the search strategy/corpus is included

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Barry, E.S., Merkebu, J. & Varpio, L. State-of-the-art literature review methodology: A six-step approach for knowledge synthesis. Perspect Med Educ 11 , 281–288 (2022). https://doi.org/10.1007/s40037-022-00725-9

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Received : 03 December 2021

Revised : 25 July 2022

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Published : 05 September 2022

Issue Date : October 2022

DOI : https://doi.org/10.1007/s40037-022-00725-9

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Research Methods

• Getting Started
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Literature Review

• What is a Literature Review?
• What is NOT a Literature Review?
• Purposes of a Literature Review
• Types of Literature Reviews
• Literature Reviews vs. Systematic Reviews
• Systematic vs. Meta-Analysis

Literature Review  is a comprehensive survey of the works published in a particular field of study or line of research, usually over a specific period of time, in the form of an in-depth, critical bibliographic essay or annotated list in which attention is drawn to the most significant works.

Also, we can define a literature review as the collected body of scholarly works related to a topic:

• Summarizes and analyzes previous research relevant to a topic
• Includes scholarly books and articles published in academic journals
• Can be an specific scholarly paper or a section in a research paper

The objective of a Literature Review is to find previous published scholarly works relevant to an specific topic

• Help gather ideas or information
• Keep up to date in current trends and findings
• Help develop new questions

A literature review is important because it:

• Explains the background of research on a topic.
• Demonstrates why a topic is significant to a subject area.
• Helps focus your own research questions or problems
• Discovers relationships between research studies/ideas.
• Suggests unexplored ideas or populations
• Identifies major themes, concepts, and researchers on a topic.
• Tests assumptions; may help counter preconceived ideas and remove unconscious bias.
• Identifies critical gaps, points of disagreement, or potentially flawed methodology or theoretical approaches.
• Indicates potential directions for future research.

All content in this section is from Literature Review Research from Old Dominion University 

Keep in mind the following, a literature review is NOT:

Not an essay 

Not an annotated bibliography  in which you summarize each article that you have reviewed.  A literature review goes beyond basic summarizing to focus on the critical analysis of the reviewed works and their relationship to your research question.

Not a research paper   where you select resources to support one side of an issue versus another.  A lit review should explain and consider all sides of an argument in order to avoid bias, and areas of agreement and disagreement should be highlighted.

A literature review serves several purposes. For example, it

• provides thorough knowledge of previous studies; introduces seminal works.
• helps focus one’s own research topic.
• identifies a conceptual framework for one’s own research questions or problems; indicates potential directions for future research.
• suggests previously unused or underused methodologies, designs, quantitative and qualitative strategies.
• identifies gaps in previous studies; identifies flawed methodologies and/or theoretical approaches; avoids replication of mistakes.
• helps the researcher avoid repetition of earlier research.
• suggests unexplored populations.
• determines whether past studies agree or disagree; identifies controversy in the literature.
• tests assumptions; may help counter preconceived ideas and remove unconscious bias.

As Kennedy (2007) notes*, it is important to think of knowledge in a given field as consisting of three layers. First, there are the primary studies that researchers conduct and publish. Second are the reviews of those studies that summarize and offer new interpretations built from and often extending beyond the original studies. Third, there are the perceptions, conclusions, opinion, and interpretations that are shared informally that become part of the lore of field. In composing a literature review, it is important to note that it is often this third layer of knowledge that is cited as "true" even though it often has only a loose relationship to the primary studies and secondary literature reviews.

Given this, while literature reviews are designed to provide an overview and synthesis of pertinent sources you have explored, there are several approaches to how they can be done, depending upon the type of analysis underpinning your study. Listed below are definitions of types of literature reviews:

Argumentative Review      This form examines literature selectively in order to support or refute an argument, deeply imbedded assumption, or philosophical problem already established in the literature. The purpose is to develop a body of literature that establishes a contrarian viewpoint. Given the value-laden nature of some social science research [e.g., educational reform; immigration control], argumentative approaches to analyzing the literature can be a legitimate and important form of discourse. However, note that they can also introduce problems of bias when they are used to to make summary claims of the sort found in systematic reviews.

Integrative Review      Considered a form of research that reviews, critiques, and synthesizes representative literature on a topic in an integrated way such that new frameworks and perspectives on the topic are generated. The body of literature includes all studies that address related or identical hypotheses. A well-done integrative review meets the same standards as primary research in regard to clarity, rigor, and replication.

Historical Review      Few things rest in isolation from historical precedent. Historical reviews are focused on examining research throughout a period of time, often starting with the first time an issue, concept, theory, phenomena emerged in the literature, then tracing its evolution within the scholarship of a discipline. The purpose is to place research in a historical context to show familiarity with state-of-the-art developments and to identify the likely directions for future research.

Methodological Review      A review does not always focus on what someone said [content], but how they said it [method of analysis]. This approach provides a framework of understanding at different levels (i.e. those of theory, substantive fields, research approaches and data collection and analysis techniques), enables researchers to draw on a wide variety of knowledge ranging from the conceptual level to practical documents for use in fieldwork in the areas of ontological and epistemological consideration, quantitative and qualitative integration, sampling, interviewing, data collection and data analysis, and helps highlight many ethical issues which we should be aware of and consider as we go through our study.

Systematic Review      This form consists of an overview of existing evidence pertinent to a clearly formulated research question, which uses pre-specified and standardized methods to identify and critically appraise relevant research, and to collect, report, and analyse data from the studies that are included in the review. Typically it focuses on a very specific empirical question, often posed in a cause-and-effect form, such as "To what extent does A contribute to B?"

Theoretical Review      The purpose of this form is to concretely examine the corpus of theory that has accumulated in regard to an issue, concept, theory, phenomena. The theoretical literature review help establish what theories already exist, the relationships between them, to what degree the existing theories have been investigated, and to develop new hypotheses to be tested. Often this form is used to help establish a lack of appropriate theories or reveal that current theories are inadequate for explaining new or emerging research problems. The unit of analysis can focus on a theoretical concept or a whole theory or framework.

* Kennedy, Mary M. "Defining a Literature."  Educational Researcher  36 (April 2007): 139-147.

All content in this section is from The Literature Review created by Dr. Robert Larabee USC

Robinson, P. and Lowe, J. (2015),  Literature reviews vs systematic reviews.  Australian and New Zealand Journal of Public Health, 39: 103-103. doi: 10.1111/1753-6405.12393

What's in the name? The difference between a Systematic Review and a Literature Review, and why it matters . By Lynn Kysh from University of Southern California

Systematic review or meta-analysis?

A  systematic review  answers a defined research question by collecting and summarizing all empirical evidence that fits pre-specified eligibility criteria.

A  meta-analysis  is the use of statistical methods to summarize the results of these studies.

Systematic reviews, just like other research articles, can be of varying quality. They are a significant piece of work (the Centre for Reviews and Dissemination at York estimates that a team will take 9-24 months), and to be useful to other researchers and practitioners they should have:

• clearly stated objectives with pre-defined eligibility criteria for studies
• explicit, reproducible methodology
• a systematic search that attempts to identify all studies
• assessment of the validity of the findings of the included studies (e.g. risk of bias)
• systematic presentation, and synthesis, of the characteristics and findings of the included studies

Not all systematic reviews contain meta-analysis. 

Meta-analysis is the use of statistical methods to summarize the results of independent studies. By combining information from all relevant studies, meta-analysis can provide more precise estimates of the effects of health care than those derived from the individual studies included within a review.  More information on meta-analyses can be found in  Cochrane Handbook, Chapter 9 .

A meta-analysis goes beyond critique and integration and conducts secondary statistical analysis on the outcomes of similar studies.  It is a systematic review that uses quantitative methods to synthesize and summarize the results.

An advantage of a meta-analysis is the ability to be completely objective in evaluating research findings.  Not all topics, however, have sufficient research evidence to allow a meta-analysis to be conducted.  In that case, an integrative review is an appropriate strategy. 

Some of the content in this section is from Systematic reviews and meta-analyses: step by step guide created by Kate McAllister.

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What is a literature review?

A literature review is an integrated analysis -- not just a summary-- of scholarly writings and other relevant evidence related directly to your research question.  That is, it represents a synthesis of the evidence that provides background information on your topic and shows a association between the evidence and your research question.

A literature review may be a stand alone work or the introduction to a larger research paper, depending on the assignment.  Rely heavily on the guidelines your instructor has given you.

Why is it important?

A literature review is important because it:

• Explains the background of research on a topic.
• Demonstrates why a topic is significant to a subject area.
• Discovers relationships between research studies/ideas.
• Identifies major themes, concepts, and researchers on a topic.
• Identifies critical gaps and points of disagreement.
• Discusses further research questions that logically come out of the previous studies.

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1. Choose a topic. Define your research question.

Your literature review should be guided by your central research question.  The literature represents background and research developments related to a specific research question, interpreted and analyzed by you in a synthesized way.

• Make sure your research question is not too broad or too narrow.  Is it manageable?
• Begin writing down terms that are related to your question. These will be useful for searches later.
• If you have the opportunity, discuss your topic with your professor and your class mates.

2. Decide on the scope of your review

How many studies do you need to look at? How comprehensive should it be? How many years should it cover? 

• This may depend on your assignment.  How many sources does the assignment require?

3. Select the databases you will use to conduct your searches.

Make a list of the databases you will search. 

Where to find databases:

• use the tabs on this guide
• Find other databases in the Nursing Information Resources web page
• More on the Medical Library web page
• ... and more on the Yale University Library web page

4. Conduct your searches to find the evidence. Keep track of your searches.

• Use the key words in your question, as well as synonyms for those words, as terms in your search. Use the database tutorials for help.
• Save the searches in the databases. This saves time when you want to redo, or modify, the searches. It is also helpful to use as a guide is the searches are not finding any useful results.
• Review the abstracts of research studies carefully. This will save you time.
• Use the bibliographies and references of research studies you find to locate others.
• Check with your professor, or a subject expert in the field, if you are missing any key works in the field.
• Ask your librarian for help at any time.
• Use a citation manager, such as EndNote as the repository for your citations. See the EndNote tutorials for help.

Review the literature

Some questions to help you analyze the research:

• What was the research question of the study you are reviewing? What were the authors trying to discover?
• Was the research funded by a source that could influence the findings?
• What were the research methodologies? Analyze its literature review, the samples and variables used, the results, and the conclusions.
• Does the research seem to be complete? Could it have been conducted more soundly? What further questions does it raise?
• If there are conflicting studies, why do you think that is?
• How are the authors viewed in the field? Has this study been cited? If so, how has it been analyzed?

Tips: 

• Review the abstracts carefully.  
• Keep careful notes so that you may track your thought processes during the research process.
• Create a matrix of the studies for easy analysis, and synthesis, across all of the studies.
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Literature Reviews

Steps in the literature review process.

• What is a literature review?
• Define your research question
• Determine inclusion and exclusion criteria
• Choose databases and search
• Review Results
• Synthesize Results
• Analyze Results
• Librarian Support
• Artificial Intelligence (AI) Tools
• You may need to some exploratory searching of the literature to get a sense of scope, to determine whether you need to narrow or broaden your focus
• Identify databases that provide the most relevant sources, and identify relevant terms (controlled vocabularies) to add to your search strategy
• Finalize your research question
• Think about relevant dates, geographies (and languages), methods, and conflicting points of view
• Conduct searches in the published literature via the identified databases
• Check to see if this topic has been covered in other discipline's databases
• Examine the citations of on-point articles for keywords, authors, and previous research (via references) and cited reference searching.
• Save your search results in a citation management tool (such as Zotero, Mendeley or EndNote)
• De-duplicate your search results
• Make sure that you've found the seminal pieces -- they have been cited many times, and their work is considered foundational 
• Check with your professor or a librarian to make sure your search has been comprehensive
• Evaluate the strengths and weaknesses of individual sources and evaluate for bias, methodologies, and thoroughness
• Group your results in to an organizational structure that will support why your research needs to be done, or that provides the answer to your research question  
• Develop your conclusions
• Are there gaps in the literature?
• Where has significant research taken place, and who has done it?
• Is there consensus or debate on this topic?
• Which methodological approaches work best?
• For example: Background, Current Practices, Critics and Proponents, Where/How this study will fit in 
• Organize your citations and focus on your research question and pertinent studies
• Compile your bibliography

Note: The first four steps are the best points at which to contact a librarian. Your librarian can help you determine the best databases to use for your topic, assess scope, and formulate a search strategy.

Videos Tutorials about Literature Reviews

This 4.5 minute video from Academic Education Materials has a Creative Commons License and a British narrator.

Recommended Reading

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A literature review is a discussion of the literature (aka. the "research" or "scholarship") surrounding a certain topic. A good literature review doesn't simply summarize the existing material, but provides thoughtful synthesis and analysis. The purpose of a literature review is to orient your own work within an existing body of knowledge. A literature review may be written as a standalone piece or be included in a larger body of work.

You can read more about literature reviews, what they entail, and how to write one, using the resources below. 

Am I the only one struggling to write a literature review?

Dr. Zina O'Leary explains the misconceptions and struggles students often have with writing a literature review. She also provides step-by-step guidance on writing a persuasive literature review.

An Introduction to Literature Reviews

Dr. Eric Jensen, Professor of Sociology at the University of Warwick, and Dr. Charles Laurie, Director of Research at Verisk Maplecroft, explain how to write a literature review, and why researchers need to do so. Literature reviews can be stand-alone research or part of a larger project. They communicate the state of academic knowledge on a given topic, specifically detailing what is still unknown.

This is the first video in a whole series about literature reviews. You can find the rest of the series in our SAGE database, Research Methods:

Videos covering research methods and statistics

Identify Themes and Gaps in Literature (with real examples) | Scribbr

Finding connections between sources is key to organizing the arguments and structure of a good literature review. In this video, you'll learn how to identify themes, debates, and gaps between sources, using examples from real papers.

4 Tips for Writing a Literature Review's Intro, Body, and Conclusion | Scribbr

While each review will be unique in its structure--based on both the existing body of both literature and the overall goals of your own paper, dissertation, or research--this video from Scribbr does a good job simplifying the goals of writing a literature review for those who are new to the process. In this video, you’ll learn what to include in each section, as well as 4 tips for the main body illustrated with an example.

• Literature Review This chapter in SAGE's Encyclopedia of Research Design describes the types of literature reviews and scientific standards for conducting literature reviews.
• UNC Writing Center: Literature Reviews This handout from the Writing Center at UNC will explain what literature reviews are and offer insights into the form and construction of literature reviews in the humanities, social sciences, and sciences.
• Purdue OWL: Writing a Literature Review The overview of literature reviews comes from Purdue's Online Writing Lab. It explains the basic why, what, and how of writing a literature review.

Organizational Tools for Literature Reviews

One of the most daunting aspects of writing a literature review is organizing your research. There are a variety of strategies that you can use to help you in this task. We've highlighted just a few ways writers keep track of all that information! You can use a combination of these tools or come up with your own organizational process. The key is choosing something that works with your own learning style.

Citation Managers

Citation managers are great tools, in general, for organizing research, but can be especially helpful when writing a literature review. You can keep all of your research in one place, take notes, and organize your materials into different folders or categories. Read more about citations managers here:

• Manage Citations & Sources

Concept Mapping

Some writers use concept mapping (sometimes called flow or bubble charts or "mind maps") to help them visualize the ways in which the research they found connects.

There is no right or wrong way to make a concept map. There are a variety of online tools that can help you create a concept map or you can simply put pen to paper. To read more about concept mapping, take a look at the following help guides:

• Using Concept Maps From Williams College's guide, Literature Review: A Self-guided Tutorial

Synthesis Matrix

A synthesis matrix is is a chart you can use to help you organize your research into thematic categories. By organizing your research into a matrix, like the examples below, can help you visualize the ways in which your sources connect. 

• Walden University Writing Center: Literature Review Matrix Find a variety of literature review matrix examples and templates from Walden University.
• Writing A Literature Review and Using a Synthesis Matrix An example synthesis matrix created by NC State University Writing and Speaking Tutorial Service Tutors. If you would like a copy of this synthesis matrix in a different format, like a Word document, please ask a librarian. CC-BY-SA 3.0
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• Last Updated: May 7, 2024 9:51 AM

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Research Methods: Literature Reviews

• Annotated Bibliographies
• Literature Reviews
• Scoping Reviews
• Systematic Reviews
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• Persuasive Arguments
• Subject Specific Methodology

A literature review involves researching, reading, analyzing, evaluating, and summarizing scholarly literature (typically journals and articles) about a specific topic. The results of a literature review may be an entire report or article OR may be part of a article, thesis, dissertation, or grant proposal. A literature review helps the author learn about the history and nature of their topic, and identify research gaps and problems.

Steps & Elements

Problem formulation

• Determine your topic and its components by asking a question
• Research: locate literature related to your topic to identify the gap(s) that can be addressed
• Read: read the articles or other sources of information
• Analyze: assess the findings for relevancy
• Evaluating: determine how the article are relevant to your research and what are the key findings
• Synthesis: write about the key findings and how it is relevant to your research

Elements of a Literature Review

• Summarize subject, issue or theory under consideration, along with objectives of the review
• Divide works under review into categories (e.g. those in support of a particular position, those against, those offering alternative theories entirely)
• Explain how each work is similar to and how it varies from the others
• Conclude which pieces are best considered in their argument, are most convincing of their opinions, and make the greatest contribution to the understanding and development of an area of research

Writing a Literature Review Resources

• How to Write a Literature Review From the Wesleyan University Library
• Write a Literature Review From the University of California Santa Cruz Library. A Brief overview of a literature review, includes a list of stages for writing a lit review.
• Literature Reviews From the University of North Carolina Writing Center. Detailed information about writing a literature review.
• Undertaking a literature review: a step-by-step approach Cronin, P., Ryan, F., & Coughan, M. (2008). Undertaking a literature review: A step-by-step approach. British Journal of Nursing, 17(1), p.38-43

Literature Review Tutorial

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Teaching and Research guides

Literature reviews.

• Introduction
• Plan your search
• Where to search
• Refine and update your search
• Finding grey literature
• Writing the review
• Referencing

Research methods overview

Finding literature on research methodologies, sage research methods online.

• Get material not at RMIT
• Further help

What are research methods?

Research methodology is the specific strategies, processes, or techniques utilised in the collection of information that is created and analysed.

The methodology section of a research paper, or thesis, enables the reader to critically evaluate the study’s validity and reliability by addressing how the data was collected or generated, and how it was analysed.

Types of research methods

There are three main types of research methods which use different designs for data collection.  

(1) Qualitative research

Qualitative research gathers data about lived experiences, emotions or behaviours, and the meanings individuals attach to them. It assists in enabling researchers to gain a better understanding of complex concepts, social interactions or cultural phenomena. This type of research is useful in the exploration of how or why things have occurred, interpreting events and describing actions.

Examples of qualitative research designs include:

• focus groups
• observations
• document analysis
• oral history or life stories  

(2) Quantitative research

Quantitative research gathers numerical data which can be ranked, measured or categorised through statistical analysis. It assists with uncovering patterns or relationships, and for making generalisations. This type of research is useful for finding out how many, how much, how often, or to what extent.

Examples of quantitative research designs include:

• surveys or questionnaires
• observation
• document screening
• experiments  

(3) Mixed method research

Mixed Methods research integrates both Qualitative research and Quantitative research. It provides a holistic approach combining and analysing the statistical data with deeper contextualised insights. Using Mixed Methods also enables triangulation, or verification, of the data from two or more sources.

Sometimes in your literature review, you might need to discuss and evaluate relevant research methodologies in order to justify your own choice of research methodology.

When searching for literature on research methodologies it is important to search across a range of sources. No single information source will supply all that you need. Selecting appropriate sources will depend upon your research topic.

Developing a robust search strategy will help reduce irrelevant results. It is good practice to plan a strategy before you start to search.

Search tips

(1) free text keywords.

Free text searching is the use of natural language words to conduct your search. Use selective free text keywords such as: phenomenological, "lived experience", "grounded theory", "life experiences", "focus groups", interview, quantitative, survey, validity, variance, correlation and statistical.

To locate books on your desired methodology, try LibrarySearch . Remember to use  refine  options such as books, ebooks, subject, and publication date.  

(2) Subject headings in Databases

Databases categorise their records using subject terms, or a controlled vocabulary (thesaurus). These subject headings may be useful to use, in addition to utilising free text keywords in a database search.

Subject headings will differ across databases, for example, the PubMed database uses 'Qualitative Research' whilst the CINHAL database uses 'Qualitative Studies.'  

(3) Limiting search results

Databases enable sets of results to be limited or filtered by specific fields, look for options such as Publication Type, Article Type, etc. and apply them to your search.  

(4) Browse the Library shelves

To find books on  research methods  browse the Library shelves at call number  001.42

• SAGE Research Methods Online SAGE Research Methods Online (SRMO) is a research tool supported by a newly devised taxonomy that links content and methods terms. It provides the most comprehensive picture available today of research methods (quantitative, qualitative and mixed methods) across the social and behavioural sciences.

SAGE Research Methods Overview  (2:07 min) by SAGE Publishing  ( YouTube ) 

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• Process: Literature Reviews
• Literature Review
• Managing Sources

Ask a Librarian

Does your assignment or publication require that you write a literature review? This guide is intended to help you understand what a literature is, why it is worth doing, and some quick tips composing one.

Understanding Literature Reviews

What is a literature review  .

Typically, a literature review is a written discussion that examines publications about  a particular subject area or topic. Depending on disciplines, publications, or authors a literature review may be: 

A summary of sources An organized presentation of sources A synthesis or interpretation of sources An evaluative analysis of sources

A Literature Review may be part of a process or a product. It may be:

A part of your research process A part of your final research publication An independent publication

Why do a literature review?

The Literature Review will place your research in context. It will help you and your readers:  

Locate patterns, relationships, connections, agreements, disagreements, & gaps in understanding Identify methodological and theoretical foundations Identify landmark and exemplary works Situate your voice in a broader conversation with other writers, thinkers, and scholars

The Literature Review will aid your research process. It will help you to:

Establish your knowledge Understand what has been said Define your questions Establish a relevant methodology Refine your voice Situate your voice in the conversation

What does a literature review look like?

The Literature Review structure and organization may include sections such as:  

An introduction or overview A body or organizational sub-divisions A conclusion or an explanation of significance

The body of a literature review may be organized in several ways, including:

Chronologically: organized by date of publication Methodologically: organized by type of research method used Thematically: organized by concept, trend, or theme Ideologically: organized by belief, ideology, or school of thought

• Find a focus
• Find models
• Review your target publication
• Track citations
• Read critically
• Manage your citations
• Ask friends, faculty, and librarians

Additional Sources

• Reviewing the literature. Project Planner.
• Literature Review: By UNC Writing Center
• PhD on Track
• CU Graduate Students Thesis & Dissertation Guidance
• CU Honors Thesis Guidance

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Home » Research Methodology – Types, Examples and writing Guide

Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

• Provide an overview of the research problem and the need for a research methodology section
• Outline the main research questions and objectives

II. Research Design

• Explain the research design chosen and why it is appropriate for the research question(s) and objectives
• Discuss any alternative research designs considered and why they were not chosen
• Describe the research setting and participants (if applicable)

III. Data Collection Methods

• Describe the methods used to collect data (e.g., surveys, interviews, observations)
• Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

• Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
• Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or software used for data analysis

V. Ethical Considerations

• Discuss any ethical issues that may arise from the research and how they were addressed
• Explain how informed consent was obtained (if applicable)
• Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

• Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

• Summarize the key aspects of the research methodology section
• Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

• Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
• Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
• Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
• Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
• Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
• Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
• Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
• Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

• To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
• To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
• To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
• To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
• To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
• To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
• To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
• To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
• To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

• To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
• To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
• To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
• To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
• To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

• Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
• Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
• Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
• Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
• Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
• Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
• Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
• Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

About the author

Muhammad Hassan

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What is a Literature Review?

Description.

A literature review, also called a review article or review of literature, surveys the existing research on a topic. The term "literature" in this context refers to published research or scholarship in a particular discipline, rather than "fiction" (like American Literature) or an individual work of literature. In general, literature reviews are most common in the sciences and social sciences.

Literature reviews may be written as standalone works, or as part of a scholarly article or research paper. In either case, the purpose of the review is to summarize and synthesize the key scholarly work that has already been done on the topic at hand. The literature review may also include some analysis and interpretation. A literature review is  not  a summary of every piece of scholarly research on a topic.

Why are literature reviews useful?

Literature reviews can be very helpful for newer researchers or those unfamiliar with a field by synthesizing the existing research on a given topic, providing the reader with connections and relationships among previous scholarship. Reviews can also be useful to veteran researchers by identifying potentials gaps in the research or steering future research questions toward unexplored areas. If a literature review is part of a scholarly article, it should include an explanation of how the current article adds to the conversation. (From: https://library.drake.edu/englit/criticism)

How is a literature review different from a research article?

Research articles: "are empirical articles that describe one or several related studies on a specific, quantitative, testable research question....they are typically organized into four text sections: Introduction, Methods, Results, Discussion." Source: https://psych.uw.edu/storage/writing_center/litrev.pdf)

Steps for Writing a Literature Review

1. Identify and define the topic that you will be reviewing.

The topic, which is commonly a research question (or problem) of some kind, needs to be identified and defined as clearly as possible.  You need to have an idea of what you will be reviewing in order to effectively search for references and to write a coherent summary of the research on it.  At this stage it can be helpful to write down a description of the research question, area, or topic that you will be reviewing, as well as to identify any keywords that you will be using to search for relevant research.

2. Conduct a Literature Search

Use a range of keywords to search databases such as PsycINFO and any others that may contain relevant articles.  You should focus on peer-reviewed, scholarly articles . In SuperSearch and most databases, you may find it helpful to select the Advanced Search mode and include "literature review" or "review of the literature" in addition to your other search terms.  Published books may also be helpful, but keep in mind that peer-reviewed articles are widely considered to be the “gold standard” of scientific research.  Read through titles and abstracts, select and obtain articles (that is, download, copy, or print them out), and save your searches as needed. Most of the databases you will need are linked to from the Cowles Library Psychology Research guide .

3. Read through the research that you have found and take notes.

Absorb as much information as you can.  Read through the articles and books that you have found, and as you do, take notes.  The notes should include anything that will be helpful in advancing your own thinking about the topic and in helping you write the literature review (such as key points, ideas, or even page numbers that index key information).  Some references may turn out to be more helpful than others; you may notice patterns or striking contrasts between different sources; and some sources may refer to yet other sources of potential interest.  This is often the most time-consuming part of the review process.  However, it is also where you get to learn about the topic in great detail. You may want to use a Citation Manager to help you keep track of the citations you have found. 

4. Organize your notes and thoughts; create an outline.

At this stage, you are close to writing the review itself.  However, it is often helpful to first reflect on all the reading that you have done.  What patterns stand out?  Do the different sources converge on a consensus?  Or not?  What unresolved questions still remain?  You should look over your notes (it may also be helpful to reorganize them), and as you do, to think about how you will present this research in your literature review.  Are you going to summarize or critically evaluate?  Are you going to use a chronological or other type of organizational structure?  It can also be helpful to create an outline of how your literature review will be structured.

5. Write the literature review itself and edit and revise as needed.

The final stage involves writing.  When writing, keep in mind that literature reviews are generally characterized by a  summary style  in which prior research is described sufficiently to explain critical findings but does not include a high level of detail (if readers want to learn about all the specific details of a study, then they can look up the references that you cite and read the original articles themselves).  However, the degree of emphasis that is given to individual studies may vary (more or less detail may be warranted depending on how critical or unique a given study was).   After you have written a first draft, you should read it carefully and then edit and revise as needed.  You may need to repeat this process more than once.  It may be helpful to have another person read through your draft(s) and provide feedback.

6. Incorporate the literature review into your research paper draft. (note: this step is only if you are using the literature review to write a research paper. Many times the literature review is an end unto itself).

After the literature review is complete, you should incorporate it into your research paper (if you are writing the review as one component of a larger paper).  Depending on the stage at which your paper is at, this may involve merging your literature review into a partially complete Introduction section, writing the rest of the paper around the literature review, or other processes.

These steps were taken from: https://psychology.ucsd.edu/undergraduate-program/undergraduate-resources/academic-writing-resources/writing-research-papers/writing-lit-review.html#6.-Incorporate-the-literature-r

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A Mystery or a Route? A Systematic Literature Review of Transcreation and Translation Studies

• He Zhu Universiti Putra Malaysia
• Lay Hoon Ang Universiti Putra Malaysia
• Muhammad Alif Redzuan Abdullah Universiti Putra Malaysia
• Hongxiang Zhu Universiti Putra Malaysia

Transcreation is an inter-cultural and inter-linguistic activity, which has obtained particular academic interest recently. However, few studies have reviewed the current status quo on transcreation systematically, although transcreation has been applied in various fields such as literature and advertising translation. In this study, a systematic literature review is conducted to shed light on this topic by identifying and analysing genres, foci, methods, and theories related to transcreation. The databases cover Scopus, Web of Science and CNKI from 1995 to 2023. A total of 61 publications were identified with PRISMA 2020. The qualitative findings showed that (1) most studies of transcreation focus on literary and advertising, while other fields of audiovisual, news and political, training and interpreting need to be explored; (2) research foci of transcreation strategies, culture and ideology gain more attention while transcreation process and evaluation, localization and persuasion have not been much examined; (3) content analysis is most frequently adopted while other qualitative methods are less used. Besides, there is a lack of using quantitative and mixed methods; (4) systematical functional linguistics is often used as a pointcut to examine strategies of transcreation. Besides, multimodal social semiotics is used to explore transcreation through texts, pictures and videos. Other linguistic theories and cross-disciplinary theories remain unexplored in transcreation. In conclusion, this study provides a review of transcreation and translation studies and reveals some research gaps that could enlighten future studies.

Author Biographies

He zhu, universiti putra malaysia.

Faculty of Modern Languages and Communication

Lay Hoon Ang, Universiti Putra Malaysia

Muhammad alif redzuan abdullah, universiti putra malaysia, hongxiang zhu, universiti putra malaysia.

Ai, Z. (2014). Mu Di Lun Zhi Dao Xia De Guang Gao Wen Ben Fan Yi Ce Lue Yan Jiu 目的论指导下的广告文本翻译策略研究 [On Translation Strategies of Advertising Text from the Perspective of Skopostheorie]. Journal of Jiangsu Normal University, 5(1), 90-93.

Al-Omar, N. (2020). Ideology in advertising: Some implications for transcreation into Arabic. Hikma, 19(1), 43-68. https://doi.org/10.21071/hikma.v19i1.11713

Benetello, C. (2018). When translation is not enough: Transcreation as a convention-defying practice. A practitioner’s perspective. The Journal of Specialised Translation, 29, 28–43. https://jostrans.soap2.ch/issue29/art_benetello.php . Retrieved on 01/02/2024.

Borodo, M., & Wood, K. (2023). The translation and transcreation of adventure comics. inTRAlinea, 1-14. https://www.intralinea.org/specials/article/2632.2024.01.025 . Retrieved on 21/02/2024.

Burnham, J. F. (2006). Scopus database: A review. Biomedical digital libraries, 3(1), 1-8. https://bio-diglib.biomedcentral.com/articles/10.1186/1742-5581-3-1 . Retrieved on 12/01/2024.

Carreira, O. (2021). Quality evaluation and workflows in transcreation: A social study. In M. D. Olvera-Lobo, J. Gutiérrez-Artacho, I. Rivera-Trigueros, & M. Díaz-Millón (Eds.), Innovative perspectives on corporate communication in the global world (pp. 177-194). IGI Global. https://doi.org/10.4018/978-1-7998-6799-9.ch010

Carreira, O. (2022). Is transcreation a service or a strategy? A social study into the perceptions of language professionals. Babel, 68(4), 498-516. https://doi.org/10.1075/babel.00277.car

Carreira, O. (2023). Surveying the economics of transcreation from the perspective of language professionals. Across Languages and Cultures, 24(1), 127-144. https://doi.org/10.1080/0907676X.2023.2214318

Chakravarty, R. (2021). Textual encounters: Tagore’s translations of medieval poetry. Translation Studies, 14(2), 167-184. https://doi.org/10.1080/14781700.2021.1909493

Chaume, F. (2018). Is audiovisual translation putting the concept of translation up against the ropes?. The Journal of Specialised Translation, 30, 84-104. https://jostrans.soap2.ch/issue30/art_chaume.php . Retrieved on 12/12/2023.

Corrius, M., & Espasa, E. (2023). Is transcreation another way of translating? Subtitling Estrella Damm’s advertising campaigns into English. Íkala, Revista de Lenguaje y Cultura, 28(2), 1-21. https://doi.org/10.17533/udea.ikala.v28n2a09

Crowe, M. (2013). Crowe Critical Appraisal Tool (CCAT) User Guide. Conchra House.

Díaz-Millón, M., & Olvera-Lobo, M. D. (2023). Towards a definition of transcreation: A systematic literature review. Perspectives, 31(2), 347-364. https://doi.org/10.1080/0907676X.2021.2004177

Fang, J., & Song, Z. (2014). Exploring the Chinese translation of Australian health product labels: Are they selling the same thing?. Cultus, (7), 72-95. https://iris.unipa.it/retrieve/handle/10447/130535/197987/cultus#page=72 . Retrieved on 03/01/2024.

Fernández, Costales, A. (2014). Video game localisation: adapting superheroes to different cultures. Quaderns: revista de traducció, (21), 0225-239. https://ddd.uab.cat/pub/quaderns/quaderns_a2014n21/quaderns_a2014n21p225.pdf . Retrieved on 11/11/2023.

Gaballo, V. (2012). Exploring the boundaries of transcreation in specialized translation. ESP Across Cultures, 9(1), 95-113. https://core.ac.uk/download/pdf/55276390.pdf . Retrieved on 15/02/2024.

Galván, B. E. (2019). Translation depends on the artist: Two approaches to the illustrations of James and the Giant Peach through the prism of intersemiotic translation. Babel, 65(1), 61-80. https://doi.org/10.1075/babel.00074.ech

Gao, J., & Hua, Y. (2021). On the English translation strategy of science fiction from Humboldt's linguistic worldview—taking the English translation of Three-Body problem as an example. Theory and Practice in Language Studies, 11(2), 186-190. http://dx.doi.org/10.17507/tpls.1102.11

Ho, N. M. (2021). Transcreation in marketing: A corpus-based study of persuasion in optional shifts from English to Chinese. Perspectives, 29(3), 426-438. https://doi.org/10.1080/0907676X.2020.1778046

Hu, H., & Wu, J. (2020). Fa Lv Shu Yu De Biao Zhun Hua Ying Yi Tan Jiu 法律术语的标准化英译探究 [Research on standardized English translation of legal terms]. Chinese Science & Technology Translators Journal, 33(3), 35-38.

Husa, J. (2017). Translating legal language and comparative law. International Journal for the Semiotics of Law, 30, 261-272. https://doi.org/10.1007/s11196-016-9490-9

Kassawat, M. (2020). Decoding transcreation in corporate website localization into Arabic. The Journal of Internationalization and Localization, 7(1-2), 69-94. https://doi.org/10.1075/jial.20010.kas

Katan, D. (2016). Translation at the cross-roads: Time for the transcreational turn?. Perspectives, 24(3), 365-381. http://dx.doi.org/10.1080/0907676X.2015.1016049

Lal, P. (1957, 1964). Great Sanskrit plays in modern translation. New Directions.

Li, M. (2020). A systematic review of the research on Chinese character teaching and learning. Frontiers of Education in China, 15(1), 39-72. https://doi.org/10.1007/s11516-020-0003-y

Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P., ... & Moher, D. (2009). The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. Annals of internal medicine, 151(4), W-65. https://doi.org/10.1371/journal.pmed.1000100

Malenkina, N., & Ivanov, S. (2018). A linguistic analysis of the official tourism websites of the seventeen Spanish Autonomous Communities. Journal of Destination Marketing & Management, 9, 204-233. https://doi.org/10.1016/j.jdmm.2018.01.007

Malenova, E.D. (2018). Creative practices in translation of transmedia projects. Journal of Siberian Federal University. Humanities & Social Sciences, 5(11), 775–786. https://doi.org/10.17516/1997-1370-0269

Mangiron, C., & O’Hagan, M. (2006). Game localisation: Unleashing imagination with ‘restricted’ translation. The Journal of Specialised Translation, 6(1), 10-21.

Morón, M., & Calvo, E. (2018). Introducing transcreation skills in translator training contexts: A situated project-based approach. The Journal of Specialised Translation, 29, 126-148. https://jostrans.soap2.ch/issue29/art_moron.php . Retrieved on 29/12/2023.

Nam, J., & Jung, Y. (2022). Exploring fans’ participation in digital media: Transcreation of webtoons. Telecommunications Policy, 46(10), 1-14. https://doi.org/10.1016/j.telpol.2022.102407

Nishimura, A., & Itoh, T. (2020). Implementation and experiments for interactive lyrics transcreation system. Visual Computing for Industry, Biomedicine, and Art, 3(1), 1-16. https://doi.org/10.1186/s42492-020-00053-x

Pal, B., & Bhattacharjee, P. (2022). What is translated; what is not translated: Studying the translation process of select Bengali Dalit short stories. The Translator, 28(1), 1-14. https://doi.org/10.1080/13556509.2021.1894763

Pedersen, D. (2014). Exploring the concept of transcreation–transcreation as “more than translation”. Cultus: The Journal of intercultural mediation and communication, 7(1), 57-71.

Petrović, K. (2023). Journalistic transcreation of news agency articles from English into Serbian: Associated Press and Reuters articles in Blic and N1 online portals. ELOPE: English Language Overseas Perspectives and Enquiries, 20(1), 67-88. https://doi.org/10.4312/elope.20.1.67-88

Piñeiro, B., Díaz, D. R., Monsalve, L. M., Martínez, Ú., Meade, C. D., Meltzer, L. R., Karen O. Brandon., Unrod M., Brandon T. H., Simmons V. N. (2018). Systematic transcreation of self-help smoking cessation materials for Hispanic/Latino smokers: Improving cultural relevance and acceptability. Journal of health communication, 23(4), 350-359. https://doi.org/10.1080/10810730.2018.1448487

Ray R. & N. Kelly. (2010). Reaching New Markets through Transcreation. Common Sense Advisory.

Reiss, K. (2000). Type, kind and individuality of text: Decision making in translation. In Venuti, Lawrence (ed). The translation Studies Reader, Routledge, 160-171.

Rike, S. M. (2013). Bilingual corporate websites-from translation to transcreation? The Journal of Specialised Translation, 20, 68-85. https://jostrans.soap2.ch/issue20/art_rike.php . Retrieved on 10/02/2024.

Risku, H., Pichler, T., & Wieser, V. (2017). Transcreation as a translation service: Process requirements and client expectations. Across Languages and Cultures, 18(1), 53-77. https://doi.org/10.1556/084.2017.18.1.3

Simmons, V. N., Quinn, G., Litvin, E. B., Rojas, A., Jimenez, J., Castro, E., Meade C. D., Brandon, T. H. (2011). Transcreation of validated smoking relapse-prevention booklets for use with Hispanic populations. Journal of health care for the poor and underserved, 22(3), 886. https://doi.org/10.1353/hpu.2011.0091

Steinhardt, I., Schneijderberg, C., Götze, N., Baumann, J., & Krücken, G. (2017). Mapping the quality assurance of teaching and learning in higher education: the emergence of a specialty?. Higher Education, 74, 221-237. https://doi.org/ 10.1007/s10734-016-0045-5

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Wang, L., Ang, L. H., & Halim, H. A. (2021). What is real transcreation? A case study of transcreation in corporate communication writing. International Journal of Academic Research in Business and Social Science, 11(12), 1150-1165. http://dx.doi.org/10.6007/IJARBSS/v11-i12/11686

Wu, H. (2022). Chuan Bo Xue Shi Jiao Xia Xu Yuan Chong Tang Shi Dian Gu Ying Yi Yan Jiu 传播学视角下许渊冲唐诗典故英译研究 [A study on the English translation of Xu Yuanchong’s Tang poetry allusions from the perspective of communication studies]. Xin Wen Ai Hao Zhe, 1, 66-69.

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Zhu, L., Ang, L. H., & Mansor, N. S. (2023). Manipulation of female stereotypes in Chinese translations of fragrance product descriptions. Theory and Practice in Language Studies, 13(1), 227-236. https://doi.org/10.17507/tpls.1301.26

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Reviewing literature for research: Doing it the right way

Shital amin poojary.

Department of Dermatology, K J Somaiya Medical College, Mumbai, Maharashtra, India

Jimish Deepak Bagadia

In an era of information overload, it is important to know how to obtain the required information and also to ensure that it is reliable information. Hence, it is essential to understand how to perform a systematic literature search. This article focuses on reliable literature sources and how to make optimum use of these in dermatology and venereology.

INTRODUCTION

A thorough review of literature is not only essential for selecting research topics, but also enables the right applicability of a research project. Most importantly, a good literature search is the cornerstone of practice of evidence based medicine. Today, everything is available at the click of a mouse or at the tip of the fingertips (or the stylus). Google is often the Go-To search website, the supposed answer to all questions in the universe. However, the deluge of information available comes with its own set of problems; how much of it is actually reliable information? How much are the search results that the search string threw up actually relevant? Did we actually find what we were looking for? Lack of a systematic approach can lead to a literature review ending up as a time-consuming and at times frustrating process. Hence, whether it is for research projects, theses/dissertations, case studies/reports or mere wish to obtain information; knowing where to look, and more importantly, how to look, is of prime importance today.

Literature search

Fink has defined research literature review as a “systematic, explicit and reproducible method for identifying, evaluating, and synthesizing the existing body of completed and recorded work produced by researchers, scholars and practitioners.”[ 1 ]

Review of research literature can be summarized into a seven step process: (i) Selecting research questions/purpose of the literature review (ii) Selecting your sources (iii) Choosing search terms (iv) Running your search (v) Applying practical screening criteria (vi) Applying methodological screening criteria/quality appraisal (vii) Synthesizing the results.[ 1 ]

This article will primarily concentrate on refining techniques of literature search.

Sources for literature search are enumerated in Table 1 .

Sources for literature search

PubMed is currently the most widely used among these as it contains over 23 million citations for biomedical literature and has been made available free by National Center for Biotechnology Information (NCBI), U.S. National Library of Medicine. However, the availability of free full text articles depends on the sources. Use of options such as advanced search, medical subject headings (MeSH) terms, free full text, PubMed tutorials, and single citation matcher makes the database extremely user-friendly [ Figure 1 ]. It can also be accessed on the go through mobiles using “PubMed Mobile.” One can also create own account in NCBI to save searches and to use certain PubMed tools.

PubMed home page showing location of different tools which can be used for an efficient literature search

Tips for efficient use of PubMed search:[ 2 , 3 , 4 ]

Use of field and Boolean operators

When one searches using key words, all articles containing the words show up, many of which may not be related to the topic. Hence, the use of operators while searching makes the search more specific and less cumbersome. Operators are of two types: Field operators and Boolean operators, the latter enabling us to combine more than one concept, thereby making the search highly accurate. A few key operators that can be used in PubMed are shown in Tables ​ Tables2 2 and ​ and3 3 and illustrated in Figures ​ Figures2 2 and ​ and3 3 .

Field operators used in PubMed search

Boolean operators used in PubMed search

PubMed search results page showing articles on donovanosis using the field operator [TIAB]; it shows all articles which have the keyword “donovanosis” in either title or abstract of the article

PubMed search using Boolean operators ‘AND’, ‘NOT’; To search for articles on treatment of lepra reaction other than steroids, after clicking the option ‘Advanced search’ on the home page, one can build the search using ‘AND’ option for treatment and ‘NOT’ option for steroids to omit articles on steroid treatment in lepra reaction

Use of medical subject headings terms

These are very specific and standardized terms used by indexers to describe every article in PubMed and are added to the record of every article. A search using MeSH will show all articles about the topic (or keywords), but will not show articles only containing these keywords (these articles may be about an entirely different topic, but still may contain your keywords in another context in any part of the article). This will make your search more specific. Within the topic, specific subheadings can be added to the search builder to refine your search [ Figure 4 ]. For example, MeSH terms for treatment are therapy and therapeutics.

PubMed search using medical subject headings (MeSH) terms for management of gonorrhea. Click on MeSH database ( Figure 1 ) →In the MeSH search box type gonorrhea and click search. Under the MeSH term gonorrhea, there will be a list of subheadings; therapy, prevention and control, click the relevant check boxes and add to search builder →Click on search →All articles on therapy, prevention and control of gonorrhea will be displayed. Below the subheadings, there are two options: (1) Restrict to medical subject headings (MeSH) major topic and (2) do not include MeSH terms found below this term in the MeSH hierarchy. These can be used to further refine the search results so that only articles which are majorly about treatment of gonorrhea will be displayed

Two additional options can be used to further refine MeSH searches. These are located below the subheadings for a MeSH term: (1) Restrict to MeSH major topic; checking this box will retrieve articles which are majorly about the search term and are therefore, more focused and (2) Do not include MeSH terms found below this term in the MeSH hierarchy. This option will again give you more focused articles as it excludes the lower specific terms [ Figure 4 ].

Similar feature is available with Cochrane library (also called MeSH), EMBASE (known as EMTREE) and PsycINFO (Thesaurus of Psychological Index Terms).

Saving your searches

Any search that one has performed can be saved by using the ‘Send to’ option and can be saved as a simple word file [ Figure 5 ]. Alternatively, the ‘Save Search’ button (just below the search box) can be used. However, it is essential to set up an NCBI account and log in to NCBI for this. One can even choose to have E-mail updates of new articles in the topic of interest.

Saving PubMed searches. A simple option is to click on the dropdown box next to ‘Send to’ option and then choose among the options. It can be saved as a text or word file by choosing ‘File’ option. Another option is the “Save search” option below the search box but this will require logging into your National Center for Biotechnology Information account. This however allows you to set up alerts for E-mail updates for new articles

Single citation matcher

This is another important tool that helps to find the genuine original source of a particular research work (when few details are known about the title/author/publication date/place/journal) and cite the reference in the most correct manner [ Figure 6 ].

Single citation matcher: Click on “Single citation matcher” on PubMed Home page. Type available details of the required reference in the boxes to get the required citation

Full text articles

In any search clicking on the link “free full text” (if present) gives you free access to the article. In some instances, though the published article may not be available free, the author manuscript may be available free of charge. Furthermore, PubMed Central articles are available free of charge.

Managing filters

Filters can be used to refine a search according to type of article required or subjects of research. One can specify the type of article required such as clinical trial, reviews, free full text; these options are available on a typical search results page. Further specialized filters are available under “manage filters:” e.g., articles confined to certain age groups (properties option), “Links” to other databases, article specific to particular journals, etc. However, one needs to have an NCBI account and log in to access this option [ Figure 7 ].

Managing filters. Simple filters are available on the ‘search results’ page. One can choose type of article, e.g., clinical trial, reviews etc. Further options are available in the “Manage filters” option, but this requires logging into National Center for Biotechnology Information account

The Cochrane library

Although reviews are available in PubMed, for systematic reviews and meta-analysis, Cochrane library is a much better resource. The Cochrane library is a collection of full length systematic reviews, which can be accessed for free in India, thanks to Indian Council of Medical Research renewing the license up to 2016, benefitting users all over India. It is immensely helpful in finding detailed high quality research work done in a particular field/topic [ Figure 8 ].

Cochrane library is a useful resource for reliable, systematic reviews. One can choose the type of reviews required, including trials

An important tool that must be used while searching for research work is screening. Screening helps to improve the accuracy of search results. It is of two types: (1) Practical: To identify a broad range of potentially useful studies. Examples: Date of publication (last 5 years only; gives you most recent updates), participants or subjects (humans above 18 years), publication language (English only) (2) methodological: To identify best available studies (for example, excluding studies not involving control group or studies with only randomized control trials).

Selecting the right quality of literature is the key to successful research literature review. The quality can be estimated by what is known as “The Evidence Pyramid.” The level of evidence of references obtained from the aforementioned search tools are depicted in Figure 9 . Systematic reviews obtained from Cochrane library constitute level 1 evidence.

Evidence pyramid: Depicting the level of evidence of references obtained from the aforementioned search tools

Thus, a systematic literature review can help not only in setting up the basis of a good research with optimal use of available information, but also in practice of evidence-based medicine.

Source of Support: Nil.

Conflict of Interest: None declared.

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AI and ethics: Investigating the first policy responses of higher education institutions to the challenge of generative AI

• Attila Dabis   ORCID: orcid.org/0000-0003-4924-7664 1 &
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This article addresses the ethical challenges posed by generative artificial intelligence (AI) tools in higher education and explores the first responses of universities to these challenges globally. Drawing on five key international documents from the UN, EU, and OECD, the study used content analysis to identify key ethical dimensions related to the use of generative AI in academia, such as accountability, human oversight, transparency, or inclusiveness. Empirical evidence was compiled from 30 leading universities ranked among the top 500 in the Shanghai Ranking list from May to July 2023, covering those institutions that already had publicly available responses to these dimensions in the form of policy documents or guidelines. The paper identifies the central ethical imperative that student assignments must reflect individual knowledge acquired during their education, with human individuals retaining moral and legal responsibility for AI-related wrongdoings. This top-down requirement aligns with a bottom-up approach, allowing instructors flexibility in determining how they utilize generative AI especially large language models in their own courses. Regarding human oversight, the typical response identified by the study involves a blend of preventive measures (e.g., course assessment modifications) and soft, dialogue-based sanctioning procedures. The challenge of transparency induced the good practice of clear communication of AI use in course syllabi in the first university responses examined by this study.

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Introduction.

The competition in generative artificial intelligence (AI) ignited by the arrival of ChatGPT, the conversational platform based on a large language model (LLM) in late November 2022 (OpenAI, 2022 ) had a shocking effect even on those who are not involved in the industry (Rudolph et al. 2023 ). Within four months, on 22 March 2023, an open letter was signed by several hundred IT professionals, corporate stakeholders, and academics calling on all AI labs to immediately pause the training of AI systems more powerful than GPT-4 (i.e., those that may trick a human being into believing it is conversing with a peer rather than a machine) for at least six months (Future of Life Institute, 2023 ).

Despite these concerns, competition in generative AI and LLMs does not seem to lose momentum, forcing various social systems to overcome the existential distress they might feel about the changes and the uncertainty of what the future may bring (Roose, 2023 ). Organisations and individuals from different sectors of the economy and various industries are looking for adaptive strategies to accommodate the emerging new normal. This includes lawmakers, international organisations, employers, and employees, as well as academic and higher education institutions (Ray, 2023 ; Wach et al. 2023 ). This fierce competition generates gaps in real-time in everyday and academic life, the latter of which is also trying to make sense of the rapid technological advancement and its effects on university-level education (Perkins, 2023 ). Naturally, these gaps can only be filled, and relevant questions answered much slower by academia, making AI-related research topics timely.

This article aims to reduce the magnitude of these gaps and is intended to help leaders, administrators, teachers, and students better understand the ramifications of AI tools on higher education institutions. It will do so by providing a non-exhaustive snapshot of how various universities around the world responded to generative AI-induced ethical challenges in their everyday academic lives within six-eights months after the arrival of ChatGPT. Thus, the research had asked what expectations and guidelines the first policies introduced into existing academic structures to ensure the informed, transparent, responsible and ethical use of the new tools of generative AI (henceforth GAI) by students and teachers. Through reviewing and evaluating first responses and related difficulties the paper helps institutional decision-makers to create better policies to address AI issues specific to academia. The research reported here thus addressed actual answers to the question of what happened at the institutional (policy) level as opposed to what should happen with the use of AI in classrooms. Based on such a descriptive overview, one may contemplate normative recommendations and their realistic implementability.

Given the global nature of the study’s subject matter, the paper presents examples from various continents. Even though it was not yet a widespread practice to adopt separate, AI-related guidelines, the research focused on universities that had already done so quite early. Furthermore, as best practices most often accrue from the highest-ranking universities, the analysis only considered higher education institutions that were represented among the top 500 universities in the Shanghai Ranking list (containing 3041 Universities at the time), a commonly used source to rank academic excellence. Footnote 1 The main sources of this content analysis are internal documents (such as Codes of Ethics, Academic Regulations, Codes of Practice and Procedure, Guidelines for Students and Teachers or similar policy documents) from those institutions whose response to the GAI challenge was publicly accessible.

The investigation is organised around AI-related ethical dilemmas as concluded from relevant international documents, such as the instruments published by the UN, the EU, and the OECD (often considered soft law material). Through these sources, the study inductively identifies the primary aspects that these AI guidelines mention and can be connected to higher education. Thus it only contains concise references to the main ethical implications of the manifold pedagogical practices in which AI tools can be utilised in the classroom. The paper starts with a review of the challenges posed by AI technology to higher education with special focus on ethical dilemmas. Section 3 covers the research objective and the methodology followed. Section 4 presents the analysis of the selected international documents and establishes a list of key ethical principles relevant in HE contexts and in parallel presents the analysis of the examples distilled from the institutional policy documents and guidelines along that dimension. The paper closes with drawing key conclusions as well as listing limitations and ideas for future research.

Generative AI and higher education: Developments in the literature

General ai-related challenges in the classroom from a historical perspective.

Jacque Ellul fatalistically wrote already in 1954 that the “infusion of some more or less vague sentiment of human welfare” cannot fundamentally alter technology’s “rigorous autonomy”, bringing him to the conclusion that “technology never observes the distinction between moral and immoral use” (Ellul, 1964 , p. 97). Footnote 2 Jumping ahead nearly six decades, the above quote comes to the fore, among others, when evaluating the moral and ethical aspects of the services offered by specific software programs, like ChatGPT. While they might be trained to give ethical answers, these moral barriers can be circumvented by prompt injection (Blalock, 2022 ), or manipulated with tricks (Alberti, 2022 ), so generative AI platforms can hardly be held accountable for the inaccuracy of their responses Footnote 3 or how the physical user who inserted a prompt will make use of the output. Indeed, the AI chatbot is now considered to be a potentially disruptive technology in higher education practices (Farazouli et al. 2024 ).

Educators and educational institution leaders have from the beginning sought solutions on how “to use a variety of the strategies and technologies of the day to help their institutions adapt to dramatically changing social needs” (Miller, 2023 , p. 3). Education in the past had always had high hopes for applying the latest technological advances (Reiser, 2001 ; Howard and Mozejko, 2015 ), including the promise of providing personalised learning or using the latest tools to create and manage courses (Crompton and Burke, 2023 ).

The most basic (and original) educational settings include three components: the blackboard with chalk, the instructor, and textbooks as elementary “educational technologies” at any level (Reiser, 2001 ). Beyond these, one may talk about “educational media” which, once digital technology had entered the picture, have progressed from Computer Based Learning to Learning Management Systems to the use of the Internet, and lately to online shared learning environments with various stages in between including intelligent tutoring system, Dialogue-based Tutoring System, and Exploratory Learning Environment and Artificial Intelligence (Paek and Kim, 2021 ). And now the latest craze is about the generative form of AI often called conversational chatbot (Rudolph et al. 2023 ).

The above-mentioned promises appear to be no different in the case of using generative AI tools in education (Baskara, 2023a ; Mhlanga, 2023 ; Yan et al. 2023 ). The general claim is that GAI chatbots have transformative potential in HE (Mollick and Mollick, 2022 ; Ilieva et al. 2023 ). It is further alleged, that feedback mechanisms supposedly provided by GAI can be used to provide personalised guidance to students (Baskara, 2023b ). Some argue, that “AI education should be expanded and improved, especially by presenting realistic use cases and the real limitations of the technology, so that students are able to use AI confidently and responsibly in their professional future” (Almaraz-López et al. 2023 , p. 1). It is still debated whether the hype is justified, yet the question still remains, how to address the issues arising in the wake of the educational application of GAI tools (Ivanov, 2023 ; Memarian and Doleck, 2023 ).

Generative AI tools, such as their most-known representative, ChatGPT impact several areas of learning and teaching. From the point of view of students, chatbots may help with so-called Self-Regulated or Self-Determined Learning (Nicol and Macfarlane‐Dick, 2006 ; Baskara, 2023b ), where students either dialogue with chatbots or AI help with reviewing student work, even correcting it and giving feedback (Uchiyama et al. 2023 ). There are innovative ideas on how to use AI to support peer feedback (Bauer et al. 2023 ). Some consider that GAI can provide adaptive and personalised environments (Qadir, 2023 ) and may offer personalised tutoring (see, for example, Limo et al. ( 2023 ) on ChatGPT as a virtual tutor for personalized learning experiences). Furthermore, Yan et al. ( 2023 ) lists nine different categories of educational tasks that prior studies have attempted to automate using LLMs: Profiling and labelling (various educational or related content), Detection, Assessment and grading, Teaching support (in various educational and communication activities), Prediction, Knowledge representation, Feedback, Content generation (outline, questions, cases, etc.), Recommendation.

From the lecturers’ point of view, one of the most argued impacts is that assessment practices need to be revisited (Chaudhry et al. 2023 ; Gamage et al. 2023 ; Lim et al. 2023 ). For example, ChatGPT-written responses to exam questions may not be distinguished from student-written answers (Rudolph et al. 2023 ; Farazouli et al. 2024 ). Furthermore, essay-type works are facing special challenges (Sweeney, 2023 ). On the other hand, AI may be utilised to automate a range of educational tasks, such as test question generation, including open-ended questions, test correction, or even essay grading, feedback provision, analysing student feedback surveys, and so on (Mollick and Mollick, 2022 ; Rasul et al. 2023 ; Gimpel et al. 2023 ).

There is no convincing evidence, however, that either lecturers or dedicated tools are able to distinguish AI-written and student-written text with high enough accuracy that can be used to prove unethical behaviour in all cases (Akram, 2023 ). This led to concerns regarding the practicality and ethicality of such innovations (Yan et al. 2023 ). Indeed, the appearance of ChatGPT in higher education has reignited the (inconclusive) debate on the potential and risks associated with AI technologies (Ray, 2023 ; Rudolph et al. 2023 ).

When new technologies appear in or are considered for higher education, debates about their claimed advantages and potential drawbacks heat up as they are expected to disrupt traditional practices and require teachers to adapt to their potential benefits and drawbacks (as collected by Farrokhnia et al. 2023 ). One key area of such debates is the ethical issues raised by the growing accessibility of generative AI and discursive chatbots.

Key ethical challenges posed by AI in higher education

Yan et al. ( 2023 ), while investigating the practicality of AI in education in general, also consider ethicality in the context of educational technology and point out that related debates over the last decade (pre-ChatGPT, so to say), mostly focused on algorithmic ethics, i.e. concerns related to data mining and using AI in learning analytics. At the same time, the use of AI by teachers or, especially, by students has received less attention (or only under the scope or traditional human ethics). However, with the arrival of generative AI chatbots (such as ChatGPT), the number of publications about their use in higher education grew rapidly (Rasul et al. 2023 ; Yan et al. 2023 ).

The study by Chan ( 2023 ) offers a (general) policy framework for higher education institutions, although it focuses on one location and is based on the perceptions of students and teachers. While there are studies that collect factors to be considered for the ethical use of AI in HE, they appear to be restricted to ChatGPT (see, for example, Mhlanga ( 2023 )). Mhlanga ( 2023 ) presents six factors: respect for privacy, fairness, and non-discrimination, transparency in the use of ChatGPT, responsible use of AI (including clarifying its limitations), ChatGPT is not a substitute for human teachers, and accuracy of information. The framework by Chan ( 2023 ) is aimed at creating policies to teach students about GAI and considers three dimensions: pedagogical, governance, and operational. Within those dimensions, ten key areas identified covering ethical concerns such as academic integrity versus academic misconduct and related ethical dilemmas (e.g. cheating or plagiarism), data privacy, transparency, accountability and security, equity in access to AI technologies, critical AI literacy, over-reliance on AI technologies (not directly ethical), responsible use of AI (in general), competencies impeded by AI (such as leadership and teamwork). Baskara ( 2023b ), while also looking at ChatGPT only, considers the following likely danger areas: privacy, algorithmic bias issues, data security, and the potential negative impact of ChatGPT on learners’ autonomy and agency, The paper also questions the possible negative impact of GAI on social interaction and collaboration among learners. Although Yan et al. ( 2023 ) considers education in general (not HE in particular) during its review of 118 papers published since 2017 on the topic of AI ethics in education, its list of areas to look at is still relevant: transparency (of the models used), privacy (related to data collection and use by AI tools), equality (such as availability of AI tools in different languages), and beneficence (e.g. avoiding bias and avoiding biased and toxic knowledge from training data). While systematically reviewing recent publications about AI’s “morality footprint” in higher education, Memarian and Doleck ( 2023 ) consider the Fairness, Accountability, Transparency, and Ethics (FATE) approach as their framework of analyses. They note that “Ethics” appears to be the most used term as it serves as a general descriptor, while the other terms are typically only used in their descriptive sense, and their operationalisation is often lacking in related literature.

Regarding education-related data analytics, Khosravi et al. ( 2022 ) argue that educational technology that involves AI should consider accountability, explainability, fairness, interpretability and safety as key ethical concerns. Ferguson et al. ( 2016 ) also looked at learning analytics solutions using AI and warned of potential issues related to privacy, beneficence, and equality. M.A. Chaudhry et al. ( 2022 ) emphasise that enhancing the comprehension of stakeholders of a new educational AI system is the most important task, which requires making all information and decision processes available to those affected, therefore the key concern is related to transparency according to their arguments.

As such debates continue, it is difficult to identify an established definition of ethical AI in HE. It is clear, however, that the focus should not be on detecting academic misconduct (Rudolph et al. 2023 ). Instead, practical recommendations are required. This is especially true as even the latest studies focus mostly on issues related to assessment practices (Chan, 2023 ; Farazouli et al. 2024 ) and often limit their scope to ChatGPT (Cotton et al. 2024 ) (this specific tool still dominates discourses of LLMs despite the availability of many other solutions since its arrival). At the same time, the list of issues addressed appears to be arbitrary, and most publications do not look at actual practices on a global scale. Indeed, reviews of actual current practices of higher education institutions are rare, and this aspect is not yet the focus of recent HE AI ethics research reports.

As follows from the growing literature and the debate shaping up about the implications of using GAI tools in HE, there was a clear need for a systematic review of how first responses in actual academic policies and guidelines in practice have represented and addressed known ethical principles.

Research objective and methodology

In order to contribute to the debate on the impact of GAI on HE, this study aimed to review how leading institutions had reacted to the arrival of generative AI (such as ChatGPT) and what policies or institutional guidelines they have put in place shortly after. The research intended to understand whether key ethical principles were reflected in the first policy responses of HE institutions and, if yes, how they were handled.

As potential principles can diverge and could be numerous, as well as early guidelines may cover wide areas, the investigation is intended to be based on a few broad categories instead of trying to manage a large set of ideals and goals. To achieve this objective, the research was executed in three steps:

It was started with identifying and collecting general ethical ideals, which were then translated and structured for the context of higher education. A thorough content analysis was performed with the intention to put emphasis on positive values instead of simply focusing on issues or risks and their mitigation.

Given those positive ideals, this research collected actual examples of university policies and guidelines already available: this step was executed from May to July 2023 to find early responses addressing such norms and principles developed by leading HE institutions.

The documents identified were then analysed to understand how such norms and principles had been addressed by leading HE institutions.

As a result, this research managed to highlight and contrast differing practical views, and the findings raise awareness about the difficulties of creating relevant institutional policies. The research considered the ethics of using GAI and not expectations towards their development. The next two sections provide details of the two steps.

Establishing ethical principles for higher education

While the review of relevant ethical and HE literature (as presented above) was not fully conclusive, it highlighted the importance and need for some ideals specific to HE. Therefore, as a first step, this study sought to find highly respected sources of such ethical dimensions by executing a directed content analysis of relevant international regulatory and policy recommendations.

In order to establish what key values and ideas drive the formation of future AI regulations in general, Corrêa et al. ( 2023 ) investigated 200 publications discussing governance policies and ethical guidelines for using AI as proposed by various organisations (including national governments and institutions, civil society and academic organisations, private companies, as well as international bodies). The authors were also interested in whether there are common patterns or missing ideals and norms in this extensive set of proposals and recommendations. As the research was looking for key principles and normative attributes that could form a common ground for the comparison of HE policies, this vast set of documents was used to identify internationally recognised bodies that have potential real influence in this arena and decided to consider the guidelines and recommendations they have put forward for the ethical governance of AI. Therefore, for the purpose of this study, the following sources were selected (some organisations, such as the EU were represented by several bodies):

European Commission ( 2021 ): Regulation of the European Parliament and of the Council Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act) and Amending Certain Union Legislative Acts (2021/0106 (COD)) . Footnote 4

European Parliament Committee on Culture and Education ( 2021 ): Report on artificial intelligence in education, culture and the audiovisual sector (2020/2017(INI)) . Footnote 5

High-Level Expert Group on Artificial Intelligence (EUHLEX) ( 2019 ): Ethics Guidelines for Trustworthy AI . Footnote 6

UNESCO ( 2022 ): Recommendation on the Ethics of Artificial Intelligence (SHS/BIO/PI/2021/1) . Footnote 7

OECD ( 2019 ): Recommendation of the Council on Artificial Intelligence (OECD/LEGAL/0449) . Footnote 8

The ethical dilemmas established by these international documents (most of which is considered soft law material) were then used to inductively identify the primary aspects around which the investigation of educational AI principles may be organised.

Among the above documents, the EUHLEX material is the salient one as it contains a Glossary that defines and explains, among others, the two primary concepts that will be used in this paper: “artificial intelligence” and “ethics”. As this paper is, to a large extent, based on the deducted categorisation embedded in these international documents, it will follow suit in using the above terms as EUHLEX did, supporting it with the definitions contained in the other four referenced international documents. Consequently, artificial intelligence (AI) systems are referred to in this paper as software and hardware systems designed by humans that “act in the physical or digital dimension by perceiving their environment through data acquisition, interpreting the collected structured or unstructured data, reasoning on the knowledge, or processing the information, derived from this data and deciding the best action(s) to take to achieve the given goal” (EUHLEX, 2019 ). With regards to ethics, the EUHLEX group defines this term, in general as an academic discipline which is a subfield of philosophy, dealing with questions like “What is a good action?”, “What is the value of a human life?”, “What is justice?”, or “What is the good life?”. It also mentions that academia distinguishes four major fields: (i) Meta-ethics, (ii) normative ethics, (iii) descriptive ethics, and (iv) applied ethics ” (EUHLEX, 2019 , p. 37). Within these, AI ethics belongs to the latter group of applied ethics that focuses on the practical issues raised by the design, development, implementation, and use of AI systems. By extension, the application of AI systems in higher education also falls under the domain of applied ethics.

The selection of sample universities

The collection of cases started with the AI guidelines compiled by the authors as members of the AI Committee at their university from May to July 2023. The AI Committee consisted of 12 members and investigated over 150 cases to gauge international best practices of GAI use in higher education when formulating a policy recommendation for their own university leadership. Given the global nature of the subject matter, examples from various continents were collected. From this initial pool authors narrowed the scope to the Top 500 higher education institutions of the Shanghai Ranking list for this study, as best practices most often accrue from the highest-ranking universities. Finally, only those institutions were included which, at the time of data collection, have indeed had publicly available policy documents or guidelines with clearly identifiable ethical considerations (such as relevant internal documents, Codes of Ethics, Academic Regulations, Codes of Practice and Procedure, or Guidelines for Students and Teachers). By the end of this selection process, 30 samples proved to be substantiated enough to be included in this study (presented in Table 1 ).

All documents were contextually analysed and annotated by both authors individually looking for references or mentions of ideas, actions or recommendations related to the ethical principles identified during the first step of the research. These comments were then compared and commonalities analysed regarding the nature and goal of the ethical recommendation.

Principles and practices of responsible use of AI in higher education

Ai-related ethical codes forming the base of this investigation.

A common feature of the selected AI ethics documents issued by international organisations is that they enumerate a set of ethical principles based on fundamental human values. The referenced international documents have different geographical- and policy scopes, yet they overlap in their categorisation of the ethical dimensions relevant to this research, even though they might use discrepant language to describe the same phenomenon (a factor we took into account when establishing key categories). For example, what EUHLEX dubs as “Human agency and oversight” is addressed by UNESCO under the section called “Human oversight and determination”, yet they essentially cover the same issues and recommended requirements. Among the many principles enshrined in these documents, the research focuses on those that can be directly linked to the everyday education practices of universities in relation to AI tools, omitting those that, within this context, are less situation-dependent and should normally form the overarching basis of the functioning of universities at all times, such as: respecting human rights and fundamental freedoms, refraining from all forms of discrimination, the right to privacy and data protection, or being aware of environmental concerns and responsibilities regarding sustainable development. As pointed out by Nikolinakos ( 2023 ), such principles and values provide essential guidance not only for development but also during the deployment and use of AI systems. Synthesising the common ethical codes in these instruments has led to the following cluster of ethical principles that are directly linked to AI-related higher education practices:

Accountability and responsibility;

Human agency and oversight;

Transparency and explainability

Inclusiveness and diversity.

The following subsections will give a comprehensive definition of these ethical areas and relate them to higher education expectations. Each subsection will first explain the corresponding ethical cluster, then present the specific university examples, concluding with a summary of the identified best practice under that particular cluster.

Accountability and responsibility

Definition in ethical codes and relevance.

The most fundamental requirements, appearing in almost all relevant documents, bring forward the necessity that mechanisms should be implemented to ensure responsibility and accountability for AI systems and their outcomes. These cover expectations both before and after their deployment, including development and use. They entail the basic requirements of auditability (i.e. the enablement of the assessment of algorithms), clear roles in the management of data and design processes (as a means for contributing to the trustworthiness of AI technology), the minimalisation and reporting of negative impacts (focusing on the possibility of identifying, assessing, documenting and reporting on the potential negative impacts of AI systems), as well as the ability of redress (understood as the capability to utilise mechanisms that offer legal and practical remedy when unjust adverse impact occurs) (EUHLEX, 2019 , pp. 19–20).

Additionally, Points 35–36 of the UNESCO recommendations remind us that it is imperative to “attribute ethical and legal responsibility for any stage of the life cycle of AI systems, as well as in cases of remedy related to AI systems, to physical persons or to existing legal entities. AI system can never replace ultimate human responsibility and accountability” (UNESCO, 2022 , p. 22).

The fulfilment of this fundamental principle is also expected from academic authors, as per the announcements of some of the largest publishing houses in the world. Accordingly, AI is not an author or co-author, Footnote 9 and AI-assisted technologies should not be cited as authors either, Footnote 10 given that AI-generated content cannot be considered capable of initiating an original piece of research without direction from human authors. The ethical guidelines of Wiley ( 2023 ) stated that ”[AI tools] also cannot be accountable for a published work or for research design, which is a generally held requirement of authorship, nor do they have legal standing or the ability to hold or assign copyright.” Footnote 11 This research angle carries over to teaching as well since students are also expected to produce outputs that are the results of their own work. Furthermore, they also often do their own research (such as literature search and review) in support of their projects, homework, thesis, and other forms of performance evaluation.

Accountability and responsibility in university first responses

The rapidly changing nature of the subject matter poses a significant challenge for scholars to assess the state of play of human responsibility. This is well exemplified by the reversal of hearts by some Australian universities (see Rudolph et al. ( 2023 ) quoting newspaper articles) who first disallowed the use of AI by students while doing assignments, just to reverse that decision a few months later and replace it by a requirement of disclosing the use of AI in homeworks. Similarly, Indian governments have been oscillating between a non-regulatory approach to foster an “innovation-friendly environment” for their universities in the summer of 2023 (Liu, 2023 ), only to roll back on this pledge a few months later (Dhaor, 2023 ).

Beyond this regulatory entropy, a fundamental principle enshrined in university codes of ethics across the globe is that students need to meet existing rules of scientific referencing and authorship. Footnote 12 In other words, they should refrain from any form of plagiarism in all their written work (including essays, theses, term papers, or in-class presentations). Submitting any work and assessments created by someone or something else (including AI-generated content) as if it was their own usually amounts to either a violation of scientific referencing, plagiarism or is considered to be a form of cheating (or a combination of these), depending on the terminology used by the respective higher education institution.

As a course description of Johns Hopkins puts it, “academic honesty is required in all work you submit to be graded …., you must solve all homework and programming assignments without the help of outside sources (e.g., GAI tools)” (Johns Hopkins University, 2023 ).

The Tokyo Institute of Technology applies a more flexible approach, as they “trust the independence of the students and expect the best use” of AI systems from them based on good sense and ethical standards. They add, however, that submitting reports that rely almost entirely on the output of GenAI is “highly improper, and its continued use is equivalent to one’s enslavement to the technology” (Tokyo Institute of Technology, 2023 ).

In the case of York University, the Senate’s Academic Standards, Curriculum, and Pedagogy Committee clarified in February 2023 that students are not authorised to use “text-, image-, code-, or video-generating AI tools when completing their academic work unless explicitly permitted by a specific instructor in a particular course” (York University Senate, 2023 ).

In the same time frame (6 February 2023), the University of Oxford stated in a guidance material for staff members that “the unauthorised use of AI tools in exams and other assessed work is a serious disciplinary offence” not permitted for students (University of Oxford, 2023b ).

Main message and best practice: honesty and mutual trust

In essence, students are not allowed to present AI-generated content as their own, Footnote 13 and they should have full responsibility and accountability for their own papers. Footnote 14 This is in line with the most ubiquitous principle enshrined in almost all university guidelines, irrespective of AI, that students are expected to complete their tasks based on their own knowledge and skills obtained throughout their education.

Given that the main challenge here is unauthorised use and overreliance on GAI platforms, the best practice answer is for students to adhere to academic honesty and integrity, scientific referencing standards, existing anti-plagiarism rules, and complete university assignments without fully relying on GAI tools, using, first and foremost, their own skills. The only exception is when instructed otherwise by their professors. By extension, preventing overuse and unauthorised use of AI assists students in avoiding undermining their own academic capacity-building efforts.

Human agency and oversight

AI systems have the potential to manipulate and influence human behaviour in ways that are not easily detectable. AI systems must, therefore, follow human-centric design principles and leave meaningful opportunities for human choice and intervention. Such systems should not be able to unjustifiably subordinate, coerce, deceive, manipulate, condition or herd humans (EUHLEX, 2019 , p. 16).

Human oversight thus refers to the capability for human intervention in every decision cycle of the AI system and the ability of users to make informed, autonomous decisions regarding AI systems. This encompasses the ability to choose not to use an AI system in a particular situation or to halt AI-related operations via a “stop” button or a comparable procedure in case the user detects anomalies, dysfunctions and unexpected performance from AI tools (European Commission, 2021 , Art. 14).

The sheer capability of active oversight and intervention vis-á-vis GAI systems is strongly linked to ethical responsibility and legal accountability. As Liao puts it, “the sufficient condition for human beings being rightsholders is that they have a physical basis for moral agency.” (Liao, 2020 , pp. 496–497). Wagner complemented this with the essential point that entity status for non-human actors would help to shield other parties from liability, i.e., primarily manufacturers and users (Wagner, 2018 ). This, in turn, would result in risk externalisation, which serves to minimise or relativise a person’s moral accountability and legal liability associated with wrongful or unethical acts.

Users, in our case, are primarily students who, at times, might be tempted to make use of AI tools in an unethical way, hoping to fulfil their university tasks faster and more efficiently than they could without these.

Human agency and oversight in university first responses

The crucial aspect of this ethical issue is the presence of a “stop” button or a similar regulatory procedure to streamline the operation of GAI tools. Existing university guidelines in this question point clearly in the direction of soft sanctions, if any, given the fact that there is a lack of evidence that AI detection platforms are effective and reliable tools to tell apart human work from AI-generated ones. Additionally, these tools raise some significant implications for privacy and data security issues, which is why university guidelines are particularly cautious when referring to these. Accordingly, the National Taiwan University, the University of Toronto, the University of Waterloo, the University of Miami, the National Autonomous University of Mexico, and Yale, among others, do not recommend the use of AI detection platforms in university assessments. The University of Zürich further added the moral perspective in a guidance note from 13 July 2023, that “forbidding the use of undetectable tools on unsupervised assignments or demanding some sort of honour code likely ends up punishing the honest students” (University of Zürich, 2023 ). Apart from unreliability, the University of Cape Town also drew attention in its guide for staff that AI detection tools may “disproportionately flag text written by non-first language speakers as AI-generated” (University of Cape Town, 2023 , p. 8).

Macquarie University took a slightly more ambiguous stance when they informed their staff that, while it is not “proof” for anything, an AI writing detection feature was launched within Turnitin as of 5 April 2023 (Hillier, 2023 ), claiming that the software has a 97% detection rate with a 1% false positive rate in the tests that they had conducted (Turnitin, 2023 ). Apart from these, Boston University is among the few examples that recommend employing AI detection tools, but only in a restricted manner to ”evaluate the degree to which AI tools have likely been employed” and not as a source for any punitive measures against students (University of Boston, 2023 ). Remarkably, they complement the above with suggestions for a merit-based scoring system, whereby instructors shall treat work by students who declare no use of AI tools as the baseline for grading. A lower baseline is suggested for students who declare the use of AI tools (depending on how extensive the usage was), and for the bottom of this spectrum, the university suggests imposing a significant penalty for low-energy or unreflective reuse of material generated by AI tools and assigning zero points for merely reproducing the output from AI platforms.

A discrepant approach was adopted at the University of Toronto. Here, if an instructor indicates that the use of AI tools is not permitted on an assessment, and a student is later found to have used such a tool nevertheless, then the instructor should consider meeting with the student as the first step of a dialogue-based process under the Code of Behaviour on Academic Matters (the same Code, which categorises the use of ChatGPT and other such tools as “unauthorised aid” or as “any other form of cheating” in case, an instructor specified that no outside assistance was permitted on an assignment) (University of Toronto, 2019 ).

More specifically, Imperial College London’s Guidance on the Use of Generative AI tools envisages the possibility of inviting a random selection of students to a so-called “authenticity interview” on their submitted assignments (Imperial College London, 2023b ). This entails requiring students to attend an oral examination of their submitted work to ensure its authenticity, which includes questions about the subject or how they approached their assignment.

As a rare exception, the University of Helsinki represents one of the more rigorous examples. The “Guidelines for the Use of AI in Teaching at the University of Helsinki” does not lay down any specific procedures for AI-related ethical offences. On the contrary, as para. 7 stipulates the unauthorised use of GAI in any course examination “constitutes cheating and will be treated in the same way as other cases of cheating” (University of Helsinki, 2023 ). Footnote 15

Those teachers who are reluctant to make AI tools a big part of their courses should rather aim to develop course assessment methods that can plausibly prevent the use of AI tools instead of attempting to filter these afterwards. Footnote 16 For example, the Humboldt-Universität zu Berlin instructs that, if possible, oral or practical examinations or written examinations performed on-site are recommended as alternatives to “classical” written home assignments (Humboldt-Universität zu Berlin, 2023a ).

Monash University also mentions some examples in this regard (Monash University, 2023a ), such as: asking students to create oral presentations, videos, and multimedia resources; asking them to incorporate more personal reflections tied to the concepts studied; implementing programmatic assessment that focuses on assessing broader attributes of students, using multiple methods rather than focusing on assessing individual kinds of knowledge or skills using a single assessment method (e.g., writing an essay).

Similarly, the University of Toronto suggest instructors to: ask students to respond to a specific reading that is very new and thus has a limited online footprint; assign group work to be completed in class, with each member contributing; or ask students to create a first draft of an assignment by hand, which could be complemented by a call to explain or justify certain elements of their work (University of Toronto, 2023 ).

Main message and best practice: Avoiding overreaction

In summary, the best practice that can be identified under this ethical dilemma is to secure human oversight through a blend of preventive measures (e.g. a shift in assessment methods) and soft sanctions. Given that AI detectors are unreliable and can cause a series of data privacy issues, the sanctioning of unauthorised AI use should happen on a “soft basis”, as part of a dialogue with the student concerned. Additionally, universities need to be aware and pay due attention to potentially unwanted rebound effects of bona fide measures, such as the merit-based scoring system of the University of Boston. In that case, using different scoring baselines based on the self-declared use of AI could, in practice, generate incentives for not declaring any use of AI at all, thereby producing counter-effective results.

While explainability refers to providing intelligible insight into the functioning of AI tools with a special focus on the interplay between the user’s input and the received output, transparency alludes to the requirement of providing unambiguous communication in the framework of system use.

As the European Commission’s Regulation proposal ( 2021 ) puts it under subchapter 5.2.4., transparency obligations should apply for systems that „(i) interact with humans, (ii) are used to detect emotions or determine association with (social) categories based on biometric data, or (iii) generate or manipulate content (‘deep fakes’). When persons interact with an AI system or their emotions or characteristics are recognised through automated means, people must be informed of that circumstance. If an AI system is used to generate or manipulate image, audio or video content that appreciably resembles authentic content, there should be an obligation to disclose that the content is generated through automated means, subject to exceptions for legitimate purposes (law enforcement, freedom of expression). This allows persons to make informed choices or step back from a given situation.”

People (in our case, university students and teachers) should, therefore, be fully informed when a decision is influenced by or relies on AI algorithms. In such instances, individuals should be able to ask for further explanation from the decision-maker using AI (e.g., a university body). Furthermore, individuals should be afforded the choice to present their case to a dedicated representative of the organisation in question who should have the power to reviset the decision and make corrections if necessary (UNESCO, 2022 , p. 22). Therefore, in the context of courses and other related education events, teachers should be clear about their utilisation of AI during the preparation of the material. Furthermore, instructors must unambiguously clarify ethical AI use in the classroom. Clear communication is essential about whether students have permission to utilise AI tools during assignments and how to report actual use.

As both UN and EU sources point out, raising awareness about and promoting basic AI literacy should be fostered as a means to empower people and reduce the digital divides and digital access inequalities resulting from the broad adoption of AI systems (EUHLEX, 2019 , p. 23; UNESCO, 2022 , p. 34).

Transparency and explainability in university first responses

The implementation of this principle seems to revolve around the challenge of decentralisation of university work, including the respect for teachers’ autonomy.

Teachers’ autonomy entails that teachers can decide if and to what extent they will allow their students to use AI platforms as part of their respective courses. This, however, comes with the essential corollary, that they must clearly communicate their decision to both students and university management in the course syllabus. To support transparency in this respect, many universities decided to establish 3-level- or 4-level admissibility frameworks (and even those who did not establish such multi-level systems, e.g., the University of Toronto, urge instructors to explicitly indicate in the course syllabus the expected use of AI) (University of Toronto, 2023 ).

The University of Auckland is among the universities that apply a fully laissez passer laissez-faire approach in this respect, meaning that there is a lack of centralised guidance or recommendations on this subject. They rather confer all practical decision-making of GAI use on course directors, adding that it is ultimately the student’s responsibility to correctly acknowledge the use of Gen-AI software (University of Auckland, 2023 ). Similarly, the University of Helsinki gives as much manoeuvring space to their staff as to allow them to change the course of action during the semester. As para 1 of their earlier quoted Guidelines stipulates, teachers are responsible for deciding how GAI can be used on a given course and are free to fully prohibit their use if they think it impedes the achievement of the learning objectives.

Colorado State University, for example, provides its teachers with 3 types of syllabus statement options (Colorado State University, 2023 ): (a) the prohibitive statement: whereby any work created, or inspired by AI agents is considered plagiarism and will not be tolerated; (b) the use-with-permission statement: whereby generative AI can be used but only as an exception and in line with the teachers further instruction, and (c) the abdication statement: where the teacher acknowledges that the course grade will also be a reflection of the students ability to harness AI technologies as part of their preparation for their future in a workforce that will increasingly require AI-literacy.

Macquarie University applies a similar system and provides it’s professors with an Assessment Checklist in which AI use can be either “Not permitted” or “Some use permitted” (meaning that the scope of use is limited while the majority of the work should be written or made by the student.), or “Full use permitted (with attribution)”, alluding to the adaptive use of AI tools, where the generated content is edited, mixed, adapted and integrated into the student’s final submission – with attribution of the source (Macquarie University, 2023 ).

The same approach is used at Monash University where generative AI tools can be: (a) used for all assessments in a specific unit; (b) cannot be used for any assessments; (c) some AI tools may be used selectively (Monash University, 2023b ).

The University of Cape Town (UCT) applies a 3-tier system not just in terms of the overall approach to the use or banning of GAI, but also with regard to specific assessment approaches recommended to teachers. As far as the former is concerned, they differentiate between the strategies of: (a) Avoiding (reverting to in-person assessment, where the use of AI isn’t possible); (b) Outrunning (devising an assessment that AI cannot produce); and (c) Embracing (discussing the appropriate use of AI with students and its ethical use to create the circumstances for authentic assessment outputs). The assessment possibilities, in turn, are categorised into easy, medium, and hard levels. Easy tasks include, e.g., generic short written assignments. Medium level might include examples such as personalised or context-based assessments (e.g. asking students to write to a particular audience whose knowledge and values must be considered or asking questions that would require them to give a response that draws from concepts that were learnt in class, in a lab, field trip…etc). In contrast, hard assessments include projects involving real-world applications, synchronous oral assessments, or panel assessments (University of Cape Town, 2023 ).

4-tier-systems are analogues. The only difference is that they break down the “middle ground”. Accordingly, the Chinese University of Hong Kong clarifies that Approach 1 (by default) means the prohibition of all use of AI tools; Approach 2 entails using AI tools only with prior permission; Approach 3 means using AI tools only with explicit acknowledgement; and Approach 4 is reserved for courses in which the use of AI tools is freely permitted with no acknowledgement needed (Chinese University of Hong Kong, 2023 ).

Similarly, the University of Delaware provides course syllabus statement examples for teachers including: (1) Prohibiting all use of AI tools; (2) Allowing their use only with prior permission; (3) Allow their use only with explicit acknowledgement; (4) Freely allow their use (University of Delaware, 2023 ).

The Technical University of Berlin also proposes a 4-tier system but uses a very different logic based on the practical knowledge one can obtain by using GAI. Accordingly, they divide AI tools as used to: (a) acquire professional competence; (b) learn to write scientifically; (c) be able to assess AI tools and compare them with scientific methods; d) professional use of AI tools in scientific work. Their corresponding guideline even quotes Art. 5 of the German Constitution referencing the freedom of teaching ( Freiheit der Lehre ), entailing that teachers should have the ability to decide for themselves which teaching aids they allow or prohibit. Footnote 17

This detailed approach, however, is rather the exception. According to the compilation on 6 May 2023 by Solis ( 2023 ), among the 100 largest German universities, 2% applied a general prohibition on the use of ChatGPT, 23% granted partial permission, 12% generally permitted its use, while 63% of the universities had none or only vague guidelines in this respect.

Main message and best practice: raising awareness

Overall, the best practice answer to the dilemma of transparency is the internal decentralisation of university work and the application of a “bottom-up” approach that respects the autonomy of university professors. Notwithstanding the potential existence of regulatory frameworks that set out binding rules for all citizens of an HE institution, this means providing university instructors with proper manoeuvring space to decide on their own how they would like to make AI use permissible in their courses, insofar as they communicate their decision openly.

Inclusiveness and diversity

Para. 34 of the Report by the European Parliament Committee on Culture and Education ( 2021 ) highlights that inclusive education can only be reached with the proactive presence of teachers and stresses that “AI technologies cannot be used to the detriment or at the expense of in-person education, as teachers must not be replaced by any AI or AI-related technologies”. Additionally, para. 20 of the same document highlights the need to create diverse teams of developers and engineers to work alongside the main actors in the educational, cultural, and audiovisual sectors in order to prevent gender or social bias from being inadvertently included in AI algorithms, systems, and applications.

This approach also underlines the need to consider the variety of different theories through which AI has been developed as a precursor to ensuring the application of the principle of diversity (UNESCO, 2022 , pp. 33–35), and it also recognises that a nuanced answer to AI-related challenges is only possible if affected stakeholders have an equal say in regulatory and design processes. An idea closely linked to the principle of fairness and the pledge to leave no one behind who might be affected by the outcome of using AI systems (EUHLEX, 2019 , pp. 18–19).

Therefore, in the context of higher education, the principle of inclusiveness aims to ensure that an institution provides the same opportunities to access the benefits of AI technologies for all its students, irrespective of their background, while also considering the particular needs of various vulnerable groups potentially marginalised based on age, gender, culture, religion, language, or disabilities. Footnote 18 Inclusiveness also alludes to stakeholder participation in internal university dialogues on the use and impact of AI systems (including students, teachers, administration and leadership) as well as in the constant evaluation of how these systems evolve. On a broader scale, it implies communication with policymakers on how higher education should accommodate itself to this rapidly changing environment (EUHLEX, 2019 , p. 23; UNESCO, 2022 , p. 35).

Inclusiveness and diversity in university first responses

Universities appear to be aware of the potential disadvantages for students who are either unfamiliar with GAI or who choose not to use it or use it in an unethical manner. As a result, many universities thought that the best way to foster inclusive GAI use was to offer specific examples of how teachers could constructively incorporate these tools into their courses.

The University of Waterloo, for example, recommends various methods that instructors can apply on sight, with the same set of tools for all students during their courses, which in itself mitigates the effects of any discrepancies in varying student backgrounds (University of Waterloo, 2023 ): (a) Give students a prompt during class, and the resulting text and ask them to critique and improve it using track changes; (b) Create two distinct texts and have students explain the flaws of each or combine them in some way using track changes; (c) Test code and documentation accuracy with a peer; or (d) Use ChatGPT to provide a preliminary summary of an issue as a jumping-off point for further research and discussion.

The University of Pittsburgh ( 2023 ) and Monash added similar recommendations to their AI guidelines (Monash University, 2023c ).

The University of Cambridge mentions under its AI-deas initiative a series of projects aimed to develop new AI methods to understand and address sensory, neural or linguistic challenges such as hearing loss, brain injury or language barriers to support people who find communicating a daily challenge in order to improve equity and inclusion. As they put it, “with AI we can assess and diagnose common language and communication conditions at scale, and develop technologies such as intelligent hearing aids, real-time machine translation, or other language aids to support affected individuals at home, work or school.” (University of Cambridge, 2023 ).

The homepage of the Technical University of Berlin (Technische Universität Berlin) displays ample and diverse materials, including videos Footnote 19 and other documents, as a source of inspiration for teachers on how to provide an equitable share of AI knowledge for their students (Glathe et al. 2023 ). More progressively, the university’s Institute of Psychology offers a learning modul called “Inclusive Digitalisation”, available for students enrolled in various degree programmes to understand inclusion and exclusion mechanisms in digitalisation. This modul touches upon topics such as barrier-free software design, mechanisms and reasons for digitalised discrimination or biases in corporate practices (their homepage specifically alludes to the fact that input and output devices, such as VR glasses, have exclusively undergone testing with male test subjects and that the development of digital products and services is predominantly carried out by men. The practical ramifications of such a bias result in input and output devices that are less appropriate for women and children) (Technische Universität Berlin, 2023 ).

Columbia recommends the practice of “scaffolding”, which is the process of breaking down a larger assignment into subtasks (Columbia University, 2023 ). In their understanding, this method facilitates regular check-ins and enables students to receive timely feedback throughout the learning process. Simultaneously, the implementation of scaffolding helps instructors become more familiar with students and their work as the semester progresses, allowing them to take additional steps in the case of students who might need more attention due to their vulnerable backgrounds or disabilities to complete the same tasks.

The Humboldt-Universität zu Berlin, in its Recommendations, clearly links the permission of GAI use with the requirement of equal accessibility. They remind that if examiners require students to use AI for an examination, “students must be provided with access to these technologies free of charge and in compliance with data protection regulations” (Humboldt-Universität zu Berlin, 2023b ).

Concurringly, the University of Cape Town also links inclusivity to accessibility. As they put it, “there is a risk that those with poorer access to connectivity, devices, data and literacies will get unequal access to the opportunities being provided by AI”, leading to the conclusion that the planning of the admissible use of GAI on campus should be cognizant of access inequalities (University of Cape Town, 2023 ). They also draw their staff’s attention to a UNESCO guide material containing useful methods to incorporate ChatGPT into the course, including methods such as the “Socratic opponent” (AI acts as an opponent to develop an argument), the “study buddy” (AI helps the student reflect on learning material) or the “dynamic assessor” (AI provides educators with a profile of each student’s current knowledge based on their interactions with ChatGPT) (UNESCO International Institute for Higher Education in Latin America and the Caribbean, 2023 ).

Finally, the National Autonomous University of Mexico’s Recommendations suggest using GAI tools, among others, for the purposes of community development. They suggest that such community-building activities, whether online or in live groups, kill two birds with one stone. On the one hand, they assist individuals in keeping their knowledge up to date with a topic that is constantly evolving, while it offers people from various backgrounds the opportunity to become part of communities in the process where they can share their experiences and build new relations (National Autonomous University of Mexico, 2023 ).

Main message and best practice: Proactive central support and the pledge to leave no one behind

To conclude, AI-related inclusivity for students is best fostered if the university does not leave its professors solely to their own resources to come up with diverging initiatives. The best practice example for this dilemma thus lies in a proactive approach that results in the elaboration of concrete teaching materials (e.g., subscriptions to AI tools to ensure equal accessibility for all students, templates, video tutorials, open-access answers to FAQs…etc.), specific ideas, recommendations and to support specialised programmes and collaborations with an inclusion-generating edge. With centrally offered resources and tools institutions seem to be able to ensure accessability irrespective of students’ background and financial abilities.

Discussion of the First Responses

While artificial intelligence and even its generative form has been around for a while, the arrival of application-ready LLMs – most notably ChatGPT has changed the game when it comes to grammatically correct large-scale and content-specific text generation. This has invoked an immediate reaction from the higher education community as the question arose as to how it may affect various forms of student performance evaluation (such as essay and thesis writing) (Chaudhry et al. 2023 ; Yu, 2023 ; Farazouli et al. 2024 ).

Often the very first reaction (a few months after the announcement of the availability of ChatGPT) was a ban on these tools and a potential return to hand-written evaluation and oral exams. In the institutions investigated under this research, notable examples may be most Australian universities (such as Monash) or even Oxford. On the other hand, even leading institutions have immediately embraced this new tool as a great potential helper of lecturers – the top name here being Harvard. Very early responses thus ranged widely – and have changed fast over the first six-eight months “post-ChatGPT”.

Over time responses from the institutions investigated started to put out clear guidelines and even created dedicated policies or modified existing ones to ensure a framework of acceptable use. The inspiration leading these early regulatory efforts was influenced by the international ethics documents reviewed in this paper. Institutions were aware of and relied on those guidelines. The main goal of this research was to shed light on the questions of how much and in what ways they took them on board regarding first responses. Most first reactions were based on “traditional” AI ethics and understanding of AI before LLMs and the generative revolution. First responses by institutions were not based on scientific literature or arguments from journal publications. Instead, as our results demonstrated it was based on publicly available ethical norms and guidelines published by well-known international organizations and professional bodies.

Conclusions, limitations and future research

Ethical dilemmas discussed in this paper were based on the conceptualisation embedded in relevant documents of various international fora. Each ethical dimension, while multifaceted in itself, forms a complex set of challenges that are inextricably intertwined with one another. Browsing university materials, the overall impression is that Universities primarily aim to explore and harness the potential benefits of generative AI but not with an uncritical mindset. They are focusing on the opportunities while simultaneously trying to address the emerging challenges in the field.

Accordingly, the main ethical imperative is that students must complete university assignments based on the knowledge and skills they acquired during their university education unless their instructors determine otherwise. Moral and legal responsibility in this regard always rests with human individuals. AI agents possess neither the legal standing nor the physical basis for moral agency, which makes them incapable of assuming such responsibilities. This “top-down” requirement is most often complemented by the “bottom-up” approach of providing instructors with proper maneuvering space to decide how they would like to make AI use permissible in their courses.

Good practice in human oversight could thus be achieved through a combination of preventive measures and soft, dialogue-based procedures. This latter category includes the simple act of teachers providing clear, written communications in their syllabi and engaging in a dialogue with their students to provide unambiguous and transparent instructions on the use of generative AI tools within their courses. Additionally, to prevent the unauthorised use of AI tools, changing course assessment methods by default is more effective than engaging in post-assessment review due to the unreliability of AI detection tools.

Among the many ethical dilemmas that generative AI tools pose to social systems, this paper focused on those pertaining to the pedagogical aspects of higher education. Due to this limitation, related fields, such as university research, were excluded from the scope of the analysis. However, research-related activities are certainly ripe for scientific scrutiny along the lines indicated in this study. Furthermore, only a limited set of institutions could be investigated, those who were the ”first respondents” to the set of issues covered by this study. Hereby, this paper hopes to inspire further research on the impact of AI tools on higher education. Such research could cover more institutions, but it would also be interesting to revisit the same institutions again to see how their stance and approach might have changed over time considering how fast this technology evolves and how much we learn about its capabilities and shortcomings.

Data availability

Data sharing is not applicable to this article as no datasets were generated or analysed during the current study. All documents referenced in this study are publicly available on the corresponding websites provided in the Bibliography or in the footnotes. No code has been developed as part of this research.

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While the original French version was published in 1954, the first English translation is dated 1964.

As the evaluation by Bang et al. ( 2023 ) found, ChatGPT is only 63.41% accurate on average in ten different reasoning categories under logical reasoning, non-textual reasoning, and common-sense reasoning, making it an unreliable reasoner.

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The editors-in-chief of Nature and Science stated that ChatGPT does not meet the standard for authorship: „ An attribution of authorship carries with it accountability for the work, which cannot be effectively applied to LLMs…. We would not allow AI to be listed as an author on a paper we published, and use of AI-generated text without proper citation could be considered plagiarism,” (Stokel-Walker, 2023 ). See also (Nature, 2023 ).

While there was an initial mistake that credited ChatGPT as an author of an academic paper, Elsevier issued a Corrigendum on the subject in February 2023 (O’Connor, 2023 ). Elsevier then clarified in its “Use of AI and AI-assisted technologies in writing for Elsevier” announcement, issued in March 2023, that “Authors should not list AI and AI-assisted technologies as an author or co-author, nor cite AI as an author”. See https://www.elsevier.com/about/policies-and-standards/the-use-of-generative-ai-and-ai-assisted-technologies-in-writing-for-elsevier . Accessed 23 Nov 2023.

The ethical guidelines of Wiley was updated on 28 February 2023 to clarify the publishing house’s stance on AI-generated content.

See e.g.: Section 2.4 of Princeton University’s Academic Regulations (Princeton University, 2023 ); the Code of Practice and Procedure regarding Misconduct in Research of the University of Oxford (University of Oxford, 2023a ); Section 2.1.1 of the Senate Guidelines on Academic Honesty of York University, enumerating cases of cheating (York University, 2011 ); Imperial College London’s Academic Misconduct Policy and Procedures document (Imperial College London, 2023a ); the Guidelines for seminar and term papers of the University of Vienna (Universität Wien, 2016 ); Para 4. § (1) - (4) of the Anti-plagiarism Regulation of the Corvinus University of Budapest (Corvinus University of Budapest, 2018 ), to name a few.

15 Art. 2 (c)(v) of the early Terms of Use of OpenAI Products (including ChatGPT) dated 14 March 2023 clarified the restrictions of the use of their products. Accordingly, users may not represent the output from their services as human-generated when it was not ( https://openai.com/policies/mar-2023-terms/ . Accessed 14 Nov 2023). Higher education institutions tend to follow suit with this policy. For example, the List of Student Responsibilities enumerated under the “Policies and Regulations” of the Harvard Summer School from 2023 reminds students that their “academic integrity policy forbids students to represent work as their own that they did not write, code, or create” (Harvard University, 2023 ).

A similar view was communicated by Taylor & Francis in a press release issued on 17 February 2023, in which they clarified that: “Authors are accountable for the originality, validity and integrity of the content of their submissions. In choosing to use AI tools, authors are expected to do so responsibly and in accordance with our editorial policies on authorship and principles of publishing ethics” (Taylor and Francis, 2023 ).

This is one of the rare examples where the guideline was adopted by the university’s senior management, in this case, the Academic Affairs Council.

It should be noted that abundant sources recommend harnessing AI tools’ opportunities to improve education instead of attempting to ban them. Heaven, among others, advocated on the pages of the MIT Technology Review the use of advanced chatbots such as ChatGPT as these could be used as “powerful classroom aids that make lessons more interactive, teach students media literacy, generate personalised lesson plans, save teachers time on admin” (Heaven, 2023 ).

This university based its policies on the recommendations of the German Association for University Didactics (Deutsche Gesellschaft für Hochschuldidaktik). Consequently, they draw their students’ attention to the corresponding material, see: (Glathe et al. 2023 ).

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Dabis, A., Csáki, C. AI and ethics: Investigating the first policy responses of higher education institutions to the challenge of generative AI. Humanit Soc Sci Commun 11 , 1006 (2024). https://doi.org/10.1057/s41599-024-03526-z

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