empirical review literature review

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Module 2 Chapter 3: What is Empirical Literature & Where can it be Found?

In Module 1, you read about the problem of pseudoscience. Here, we revisit the issue in addressing how to locate and assess scientific or empirical literature . In this chapter you will read about:

• distinguishing between what IS and IS NOT empirical literature
• how and where to locate empirical literature for understanding diverse populations, social work problems, and social phenomena.

Probably the most important take-home lesson from this chapter is that one source is not sufficient to being well-informed on a topic. It is important to locate multiple sources of information and to critically appraise the points of convergence and divergence in the information acquired from different sources. This is especially true in emerging and poorly understood topics, as well as in answering complex questions.

What Is Empirical Literature

Social workers often need to locate valid, reliable information concerning the dimensions of a population group or subgroup, a social work problem, or social phenomenon. They might also seek information about the way specific problems or resources are distributed among the populations encountered in professional practice. Or, social workers might be interested in finding out about the way that certain people experience an event or phenomenon. Empirical literature resources may provide answers to many of these types of social work questions. In addition, resources containing data regarding social indicators may also prove helpful. Social indicators are the “facts and figures” statistics that describe the social, economic, and psychological factors that have an impact on the well-being of a community or other population group.The United Nations (UN) and the World Health Organization (WHO) are examples of organizations that monitor social indicators at a global level: dimensions of population trends (size, composition, growth/loss), health status (physical, mental, behavioral, life expectancy, maternal and infant mortality, fertility/child-bearing, and diseases like HIV/AIDS), housing and quality of sanitation (water supply, waste disposal), education and literacy, and work/income/unemployment/economics, for example.

Three characteristics stand out in empirical literature compared to other types of information available on a topic of interest: systematic observation and methodology, objectivity, and transparency/replicability/reproducibility. Let’s look a little more closely at these three features.

Systematic Observation and Methodology. The hallmark of empiricism is “repeated or reinforced observation of the facts or phenomena” (Holosko, 2006, p. 6). In empirical literature, established research methodologies and procedures are systematically applied to answer the questions of interest.

Objectivity. Gathering “facts,” whatever they may be, drives the search for empirical evidence (Holosko, 2006). Authors of empirical literature are expected to report the facts as observed, whether or not these facts support the investigators’ original hypotheses. Research integrity demands that the information be provided in an objective manner, reducing sources of investigator bias to the greatest possible extent.

Transparency and Replicability/Reproducibility.   Empirical literature is reported in such a manner that other investigators understand precisely what was done and what was found in a particular research study—to the extent that they could replicate the study to determine whether the findings are reproduced when repeated. The outcomes of an original and replication study may differ, but a reader could easily interpret the methods and procedures leading to each study’s findings.

What is NOT Empirical Literature

By now, it is probably obvious to you that literature based on “evidence” that is not developed in a systematic, objective, transparent manner is not empirical literature. On one hand, non-empirical types of professional literature may have great significance to social workers. For example, social work scholars may produce articles that are clearly identified as describing a new intervention or program without evaluative evidence, critiquing a policy or practice, or offering a tentative, untested theory about a phenomenon. These resources are useful in educating ourselves about possible issues or concerns. But, even if they are informed by evidence, they are not empirical literature. Here is a list of several sources of information that do not meet the standard of being called empirical literature:

• your course instructor’s lectures
• political statements
• advertisements
• newspapers & magazines (journalism)
• television news reports & analyses (journalism)
• many websites, Facebook postings, Twitter tweets, and blog postings
• the introductory literature review in an empirical article

You may be surprised to see the last two included in this list. Like the other sources of information listed, these sources also might lead you to look for evidence. But, they are not themselves sources of evidence. They may summarize existing evidence, but in the process of summarizing (like your instructor’s lectures), information is transformed, modified, reduced, condensed, and otherwise manipulated in such a manner that you may not see the entire, objective story. These are called secondary sources, as opposed to the original, primary source of evidence. In relying solely on secondary sources, you sacrifice your own critical appraisal and thinking about the original work—you are “buying” someone else’s interpretation and opinion about the original work, rather than developing your own interpretation and opinion. What if they got it wrong? How would you know if you did not examine the primary source for yourself? Consider the following as an example of “getting it wrong” being perpetuated.

Example: Bullying and School Shootings . One result of the heavily publicized April 1999 school shooting incident at Columbine High School (Colorado), was a heavy emphasis placed on bullying as a causal factor in these incidents (Mears, Moon, & Thielo, 2017), “creating a powerful master narrative about school shootings” (Raitanen, Sandberg, & Oksanen, 2017, p. 3). Naturally, with an identified cause, a great deal of effort was devoted to anti-bullying campaigns and interventions for enhancing resilience among youth who experience bullying.  However important these strategies might be for promoting positive mental health, preventing poor mental health, and possibly preventing suicide among school-aged children and youth, it is a mistaken belief that this can prevent school shootings (Mears, Moon, & Thielo, 2017). Many times the accounts of the perpetrators having been bullied come from potentially inaccurate third-party accounts, rather than the perpetrators themselves; bullying was not involved in all instances of school shooting; a perpetrator’s perception of being bullied/persecuted are not necessarily accurate; many who experience severe bullying do not perpetrate these incidents; bullies are the least targeted shooting victims; perpetrators of the shooting incidents were often bullying others; and, bullying is only one of many important factors associated with perpetrating such an incident (Ioannou, Hammond, & Simpson, 2015; Mears, Moon, & Thielo, 2017; Newman &Fox, 2009; Raitanen, Sandberg, & Oksanen, 2017). While mass media reports deliver bullying as a means of explaining the inexplicable, the reality is not so simple: “The connection between bullying and school shootings is elusive” (Langman, 2014), and “the relationship between bullying and school shooting is, at best, tenuous” (Mears, Moon, & Thielo, 2017, p. 940). The point is, when a narrative becomes this publicly accepted, it is difficult to sort out truth and reality without going back to original sources of information and evidence.

What May or May Not Be Empirical Literature: Literature Reviews

Investigators typically engage in a review of existing literature as they develop their own research studies. The review informs them about where knowledge gaps exist, methods previously employed by other scholars, limitations of prior work, and previous scholars’ recommendations for directing future research. These reviews may appear as a published article, without new study data being reported (see Fields, Anderson, & Dabelko-Schoeny, 2014 for example). Or, the literature review may appear in the introduction to their own empirical study report. These literature reviews are not considered to be empirical evidence sources themselves, although they may be based on empirical evidence sources. One reason is that the authors of a literature review may or may not have engaged in a systematic search process, identifying a full, rich, multi-sided pool of evidence reports.

There is, however, a type of review that applies systematic methods and is, therefore, considered to be more strongly rooted in evidence: the systematic review .

Systematic review of literature. A systematic reviewis a type of literature report where established methods have been systematically applied, objectively, in locating and synthesizing a body of literature. The systematic review report is characterized by a great deal of transparency about the methods used and the decisions made in the review process, and are replicable. Thus, it meets the criteria for empirical literature: systematic observation and methodology, objectivity, and transparency/reproducibility. We will work a great deal more with systematic reviews in the second course, SWK 3402, since they are important tools for understanding interventions. They are somewhat less common, but not unheard of, in helping us understand diverse populations, social work problems, and social phenomena.

Locating Empirical Evidence

Social workers have available a wide array of tools and resources for locating empirical evidence in the literature. These can be organized into four general categories.

Journal Articles. A number of professional journals publish articles where investigators report on the results of their empirical studies. However, it is important to know how to distinguish between empirical and non-empirical manuscripts in these journals. A key indicator, though not the only one, involves a peer review process . Many professional journals require that manuscripts undergo a process of peer review before they are accepted for publication. This means that the authors’ work is shared with scholars who provide feedback to the journal editor as to the quality of the submitted manuscript. The editor then makes a decision based on the reviewers’ feedback:

• Accept as is
• Accept with minor revisions
• Request that a revision be resubmitted (no assurance of acceptance)

When a “revise and resubmit” decision is made, the piece will go back through the review process to determine if it is now acceptable for publication and that all of the reviewers’ concerns have been adequately addressed. Editors may also reject a manuscript because it is a poor fit for the journal, based on its mission and audience, rather than sending it for review consideration.

Indicators of journal relevance. Various journals are not equally relevant to every type of question being asked of the literature. Journals may overlap to a great extent in terms of the topics they might cover; in other words, a topic might appear in multiple different journals, depending on how the topic was being addressed. For example, articles that might help answer a question about the relationship between community poverty and violence exposure might appear in several different journals, some with a focus on poverty, others with a focus on violence, and still others on community development or public health. Journal titles are sometimes a good starting point but may not give a broad enough picture of what they cover in their contents.

In focusing a literature search, it also helps to review a journal’s mission and target audience. For example, at least four different journals focus specifically on poverty:

• Journal of Children & Poverty
• Journal of Poverty
• Journal of Poverty and Social Justice
• Poverty & Public Policy

Let’s look at an example using the Journal of Poverty and Social Justice . Information about this journal is located on the journal’s webpage: http://policy.bristoluniversitypress.co.uk/journals/journal-of-poverty-and-social-justice . In the section headed “About the Journal” you can see that it is an internationally focused research journal, and that it addresses social justice issues in addition to poverty alone. The research articles are peer-reviewed (there appear to be non-empirical discussions published, as well). These descriptions about a journal are almost always available, sometimes listed as “scope” or “mission.” These descriptions also indicate the sponsorship of the journal—sponsorship may be institutional (a particular university or agency, such as Smith College Studies in Social Work ), a professional organization, such as the Council on Social Work Education (CSWE) or the National Association of Social Work (NASW), or a publishing company (e.g., Taylor & Frances, Wiley, or Sage).

Indicators of journal caliber.  Despite engaging in a peer review process, not all journals are equally rigorous. Some journals have very high rejection rates, meaning that many submitted manuscripts are rejected; others have fairly high acceptance rates, meaning that relatively few manuscripts are rejected. This is not necessarily the best indicator of quality, however, since newer journals may not be sufficiently familiar to authors with high quality manuscripts and some journals are very specific in terms of what they publish. Another index that is sometimes used is the journal’s impact factor . Impact factor is a quantitative number indicative of how often articles published in the journal are cited in the reference list of other journal articles—the statistic is calculated as the number of times on average each article published in a particular year were cited divided by the number of articles published (the number that could be cited). For example, the impact factor for the Journal of Poverty and Social Justice in our list above was 0.70 in 2017, and for the Journal of Poverty was 0.30. These are relatively low figures compared to a journal like the New England Journal of Medicine with an impact factor of 59.56! This means that articles published in that journal were, on average, cited more than 59 times in the next year or two.

Impact factors are not necessarily the best indicator of caliber, however, since many strong journals are geared toward practitioners rather than scholars, so they are less likely to be cited by other scholars but may have a large impact on a large readership. This may be the case for a journal like the one titled Social Work, the official journal of the National Association of Social Workers. It is distributed free to all members: over 120,000 practitioners, educators, and students of social work world-wide. The journal has a recent impact factor of.790. The journals with social work relevant content have impact factors in the range of 1.0 to 3.0 according to Scimago Journal & Country Rank (SJR), particularly when they are interdisciplinary journals (for example, Child Development , Journal of Marriage and Family , Child Abuse and Neglect , Child Maltreatmen t, Social Service Review , and British Journal of Social Work ). Once upon a time, a reader could locate different indexes comparing the “quality” of social work-related journals. However, the concept of “quality” is difficult to systematically define. These indexes have mostly been replaced by impact ratings, which are not necessarily the best, most robust indicators on which to rely in assessing journal quality. For example, new journals addressing cutting edge topics have not been around long enough to have been evaluated using this particular tool, and it takes a few years for articles to begin to be cited in other, later publications.

Beware of pseudo-, illegitimate, misleading, deceptive, and suspicious journals . Another side effect of living in the Age of Information is that almost anyone can circulate almost anything and call it whatever they wish. This goes for “journal” publications, as well. With the advent of open-access publishing in recent years (electronic resources available without subscription), we have seen an explosion of what are called predatory or junk journals . These are publications calling themselves journals, often with titles very similar to legitimate publications and often with fake editorial boards. These “publications” lack the integrity of legitimate journals. This caution is reminiscent of the discussions earlier in the course about pseudoscience and “snake oil” sales. The predatory nature of many apparent information dissemination outlets has to do with how scientists and scholars may be fooled into submitting their work, often paying to have their work peer-reviewed and published. There exists a “thriving black-market economy of publishing scams,” and at least two “journal blacklists” exist to help identify and avoid these scam journals (Anderson, 2017).

This issue is important to information consumers, because it creates a challenge in terms of identifying legitimate sources and publications. The challenge is particularly important to address when information from on-line, open-access journals is being considered. Open-access is not necessarily a poor choice—legitimate scientists may pay sizeable fees to legitimate publishers to make their work freely available and accessible as open-access resources. On-line access is also not necessarily a poor choice—legitimate publishers often make articles available on-line to provide timely access to the content, especially when publishing the article in hard copy will be delayed by months or even a year or more. On the other hand, stating that a journal engages in a peer-review process is no guarantee of quality—this claim may or may not be truthful. Pseudo- and junk journals may engage in some quality control practices, but may lack attention to important quality control processes, such as managing conflict of interest, reviewing content for objectivity or quality of the research conducted, or otherwise failing to adhere to industry standards (Laine & Winker, 2017).

One resource designed to assist with the process of deciphering legitimacy is the Directory of Open Access Journals (DOAJ). The DOAJ is not a comprehensive listing of all possible legitimate open-access journals, and does not guarantee quality, but it does help identify legitimate sources of information that are openly accessible and meet basic legitimacy criteria. It also is about open-access journals, not the many journals published in hard copy.

An additional caution: Search for article corrections. Despite all of the careful manuscript review and editing, sometimes an error appears in a published article. Most journals have a practice of publishing corrections in future issues. When you locate an article, it is helpful to also search for updates. Here is an example where data presented in an article’s original tables were erroneous, and a correction appeared in a later issue.

• Marchant, A., Hawton, K., Stewart A., Montgomery, P., Singaravelu, V., Lloyd, K., Purdy, N., Daine, K., & John, A. (2017). A systematic review of the relationship between internet use, self-harm and suicidal behaviour in young people: The good, the bad and the unknown. PLoS One, 12(8): e0181722. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5558917/
• Marchant, A., Hawton, K., Stewart A., Montgomery, P., Singaravelu, V., Lloyd, K., Purdy, N., Daine, K., & John, A. (2018).Correction—A systematic review of the relationship between internet use, self-harm and suicidal behaviour in young people: The good, the bad and the unknown. PLoS One, 13(3): e0193937.  http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0193937

Search Tools. In this age of information, it is all too easy to find items—the problem lies in sifting, sorting, and managing the vast numbers of items that can be found. For example, a simple Google® search for the topic “community poverty and violence” resulted in about 15,600,000 results! As a means of simplifying the process of searching for journal articles on a specific topic, a variety of helpful tools have emerged. One type of search tool has previously applied a filtering process for you: abstracting and indexing databases . These resources provide the user with the results of a search to which records have already passed through one or more filters. For example, PsycINFO is managed by the American Psychological Association and is devoted to peer-reviewed literature in behavioral science. It contains almost 4.5 million records and is growing every month. However, it may not be available to users who are not affiliated with a university library. Conducting a basic search for our topic of “community poverty and violence” in PsychINFO returned 1,119 articles. Still a large number, but far more manageable. Additional filters can be applied, such as limiting the range in publication dates, selecting only peer reviewed items, limiting the language of the published piece (English only, for example), and specified types of documents (either chapters, dissertations, or journal articles only, for example). Adding the filters for English, peer-reviewed journal articles published between 2010 and 2017 resulted in 346 documents being identified.

Just as was the case with journals, not all abstracting and indexing databases are equivalent. There may be overlap between them, but none is guaranteed to identify all relevant pieces of literature. Here are some examples to consider, depending on the nature of the questions asked of the literature:

• Academic Search Complete—multidisciplinary index of 9,300 peer-reviewed journals
• AgeLine—multidisciplinary index of aging-related content for over 600 journals
• Campbell Collaboration—systematic reviews in education, crime and justice, social welfare, international development
• Google Scholar—broad search tool for scholarly literature across many disciplines
• MEDLINE/ PubMed—National Library of medicine, access to over 15 million citations
• Oxford Bibliographies—annotated bibliographies, each is discipline specific (e.g., psychology, childhood studies, criminology, social work, sociology)
• PsycINFO/PsycLIT—international literature on material relevant to psychology and related disciplines
• SocINDEX—publications in sociology
• Social Sciences Abstracts—multiple disciplines
• Social Work Abstracts—many areas of social work are covered
• Web of Science—a “meta” search tool that searches other search tools, multiple disciplines

Placing our search for information about “community violence and poverty” into the Social Work Abstracts tool with no additional filters resulted in a manageable 54-item list. Finally, abstracting and indexing databases are another way to determine journal legitimacy: if a journal is indexed in a one of these systems, it is likely a legitimate journal. However, the converse is not necessarily true: if a journal is not indexed does not mean it is an illegitimate or pseudo-journal.

Government Sources. A great deal of information is gathered, analyzed, and disseminated by various governmental branches at the international, national, state, regional, county, and city level. Searching websites that end in.gov is one way to identify this type of information, often presented in articles, news briefs, and statistical reports. These government sources gather information in two ways: they fund external investigations through grants and contracts and they conduct research internally, through their own investigators. Here are some examples to consider, depending on the nature of the topic for which information is sought:

• Agency for Healthcare Research and Quality (AHRQ) at https://www.ahrq.gov/
• Bureau of Justice Statistics (BJS) at https://www.bjs.gov/
• Census Bureau at https://www.census.gov
• Morbidity and Mortality Weekly Report of the CDC (MMWR-CDC) at https://www.cdc.gov/mmwr/index.html
• Child Welfare Information Gateway at https://www.childwelfare.gov
• Children’s Bureau/Administration for Children & Families at https://www.acf.hhs.gov
• Forum on Child and Family Statistics at https://www.childstats.gov
• National Institutes of Health (NIH) at https://www.nih.gov , including (not limited to):
• National Institute on Aging (NIA at https://www.nia.nih.gov
• National Institute on Alcohol Abuse and Alcoholism (NIAAA) at https://www.niaaa.nih.gov
• National Institute of Child Health and Human Development (NICHD) at https://www.nichd.nih.gov
• National Institute on Drug Abuse (NIDA) at https://www.nida.nih.gov
• National Institute of Environmental Health Sciences at https://www.niehs.nih.gov
• National Institute of Mental Health (NIMH) at https://www.nimh.nih.gov
• National Institute on Minority Health and Health Disparities at https://www.nimhd.nih.gov
• National Institute of Justice (NIJ) at https://www.nij.gov
• Substance Abuse and Mental Health Services Administration (SAMHSA) at https://www.samhsa.gov/
• United States Agency for International Development at https://usaid.gov

Each state and many counties or cities have similar data sources and analysis reports available, such as Ohio Department of Health at https://www.odh.ohio.gov/healthstats/dataandstats.aspx and Franklin County at https://statisticalatlas.com/county/Ohio/Franklin-County/Overview . Data are available from international/global resources (e.g., United Nations and World Health Organization), as well.

Other Sources. The Health and Medicine Division (HMD) of the National Academies—previously the Institute of Medicine (IOM)—is a nonprofit institution that aims to provide government and private sector policy and other decision makers with objective analysis and advice for making informed health decisions. For example, in 2018 they produced reports on topics in substance use and mental health concerning the intersection of opioid use disorder and infectious disease,  the legal implications of emerging neurotechnologies, and a global agenda concerning the identification and prevention of violence (see http://www.nationalacademies.org/hmd/Global/Topics/Substance-Abuse-Mental-Health.aspx ). The exciting aspect of this resource is that it addresses many topics that are current concerns because they are hoping to help inform emerging policy. The caution to consider with this resource is the evidence is often still emerging, as well.

Numerous “think tank” organizations exist, each with a specific mission. For example, the Rand Corporation is a nonprofit organization offering research and analysis to address global issues since 1948. The institution’s mission is to help improve policy and decision making “to help individuals, families, and communities throughout the world be safer and more secure, healthier and more prosperous,” addressing issues of energy, education, health care, justice, the environment, international affairs, and national security (https://www.rand.org/about/history.html). And, for example, the Robert Woods Johnson Foundation is a philanthropic organization supporting research and research dissemination concerning health issues facing the United States. The foundation works to build a culture of health across systems of care (not only medical care) and communities (https://www.rwjf.org).

While many of these have a great deal of helpful evidence to share, they also may have a strong political bias. Objectivity is often lacking in what information these organizations provide: they provide evidence to support certain points of view. That is their purpose—to provide ideas on specific problems, many of which have a political component. Think tanks “are constantly researching solutions to a variety of the world’s problems, and arguing, advocating, and lobbying for policy changes at local, state, and federal levels” (quoted from https://thebestschools.org/features/most-influential-think-tanks/ ). Helpful information about what this one source identified as the 50 most influential U.S. think tanks includes identifying each think tank’s political orientation. For example, The Heritage Foundation is identified as conservative, whereas Human Rights Watch is identified as liberal.

While not the same as think tanks, many mission-driven organizations also sponsor or report on research, as well. For example, the National Association for Children of Alcoholics (NACOA) in the United States is a registered nonprofit organization. Its mission, along with other partnering organizations, private-sector groups, and federal agencies, is to promote policy and program development in research, prevention and treatment to provide information to, for, and about children of alcoholics (of all ages). Based on this mission, the organization supports knowledge development and information gathering on the topic and disseminates information that serves the needs of this population. While this is a worthwhile mission, there is no guarantee that the information meets the criteria for evidence with which we have been working. Evidence reported by think tank and mission-driven sources must be utilized with a great deal of caution and critical analysis!

In many instances an empirical report has not appeared in the published literature, but in the form of a technical or final report to the agency or program providing the funding for the research that was conducted. One such example is presented by a team of investigators funded by the National Institute of Justice to evaluate a program for training professionals to collect strong forensic evidence in instances of sexual assault (Patterson, Resko, Pierce-Weeks, & Campbell, 2014): https://www.ncjrs.gov/pdffiles1/nij/grants/247081.pdf . Investigators may serve in the capacity of consultant to agencies, programs, or institutions, and provide empirical evidence to inform activities and planning. One such example is presented by Maguire-Jack (2014) as a report to a state’s child maltreatment prevention board: https://preventionboard.wi.gov/Documents/InvestmentInPreventionPrograming_Final.pdf .

When Direct Answers to Questions Cannot Be Found. Sometimes social workers are interested in finding answers to complex questions or questions related to an emerging, not-yet-understood topic. This does not mean giving up on empirical literature. Instead, it requires a bit of creativity in approaching the literature. A Venn diagram might help explain this process. Consider a scenario where a social worker wishes to locate literature to answer a question concerning issues of intersectionality. Intersectionality is a social justice term applied to situations where multiple categorizations or classifications come together to create overlapping, interconnected, or multiplied disadvantage. For example, women with a substance use disorder and who have been incarcerated face a triple threat in terms of successful treatment for a substance use disorder: intersectionality exists between being a woman, having a substance use disorder, and having been in jail or prison. After searching the literature, little or no empirical evidence might have been located on this specific triple-threat topic. Instead, the social worker will need to seek literature on each of the threats individually, and possibly will find literature on pairs of topics (see Figure 3-1). There exists some literature about women’s outcomes for treatment of a substance use disorder (a), some literature about women during and following incarceration (b), and some literature about substance use disorders and incarceration (c). Despite not having a direct line on the center of the intersecting spheres of literature (d), the social worker can develop at least a partial picture based on the overlapping literatures.

Figure 3-1. Venn diagram of intersecting literature sets.

Take a moment to complete the following activity. For each statement about empirical literature, decide if it is true or false.

Social Work 3401 Coursebook Copyright © by Dr. Audrey Begun is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License , except where otherwise noted.

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Writing a Literature Review

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A literature review is a document or section of a document that collects key sources on a topic and discusses those sources in conversation with each other (also called synthesis ). The lit review is an important genre in many disciplines, not just literature (i.e., the study of works of literature such as novels and plays). When we say “literature review” or refer to “the literature,” we are talking about the research ( scholarship ) in a given field. You will often see the terms “the research,” “the scholarship,” and “the literature” used mostly interchangeably.

Where, when, and why would I write a lit review?

There are a number of different situations where you might write a literature review, each with slightly different expectations; different disciplines, too, have field-specific expectations for what a literature review is and does. For instance, in the humanities, authors might include more overt argumentation and interpretation of source material in their literature reviews, whereas in the sciences, authors are more likely to report study designs and results in their literature reviews; these differences reflect these disciplines’ purposes and conventions in scholarship. You should always look at examples from your own discipline and talk to professors or mentors in your field to be sure you understand your discipline’s conventions, for literature reviews as well as for any other genre.

A literature review can be a part of a research paper or scholarly article, usually falling after the introduction and before the research methods sections. In these cases, the lit review just needs to cover scholarship that is important to the issue you are writing about; sometimes it will also cover key sources that informed your research methodology.

Lit reviews can also be standalone pieces, either as assignments in a class or as publications. In a class, a lit review may be assigned to help students familiarize themselves with a topic and with scholarship in their field, get an idea of the other researchers working on the topic they’re interested in, find gaps in existing research in order to propose new projects, and/or develop a theoretical framework and methodology for later research. As a publication, a lit review usually is meant to help make other scholars’ lives easier by collecting and summarizing, synthesizing, and analyzing existing research on a topic. This can be especially helpful for students or scholars getting into a new research area, or for directing an entire community of scholars toward questions that have not yet been answered.

What are the parts of a lit review?

Most lit reviews use a basic introduction-body-conclusion structure; if your lit review is part of a larger paper, the introduction and conclusion pieces may be just a few sentences while you focus most of your attention on the body. If your lit review is a standalone piece, the introduction and conclusion take up more space and give you a place to discuss your goals, research methods, and conclusions separately from where you discuss the literature itself.

Introduction:

• An introductory paragraph that explains what your working topic and thesis is
• A forecast of key topics or texts that will appear in the review
• Potentially, a description of how you found sources and how you analyzed them for inclusion and discussion in the review (more often found in published, standalone literature reviews than in lit review sections in an article or research paper)
• 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 sentence to draw connections, comparisons, and contrasts.

Conclusion:

• Summarize the key findings you have taken from the literature and emphasize their significance
• Connect it back to your primary research question

How should I organize my lit review?

Lit reviews can take many different organizational patterns depending on what you are trying to accomplish with the review. Here are some examples:

• Chronological : The simplest approach is to trace the development of the topic over time, which helps familiarize the audience with the topic (for instance if you are introducing something that is not commonly known in your field). If you choose this strategy, be careful to avoid simply listing and summarizing sources in order. Try to analyze the patterns, turning points, and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred (as mentioned previously, this may not be appropriate in your discipline — check with a teacher or mentor if you’re unsure).
• Thematic : If you have found some recurring central themes that you will continue working with throughout your piece, you can organize your literature review into subsections that address different aspects of the topic. For example, if you are reviewing literature about women and religion, key themes can include the role of women in churches and the religious attitude towards women.
• Qualitative versus quantitative research
• Empirical versus theoretical scholarship
• Divide the research by sociological, historical, or cultural sources
• Theoretical : In many humanities articles, the literature review is the foundation for the theoretical framework. You can use it to discuss various theories, models, and definitions of key concepts. You can argue for the relevance of a specific theoretical approach or combine various theorical concepts to create a framework for your research.

What are some strategies or tips I can use while writing my lit review?

Any lit review is only as good as the research it discusses; make sure your sources are well-chosen and your research is thorough. Don’t be afraid to do more research if you discover a new thread as you’re writing. More info on the research process is available in our "Conducting Research" resources .

As you’re doing your research, create an annotated bibliography ( see our page on the this type of document ). Much of the information used in an annotated bibliography can be used also in a literature review, so you’ll be not only partially drafting your lit review as you research, but also developing your sense of the larger conversation going on among scholars, professionals, and any other stakeholders in your topic.

Usually you will need to synthesize research rather than just summarizing it. This means drawing connections between sources to create a picture of the scholarly conversation on a topic over time. Many student writers struggle to synthesize because they feel they don’t have anything to add to the scholars they are citing; here are some strategies to help you:

• It often helps to remember that the point of these kinds of syntheses is to show your readers how you understand your research, to help them read the rest of your paper.
• Writing teachers often say synthesis is like hosting a dinner party: imagine all your sources are together in a room, discussing your topic. What are they saying to each other?
• Look at the in-text citations in each paragraph. Are you citing just one source for each paragraph? This usually indicates summary only. When you have multiple sources cited in a paragraph, you are more likely to be synthesizing them (not always, but often
• Read more about synthesis here.

The most interesting literature reviews are often written as arguments (again, as mentioned at the beginning of the page, this is discipline-specific and doesn’t work for all situations). Often, the literature review is where you can establish your research as filling a particular gap or as relevant in a particular way. You have some chance to do this in your introduction in an article, but the literature review section gives a more extended opportunity to establish the conversation in the way you would like your readers to see it. You can choose the intellectual lineage you would like to be part of and whose definitions matter most to your thinking (mostly humanities-specific, but this goes for sciences as well). In addressing these points, you argue for your place in the conversation, which tends to make the lit review more compelling than a simple reporting of other sources.

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• What is a Literature Review? | Guide, Template, & Examples

What is a Literature Review? | Guide, Template, & Examples

Published on 22 February 2022 by Shona McCombes . Revised on 7 June 2022.

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.

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 summarise sources – it analyses, synthesises, and critically evaluates to give a clear picture of the state of knowledge on the subject.

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Table of contents

Why write 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, frequently asked questions about literature reviews, introduction.

• Quick Run-through
• Step 1 & 2

When you write a dissertation or thesis, you will 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 scholarly context
• Develop a theoretical framework and methodology for your research
• Position yourself in relation to other researchers and theorists
• Show how your dissertation addresses a gap or contributes to a debate

You might also have to write a literature review as a stand-alone assignment. In this case, the purpose is to evaluate the current state of research and demonstrate your knowledge of scholarly debates around a topic.

The content will look slightly different in each case, but the process of conducting a literature review follows the same steps. 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 objectives and questions .

If you are writing a literature review as a stand-alone assignment, you will have to choose a focus and develop a central question to direct your search. Unlike a dissertation research question, this question has to be answerable without collecting original data. You should be able to answer it based only on a review of existing publications.

Make a list of keywords

Start by creating a list of keywords related to your research topic. 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 if 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 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 use boolean operators to help narrow down your search:

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.

To identify the most important publications on your topic, take note of recurring citations. If the same authors, books or articles keep appearing in your reading, make sure to seek them out.

You probably won’t be able to read absolutely everything that has been written on the topic – you’ll have to evaluate which sources are most relevant to your questions.

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?
• How does the publication contribute to your understanding of the topic? What are its key insights and arguments?
• 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 find out how many times an article has been cited on Google Scholar – a high citation count means the article has been influential in the field, and should certainly be included in your literature review.

The scope of your review will depend on your topic and discipline: in the sciences you usually only review recent literature, but in the humanities you might take a long historical perspective (for example, to trace how a concept has changed in meaning over time).

Remember that you can use our template to summarise and evaluate sources you’re thinking about using!

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’s important to keep track of your sources with references to avoid plagiarism . It can be helpful to make an annotated bibliography, where you compile full reference 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.

You can use our free APA Reference Generator for quick, correct, consistent citations.

To begin organising your literature review’s argument and structure, you need to 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 organising the body of a literature review. You should have a rough idea of your strategy before you start writing.

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 summarising sources in order.

Try to analyse 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 organise 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.

If you are writing the literature review as part of your dissertation or thesis, reiterate your central problem or research question and give a brief summary of the scholarly context. You can emphasise the timeliness of the topic (“many recent studies have focused on the problem of x”) or highlight a gap in the literature (“while there has been much research on x, few researchers have taken y into consideration”).

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, make sure to follow these tips:

• Summarise and synthesise: give an overview of the main points of each source and combine them into a coherent whole.
• Analyse and interpret: don’t just paraphrase other researchers – add your own interpretations, 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 transitions and topic sentences to draw connections, comparisons and contrasts.

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

If the literature review is part of your dissertation or thesis, reiterate how your research addresses gaps and contributes new knowledge, or discuss how you have drawn on existing theories and methods to build a framework for your research. This can lead directly into your methodology section.

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 dissertation , thesis, research paper , or proposal .

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

• To familiarise 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  dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .

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SWRK 330 - Social Work Research Methods

• Literature Reviews and Empirical Research
• Databases and Search Tips
• Article Citations
• Scholarly Journal Evaulation
• Statistical Sources
• Books and eBooks

What is a Literature Review?

Empirical research.

• Annotated Bibliographies

A literature review  summarizes and discusses previous publications  on a topic.

It should also:

explore past research and its strengths and weaknesses.

be used to validate the target and methods you have chosen for your proposed research.

consist of books and scholarly journals that provide research examples of populations or settings similar to your own, as well as community resources to document the need for your proposed research.

The literature review does not present new  primary  scholarship. 

be completed in the correct citation format requested by your professor  (see the  C itations Tab)

Access Purdue  OWL's Social Work Literature Review Guidelines here .  

Empirical Research  is  research  that is based on experimentation or observation, i.e. Evidence. Such  research  is often conducted to answer a specific question or to test a hypothesis (educated guess).

How do you know if a study is empirical? Read the subheadings within the article, book, or report and look for a description of the research "methodology."  Ask yourself: Could I recreate this study and test these results?

These are some key features to look for when identifying empirical research.

NOTE:  Not all of these features will be in every empirical research article, some may be excluded, use this only as a guide.

• Statement of methodology
• Research questions are clear and measurable
• Individuals, group, subjects which are being studied are identified/defined
• Data is presented regarding the findings
• Controls or instruments such as surveys or tests were conducted
• There is a literature review
• There is discussion of the results included
• Citations/references are included

See also Empirical Research Guide

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What is a Literature Review? How to Write It (with Examples)

A literature review is a critical analysis and synthesis of existing research on a particular topic. It provides an overview of the current state of knowledge, identifies gaps, and highlights key findings in the literature. 1 The purpose of a literature review is to situate your own research within the context of existing scholarship, demonstrating your understanding of the topic and showing how your work contributes to the ongoing conversation in the field. Learning how to write a literature review is a critical tool for successful research. Your ability to summarize and synthesize prior research pertaining to a certain topic demonstrates your grasp on the topic of study, and assists in the learning process. 

Table of Contents

• What is the purpose of literature review? 
• a. Habitat Loss and Species Extinction: 
• b. Range Shifts and Phenological Changes: 
• c. Ocean Acidification and Coral Reefs: 
• d. Adaptive Strategies and Conservation Efforts: 

How to write a good literature review 

• Choose a Topic and Define the Research Question: 
• Decide on the Scope of Your Review: 
• Select Databases for Searches: 
• Conduct Searches and Keep Track: 
• Review the Literature: 
• Organize and Write Your Literature Review: 
• How to write a literature review faster with Paperpal? 
• Frequently asked questions 

What is a literature review?

A well-conducted literature review demonstrates the researcher’s familiarity with the existing literature, establishes the context for their own research, and contributes to scholarly conversations on the topic. One of the purposes of a literature review is also to help researchers avoid duplicating previous work and ensure that their research is informed by and builds upon the existing body of knowledge.

What is the purpose of literature review?

A literature review serves several important purposes within academic and research contexts. Here are some key objectives and functions of a literature review: 2  

1. Contextualizing the Research Problem: The literature review provides a background and context for the research problem under investigation. It helps to situate the study within the existing body of knowledge. 

2. Identifying Gaps in Knowledge: By identifying gaps, contradictions, or areas requiring further research, the researcher can shape the research question and justify the significance of the study. This is crucial for ensuring that the new research contributes something novel to the field. 

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3. Understanding Theoretical and Conceptual Frameworks: Literature reviews help researchers gain an understanding of the theoretical and conceptual frameworks used in previous studies. This aids in the development of a theoretical framework for the current research. 

4. Providing Methodological Insights: Another purpose of literature reviews is that it allows researchers to learn about the methodologies employed in previous studies. This can help in choosing appropriate research methods for the current study and avoiding pitfalls that others may have encountered. 

5. Establishing Credibility: A well-conducted literature review demonstrates the researcher’s familiarity with existing scholarship, establishing their credibility and expertise in the field. It also helps in building a solid foundation for the new research. 

6. Informing Hypotheses or Research Questions: The literature review guides the formulation of hypotheses or research questions by highlighting relevant findings and areas of uncertainty in existing literature. 

Literature review example

Let’s delve deeper with a literature review example: Let’s say your literature review is about the impact of climate change on biodiversity. You might format your literature review into sections such as the effects of climate change on habitat loss and species extinction, phenological changes, and marine biodiversity. Each section would then summarize and analyze relevant studies in those areas, highlighting key findings and identifying gaps in the research. The review would conclude by emphasizing the need for further research on specific aspects of the relationship between climate change and biodiversity. The following literature review template provides a glimpse into the recommended literature review structure and content, demonstrating how research findings are organized around specific themes within a broader topic. 

Literature Review on Climate Change Impacts on Biodiversity:

Climate change is a global phenomenon with far-reaching consequences, including significant impacts on biodiversity. This literature review synthesizes key findings from various studies: 

a. Habitat Loss and Species Extinction:

Climate change-induced alterations in temperature and precipitation patterns contribute to habitat loss, affecting numerous species (Thomas et al., 2004). The review discusses how these changes increase the risk of extinction, particularly for species with specific habitat requirements. 

b. Range Shifts and Phenological Changes:

Observations of range shifts and changes in the timing of biological events (phenology) are documented in response to changing climatic conditions (Parmesan & Yohe, 2003). These shifts affect ecosystems and may lead to mismatches between species and their resources. 

c. Ocean Acidification and Coral Reefs:

The review explores the impact of climate change on marine biodiversity, emphasizing ocean acidification’s threat to coral reefs (Hoegh-Guldberg et al., 2007). Changes in pH levels negatively affect coral calcification, disrupting the delicate balance of marine ecosystems. 

d. Adaptive Strategies and Conservation Efforts:

Recognizing the urgency of the situation, the literature review discusses various adaptive strategies adopted by species and conservation efforts aimed at mitigating the impacts of climate change on biodiversity (Hannah et al., 2007). It emphasizes the importance of interdisciplinary approaches for effective conservation planning. 

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Writing a literature review involves summarizing and synthesizing existing research on a particular topic. A good literature review format should include the following elements. 

Introduction: The introduction sets the stage for your literature review, providing context and introducing the main focus of your review. 

• Opening Statement: Begin with a general statement about the broader topic and its significance in the field. 
• Scope and Purpose: Clearly define the scope of your literature review. Explain the specific research question or objective you aim to address. 
• Organizational Framework: Briefly outline the structure of your literature review, indicating how you will categorize and discuss the existing research. 
• Significance of the Study: Highlight why your literature review is important and how it contributes to the understanding of the chosen topic. 
• Thesis Statement: Conclude the introduction with a concise thesis statement that outlines the main argument or perspective you will develop in the body of the literature review. 

Body: The body of the literature review is where you provide a comprehensive analysis of existing literature, grouping studies based on themes, methodologies, or other relevant criteria. 

• Organize by Theme or Concept: Group studies that share common themes, concepts, or methodologies. Discuss each theme or concept in detail, summarizing key findings and identifying gaps or areas of disagreement. 
• Critical Analysis: Evaluate the strengths and weaknesses of each study. Discuss the methodologies used, the quality of evidence, and the overall contribution of each work to the understanding of the topic. 
• Synthesis of Findings: Synthesize the information from different studies to highlight trends, patterns, or areas of consensus in the literature. 
• Identification of Gaps: Discuss any gaps or limitations in the existing research and explain how your review contributes to filling these gaps. 
• Transition between Sections: Provide smooth transitions between different themes or concepts to maintain the flow of your literature review. 

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Conclusion: The conclusion of your literature review should summarize the main findings, highlight the contributions of the review, and suggest avenues for future research. 

• Summary of Key Findings: Recap the main findings from the literature and restate how they contribute to your research question or objective. 
• Contributions to the Field: Discuss the overall contribution of your literature review to the existing knowledge in the field. 
• Implications and Applications: Explore the practical implications of the findings and suggest how they might impact future research or practice. 
• Recommendations for Future Research: Identify areas that require further investigation and propose potential directions for future research in the field. 
• Final Thoughts: Conclude with a final reflection on the importance of your literature review and its relevance to the broader academic community. 

Conducting a literature review

Conducting a literature review is an essential step in research that involves reviewing and analyzing existing literature on a specific topic. It’s important to know how to do a literature review effectively, so here are the steps to follow: 1  

Choose a Topic and Define the Research Question:

• Select a topic that is relevant to your field of study. 
• Clearly define your research question or objective. Determine what specific aspect of the topic do you want to explore? 

Decide on the Scope of Your Review:

• Determine the timeframe for your literature review. Are you focusing on recent developments, or do you want a historical overview? 
• Consider the geographical scope. Is your review global, or are you focusing on a specific region? 
• Define the inclusion and exclusion criteria. What types of sources will you include? Are there specific types of studies or publications you will exclude? 

Select Databases for Searches:

• Identify relevant databases for your field. Examples include PubMed, IEEE Xplore, Scopus, Web of Science, and Google Scholar. 
• Consider searching in library catalogs, institutional repositories, and specialized databases related to your topic. 

Conduct Searches and Keep Track:

• Develop a systematic search strategy using keywords, Boolean operators (AND, OR, NOT), and other search techniques. 
• Record and document your search strategy for transparency and replicability. 
• Keep track of the articles, including publication details, abstracts, and links. Use citation management tools like EndNote, Zotero, or Mendeley to organize your references. 

Review the Literature:

• Evaluate the relevance and quality of each source. Consider the methodology, sample size, and results of studies. 
• Organize the literature by themes or key concepts. Identify patterns, trends, and gaps in the existing research. 
• Summarize key findings and arguments from each source. Compare and contrast different perspectives. 
• Identify areas where there is a consensus in the literature and where there are conflicting opinions. 
• Provide critical analysis and synthesis of the literature. What are the strengths and weaknesses of existing research? 

Organize and Write Your Literature Review:

• Literature review outline should be based on themes, chronological order, or methodological approaches. 
• Write a clear and coherent narrative that synthesizes the information gathered. 
• Use proper citations for each source and ensure consistency in your citation style (APA, MLA, Chicago, etc.). 
• Conclude your literature review by summarizing key findings, identifying gaps, and suggesting areas for future research. 

Whether you’re exploring a new research field or finding new angles to develop an existing topic, sifting through hundreds of papers can take more time than you have to spare. But what if you could find science-backed insights with verified citations in seconds? That’s the power of Paperpal’s new Research feature!  

How to write a literature review faster with Paperpal?

Paperpal, an AI writing assistant, integrates powerful academic search capabilities within its writing platform. With the Research feature, you get 100% factual insights, with citations backed by 250M+ verified research articles, directly within your writing interface with the option to save relevant references in your Citation Library. By eliminating the need to switch tabs to find answers to all your research questions, Paperpal saves time and helps you stay focused on your writing.   

Here’s how to use the Research feature:  

• Ask a question: Get started with a new document on paperpal.com. Click on the “Research” feature and type your question in plain English. Paperpal will scour over 250 million research articles, including conference papers and preprints, to provide you with accurate insights and citations. 
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• Cite with Confidence: Paperpal makes it easy to incorporate relevant citations and references into your writing, ensuring your arguments are well-supported by credible sources. This translates to a polished, well-researched literature review. 

The literature review sample and detailed advice on writing and conducting a review will help you produce a well-structured report. But remember that a good literature review is an ongoing process, and it may be necessary to revisit and update it as your research progresses. By combining effortless research with an easy citation process, Paperpal Research streamlines the literature review process and empowers you to write faster and with more confidence. Try Paperpal Research now and see for yourself.  

Frequently asked questions

A literature review is a critical and comprehensive analysis of existing literature (published and unpublished works) on a specific topic or research question and provides a synthesis of the current state of knowledge in a particular field. A well-conducted literature review is crucial for researchers to build upon existing knowledge, avoid duplication of efforts, and contribute to the advancement of their field. It also helps researchers situate their work within a broader context and facilitates the development of a sound theoretical and conceptual framework for their studies.

Literature review is a crucial component of research writing, providing a solid background for a research paper’s investigation. The aim is to keep professionals up to date by providing an understanding of ongoing developments within a specific field, including research methods, and experimental techniques used in that field, and present that knowledge in the form of a written report. Also, the depth and breadth of the literature review emphasizes the credibility of the scholar in his or her field.  

Before writing a literature review, it’s essential to undertake several preparatory steps to ensure that your review is well-researched, organized, and focused. This includes choosing a topic of general interest to you and doing exploratory research on that topic, writing an annotated bibliography, and noting major points, especially those that relate to the position you have taken on the topic. 

Literature reviews and academic research papers are essential components of scholarly work but serve different purposes within the academic realm. 3 A literature review aims to provide a foundation for understanding the current state of research on a particular topic, identify gaps or controversies, and lay the groundwork for future research. Therefore, it draws heavily from existing academic sources, including books, journal articles, and other scholarly publications. In contrast, an academic research paper aims to present new knowledge, contribute to the academic discourse, and advance the understanding of a specific research question. Therefore, it involves a mix of existing literature (in the introduction and literature review sections) and original data or findings obtained through research methods. 

Literature reviews are essential components of academic and research papers, and various strategies can be employed to conduct them effectively. If you want to know how to write a literature review for a research paper, here are four common approaches that are often used by researchers.  Chronological Review: This strategy involves organizing the literature based on the chronological order of publication. It helps to trace the development of a topic over time, showing how ideas, theories, and research have evolved.  Thematic Review: Thematic reviews focus on identifying and analyzing themes or topics that cut across different studies. Instead of organizing the literature chronologically, it is grouped by key themes or concepts, allowing for a comprehensive exploration of various aspects of the topic.  Methodological Review: This strategy involves organizing the literature based on the research methods employed in different studies. It helps to highlight the strengths and weaknesses of various methodologies and allows the reader to evaluate the reliability and validity of the research findings.  Theoretical Review: A theoretical review examines the literature based on the theoretical frameworks used in different studies. This approach helps to identify the key theories that have been applied to the topic and assess their contributions to the understanding of the subject.  It’s important to note that these strategies are not mutually exclusive, and a literature review may combine elements of more than one approach. The choice of strategy depends on the research question, the nature of the literature available, and the goals of the review. Additionally, other strategies, such as integrative reviews or systematic reviews, may be employed depending on the specific requirements of the research.

The literature review format can vary depending on the specific publication guidelines. However, there are some common elements and structures that are often followed. Here is a general guideline for the format of a literature review:  Introduction:   Provide an overview of the topic.  Define the scope and purpose of the literature review.  State the research question or objective.  Body:   Organize the literature by themes, concepts, or chronology.  Critically analyze and evaluate each source.  Discuss the strengths and weaknesses of the studies.  Highlight any methodological limitations or biases.  Identify patterns, connections, or contradictions in the existing research.  Conclusion:   Summarize the key points discussed in the literature review.  Highlight the research gap.  Address the research question or objective stated in the introduction.  Highlight the contributions of the review and suggest directions for future research.

Both annotated bibliographies and literature reviews involve the examination of scholarly sources. While annotated bibliographies focus on individual sources with brief annotations, literature reviews provide a more in-depth, integrated, and comprehensive analysis of existing literature on a specific topic. The key differences are as follows: 

References 

• Denney, A. S., & Tewksbury, R. (2013). How to write a literature review.  Journal of criminal justice education ,  24 (2), 218-234. 
• Pan, M. L. (2016).  Preparing literature reviews: Qualitative and quantitative approaches . Taylor & Francis. 
• Cantero, C. (2019). How to write a literature review.  San José State University Writing Center . 

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Research Methods

• Getting Started
• Literature Review Research
• Research Design
• Research Design By Discipline
• SAGE Research Methods
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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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Organizing Your Social Sciences Research Paper

• 5. The Literature Review
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
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• Common Grammar Mistakes
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• Bibliography

A literature review surveys prior research published in books, scholarly articles, and any other sources relevant to a particular issue, area of research, or theory, and by so doing, provides a description, summary, and critical evaluation of these works in relation to the research problem being investigated. Literature reviews are designed to provide an overview of sources you have used in researching a particular topic and to demonstrate to your readers how your research fits within existing scholarship about the topic.

Fink, Arlene. Conducting Research Literature Reviews: From the Internet to Paper . Fourth edition. Thousand Oaks, CA: SAGE, 2014.

Importance of a Good Literature Review

A literature review may consist of simply a summary of key sources, but in the social sciences, a literature review usually has an organizational pattern and combines both summary and synthesis, often within specific conceptual categories . A summary is a recap of the important information of the source, but a synthesis is a re-organization, or a reshuffling, of that information in a way that informs how you are planning to investigate a research problem. The analytical features of a literature review might:

• Give a new interpretation of old material or combine new with old interpretations,
• Trace the intellectual progression of the field, including major debates,
• Depending on the situation, evaluate the sources and advise the reader on the most pertinent or relevant research, or
• Usually in the conclusion of a literature review, identify where gaps exist in how a problem has been researched to date.

Given this, the purpose of a literature review is to:

• Place each work in the context of its contribution to understanding the research problem being studied.
• Describe the relationship of each work to the others under consideration.
• Identify new ways to interpret prior research.
• Reveal any gaps that exist in the literature.
• Resolve conflicts amongst seemingly contradictory previous studies.
• Identify areas of prior scholarship to prevent duplication of effort.
• Point the way in fulfilling a need for additional research.
• Locate your own research within the context of existing literature [very important].

Fink, Arlene. Conducting Research Literature Reviews: From the Internet to Paper. 2nd ed. Thousand Oaks, CA: Sage, 2005; Hart, Chris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage Publications, 1998; Jesson, Jill. Doing Your Literature Review: Traditional and Systematic Techniques . Los Angeles, CA: SAGE, 2011; Knopf, Jeffrey W. "Doing a Literature Review." PS: Political Science and Politics 39 (January 2006): 127-132; Ridley, Diana. The Literature Review: A Step-by-Step Guide for Students . 2nd ed. Los Angeles, CA: SAGE, 2012.

Types of Literature Reviews

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 primary studies. Third, there are the perceptions, conclusions, opinion, and interpretations that are shared informally among scholars that become part of the body of epistemological traditions within the 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 a number of approaches you could adopt depending upon the type of analysis underpinning your study.

Argumentative Review This form examines literature selectively in order to support or refute an argument, deeply embedded 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 make summary claims of the sort found in systematic reviews [see below].

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 or research problems. A well-done integrative review meets the same standards as primary research in regard to clarity, rigor, and replication. This is the most common form of review in the social sciences.

Historical Review Few things rest in isolation from historical precedent. Historical literature reviews focus 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 [findings], but how they came about saying what they say [method of analysis]. Reviewing methods of analysis provides a framework of understanding at different levels [i.e. those of theory, substantive fields, research approaches, and data collection and analysis techniques], how researchers draw upon 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. This approach helps highlight ethical issues which you should be aware of and consider as you go through your own 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 analyze data from the studies that are included in the review. The goal is to deliberately document, critically evaluate, and summarize scientifically all of the research about a clearly defined research problem . 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?" This type of literature review is primarily applied to examining prior research studies in clinical medicine and allied health fields, but it is increasingly being used in the social sciences.

Theoretical Review The purpose of this form is to examine the corpus of theory that has accumulated in regard to an issue, concept, theory, phenomena. The theoretical literature review helps to 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.

NOTE: Most often the literature review will incorporate some combination of types. For example, a review that examines literature supporting or refuting an argument, assumption, or philosophical problem related to the research problem will also need to include writing supported by sources that establish the history of these arguments in the literature.

Baumeister, Roy F. and Mark R. Leary. "Writing Narrative Literature Reviews."  Review of General Psychology 1 (September 1997): 311-320; Mark R. Fink, Arlene. Conducting Research Literature Reviews: From the Internet to Paper . 2nd ed. Thousand Oaks, CA: Sage, 2005; Hart, Chris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage Publications, 1998; Kennedy, Mary M. "Defining a Literature." Educational Researcher 36 (April 2007): 139-147; Petticrew, Mark and Helen Roberts. Systematic Reviews in the Social Sciences: A Practical Guide . Malden, MA: Blackwell Publishers, 2006; Torracro, Richard. "Writing Integrative Literature Reviews: Guidelines and Examples." Human Resource Development Review 4 (September 2005): 356-367; Rocco, Tonette S. and Maria S. Plakhotnik. "Literature Reviews, Conceptual Frameworks, and Theoretical Frameworks: Terms, Functions, and Distinctions." Human Ressource Development Review 8 (March 2008): 120-130; Sutton, Anthea. Systematic Approaches to a Successful Literature Review . Los Angeles, CA: Sage Publications, 2016.

Structure and Writing Style

I.  Thinking About Your Literature Review

The structure of a literature review should include the following in support of understanding the research problem :

• An overview of the subject, issue, or theory under consideration, along with the objectives of the literature review,
• Division of works under review into themes or categories [e.g. works that support a particular position, those against, and those offering alternative approaches entirely],
• An explanation of how each work is similar to and how it varies from the others,
• Conclusions as to 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 their area of research.

The critical evaluation of each work should consider :

• Provenance -- what are the author's credentials? Are the author's arguments supported by evidence [e.g. primary historical material, case studies, narratives, statistics, recent scientific findings]?
• Methodology -- were the techniques used to identify, gather, and analyze the data appropriate to addressing the research problem? Was the sample size appropriate? Were the results effectively interpreted and reported?
• Objectivity -- is the author's perspective even-handed or prejudicial? Is contrary data considered or is certain pertinent information ignored to prove the author's point?
• Persuasiveness -- which of the author's theses are most convincing or least convincing?
• Validity -- are the author's arguments and conclusions convincing? Does the work ultimately contribute in any significant way to an understanding of the subject?

II.  Development of the Literature Review

Four Basic Stages of Writing 1.  Problem formulation -- which topic or field is being examined and what are its component issues? 2.  Literature search -- finding materials relevant to the subject being explored. 3.  Data evaluation -- determining which literature makes a significant contribution to the understanding of the topic. 4.  Analysis and interpretation -- discussing the findings and conclusions of pertinent literature.

Consider the following issues before writing the literature review: Clarify If your assignment is not specific about what form your literature review should take, seek clarification from your professor by asking these questions: 1.  Roughly how many sources would be appropriate to include? 2.  What types of sources should I review (books, journal articles, websites; scholarly versus popular sources)? 3.  Should I summarize, synthesize, or critique sources by discussing a common theme or issue? 4.  Should I evaluate the sources in any way beyond evaluating how they relate to understanding the research problem? 5.  Should I provide subheadings and other background information, such as definitions and/or a history? Find Models Use the exercise of reviewing the literature to examine how authors in your discipline or area of interest have composed their literature review sections. Read them to get a sense of the types of themes you might want to look for in your own research or to identify ways to organize your final review. The bibliography or reference section of sources you've already read, such as required readings in the course syllabus, are also excellent entry points into your own research. Narrow the Topic The narrower your topic, the easier it will be to limit the number of sources you need to read in order to obtain a good survey of relevant resources. Your professor will probably not expect you to read everything that's available about the topic, but you'll make the act of reviewing easier if you first limit scope of the research problem. A good strategy is to begin by searching the USC Libraries Catalog for recent books about the topic and review the table of contents for chapters that focuses on specific issues. You can also review the indexes of books to find references to specific issues that can serve as the focus of your research. For example, a book surveying the history of the Israeli-Palestinian conflict may include a chapter on the role Egypt has played in mediating the conflict, or look in the index for the pages where Egypt is mentioned in the text. Consider Whether Your Sources are Current Some disciplines require that you use information that is as current as possible. This is particularly true in disciplines in medicine and the sciences where research conducted becomes obsolete very quickly as new discoveries are made. However, when writing a review in the social sciences, a survey of the history of the literature may be required. In other words, a complete understanding the research problem requires you to deliberately examine how knowledge and perspectives have changed over time. Sort through other current bibliographies or literature reviews in the field to get a sense of what your discipline expects. You can also use this method to explore what is considered by scholars to be a "hot topic" and what is not.

III.  Ways to Organize Your Literature Review

Chronology of Events If your review follows the chronological method, you could write about the materials according to when they were published. This approach should only be followed if a clear path of research building on previous research can be identified and that these trends follow a clear chronological order of development. For example, a literature review that focuses on continuing research about the emergence of German economic power after the fall of the Soviet Union. By Publication Order your sources by publication chronology, then, only if the order demonstrates a more important trend. For instance, you could order a review of literature on environmental studies of brown fields if the progression revealed, for example, a change in the soil collection practices of the researchers who wrote and/or conducted the studies. Thematic [“conceptual categories”] A thematic literature review is the most common approach to summarizing prior research in the social and behavioral sciences. Thematic reviews are organized around a topic or issue, rather than the progression of time, although the progression of time may still be incorporated into a thematic review. For example, a review of the Internet’s impact on American presidential politics could focus on the development of online political satire. While the study focuses on one topic, the Internet’s impact on American presidential politics, it would still be organized chronologically reflecting technological developments in media. The difference in this example between a "chronological" and a "thematic" approach is what is emphasized the most: themes related to the role of the Internet in presidential politics. Note that more authentic thematic reviews tend to break away from chronological order. A review organized in this manner would shift between time periods within each section according to the point being made. Methodological A methodological approach focuses on the methods utilized by the researcher. For the Internet in American presidential politics project, one methodological approach would be to look at cultural differences between the portrayal of American presidents on American, British, and French websites. Or the review might focus on the fundraising impact of the Internet on a particular political party. A methodological scope will influence either the types of documents in the review or the way in which these documents are discussed.

Other Sections of Your Literature Review Once you've decided on the organizational method for your literature review, the sections you need to include in the paper should be easy to figure out because they arise from your organizational strategy. In other words, a chronological review would have subsections for each vital time period; a thematic review would have subtopics based upon factors that relate to the theme or issue. However, sometimes you may need to add additional sections that are necessary for your study, but do not fit in the organizational strategy of the body. What other sections you include in the body is up to you. However, only include what is necessary for the reader to locate your study within the larger scholarship about the research problem.

Here are examples of other sections, usually in the form of a single paragraph, you may need to include depending on the type of review you write:

• Current Situation : Information necessary to understand the current topic or focus of the literature review.
• Sources Used : Describes the methods and resources [e.g., databases] you used to identify the literature you reviewed.
• History : The chronological progression of the field, the research literature, or an idea that is necessary to understand the literature review, if the body of the literature review is not already a chronology.
• Selection Methods : Criteria you used to select (and perhaps exclude) sources in your literature review. For instance, you might explain that your review includes only peer-reviewed [i.e., scholarly] sources.
• Standards : Description of the way in which you present your information.
• Questions for Further Research : What questions about the field has the review sparked? How will you further your research as a result of the review?

IV.  Writing Your Literature Review

Once you've settled on how to organize your literature review, you're ready to write each section. When writing your review, keep in mind these issues.

Use Evidence A literature review section is, in this sense, just like any other academic research paper. Your interpretation of the available sources must be backed up with evidence [citations] that demonstrates that what you are saying is valid. Be Selective Select only the most important points in each source to highlight in the review. The type of information you choose to mention should relate directly to the research problem, whether it is thematic, methodological, or chronological. Related items that provide additional information, but that are not key to understanding the research problem, can be included in a list of further readings . Use Quotes Sparingly Some short quotes are appropriate if you want to emphasize a point, or if what an author stated cannot be easily paraphrased. Sometimes you may need to quote certain terminology that was coined by the author, is not common knowledge, or taken directly from the study. Do not use extensive quotes as a substitute for using your own words in reviewing the literature. Summarize and Synthesize Remember to summarize and synthesize your sources within each thematic paragraph as well as throughout the review. Recapitulate important features of a research study, but then synthesize it by rephrasing the study's significance and relating it to your own work and the work of others. Keep Your Own Voice While the literature review presents others' ideas, your voice [the writer's] should remain front and center. For example, weave references to other sources into what you are writing but maintain your own voice by starting and ending the paragraph with your own ideas and wording. Use Caution When Paraphrasing When paraphrasing a source that is not your own, be sure to represent the author's information or opinions accurately and in your own words. Even when paraphrasing an author’s work, you still must provide a citation to that work.

V.  Common Mistakes to Avoid

These are the most common mistakes made in reviewing social science research literature.

• Sources in your literature review do not clearly relate to the research problem;
• You do not take sufficient time to define and identify the most relevant sources to use in the literature review related to the research problem;
• Relies exclusively on secondary analytical sources rather than including relevant primary research studies or data;
• Uncritically accepts another researcher's findings and interpretations as valid, rather than examining critically all aspects of the research design and analysis;
• Does not describe the search procedures that were used in identifying the literature to review;
• Reports isolated statistical results rather than synthesizing them in chi-squared or meta-analytic methods; and,
• Only includes research that validates assumptions and does not consider contrary findings and alternative interpretations found in the literature.

Cook, Kathleen E. and Elise Murowchick. “Do Literature Review Skills Transfer from One Course to Another?” Psychology Learning and Teaching 13 (March 2014): 3-11; Fink, Arlene. Conducting Research Literature Reviews: From the Internet to Paper . 2nd ed. Thousand Oaks, CA: Sage, 2005; Hart, Chris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage Publications, 1998; Jesson, Jill. Doing Your Literature Review: Traditional and Systematic Techniques . London: SAGE, 2011; Literature Review Handout. Online Writing Center. Liberty University; Literature Reviews. The Writing Center. University of North Carolina; Onwuegbuzie, Anthony J. and Rebecca Frels. Seven Steps to a Comprehensive Literature Review: A Multimodal and Cultural Approach . Los Angeles, CA: SAGE, 2016; Ridley, Diana. The Literature Review: A Step-by-Step Guide for Students . 2nd ed. Los Angeles, CA: SAGE, 2012; Randolph, Justus J. “A Guide to Writing the Dissertation Literature Review." Practical Assessment, Research, and Evaluation. vol. 14, June 2009; Sutton, Anthea. Systematic Approaches to a Successful Literature Review . Los Angeles, CA: Sage Publications, 2016; Taylor, Dena. The Literature Review: A Few Tips On Conducting It. University College Writing Centre. University of Toronto; Writing a Literature Review. Academic Skills Centre. University of Canberra.

Writing Tip

Break Out of Your Disciplinary Box!

Thinking interdisciplinarily about a research problem can be a rewarding exercise in applying new ideas, theories, or concepts to an old problem. For example, what might cultural anthropologists say about the continuing conflict in the Middle East? In what ways might geographers view the need for better distribution of social service agencies in large cities than how social workers might study the issue? You don’t want to substitute a thorough review of core research literature in your discipline for studies conducted in other fields of study. However, particularly in the social sciences, thinking about research problems from multiple vectors is a key strategy for finding new solutions to a problem or gaining a new perspective. Consult with a librarian about identifying research databases in other disciplines; almost every field of study has at least one comprehensive database devoted to indexing its research literature.

Frodeman, Robert. The Oxford Handbook of Interdisciplinarity . New York: Oxford University Press, 2010.

Another Writing Tip

Don't Just Review for Content!

While conducting a review of the literature, maximize the time you devote to writing this part of your paper by thinking broadly about what you should be looking for and evaluating. Review not just what scholars are saying, but how are they saying it. Some questions to ask:

• How are they organizing their ideas?
• What methods have they used to study the problem?
• What theories have been used to explain, predict, or understand their research problem?
• What sources have they cited to support their conclusions?
• How have they used non-textual elements [e.g., charts, graphs, figures, etc.] to illustrate key points?

When you begin to write your literature review section, you'll be glad you dug deeper into how the research was designed and constructed because it establishes a means for developing more substantial analysis and interpretation of the research problem.

Hart, Chris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage Publications, 1 998.

Yet Another Writing Tip

When Do I Know I Can Stop Looking and Move On?

Here are several strategies you can utilize to assess whether you've thoroughly reviewed the literature:

• Look for repeating patterns in the research findings . If the same thing is being said, just by different people, then this likely demonstrates that the research problem has hit a conceptual dead end. At this point consider: Does your study extend current research?  Does it forge a new path? Or, does is merely add more of the same thing being said?
• Look at sources the authors cite to in their work . If you begin to see the same researchers cited again and again, then this is often an indication that no new ideas have been generated to address the research problem.
• Search Google Scholar to identify who has subsequently cited leading scholars already identified in your literature review [see next sub-tab]. This is called citation tracking and there are a number of sources that can help you identify who has cited whom, particularly scholars from outside of your discipline. Here again, if the same authors are being cited again and again, this may indicate no new literature has been written on the topic.

Onwuegbuzie, Anthony J. and Rebecca Frels. Seven Steps to a Comprehensive Literature Review: A Multimodal and Cultural Approach . Los Angeles, CA: Sage, 2016; Sutton, Anthea. Systematic Approaches to a Successful Literature Review . Los Angeles, CA: Sage Publications, 2016.

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Literature Reviews

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• 1. Define your research question
• 2. Plan your search
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Definition: A literature review is a systematic examination and synthesis of existing scholarly research on a specific topic or subject.

Purpose: It serves to provide a comprehensive overview of the current state of knowledge within a particular field.

Analysis: Involves critically evaluating and summarizing key findings, methodologies, and debates found in academic literature.

Identifying Gaps: Aims to pinpoint areas where there is a lack of research or unresolved questions, highlighting opportunities for further investigation.

Contextualization: Enables researchers to understand how their work fits into the broader academic conversation and contributes to the existing body of knowledge.

tl;dr  A literature review critically examines and synthesizes existing scholarly research and publications on a specific topic to provide a comprehensive understanding of the current state of knowledge in the field.

What is a literature review NOT?

❌ An annotated bibliography

❌ Original research

❌ A summary

❌ Something to be conducted at the end of your research

❌ An opinion piece

❌ A chronological compilation of studies

The reason for conducting a literature review is to:

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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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Project Chapter Two: Literature Review and Steps to Writing Empirical Review

Kindly share this story:

• Conceptual review
• Theoretical review,
• Empirical review or review of empirical works of literature/studies, and lastly
• Conclusion or Summary of the literature reviewed.
• Decide on a topic
• Highlight the studies/literature that you will review in the empirical review
• Analyze the works of literature separately.
• Summarize the literature in table or concept map format.
• Synthesize the literature and then proceed to write your empirical review.

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A systematic literature review of empirical research on ChatGPT in education

• Open access
• Published: 26 May 2024
• Volume 3 , article number  60 , ( 2024 )

Cite this article

You have full access to this open access article

• Yazid Albadarin   ORCID: orcid.org/0009-0005-8068-8902 1 ,
• Mohammed Saqr 1 ,
• Nicolas Pope 1 &
• Markku Tukiainen 1  

Over the last four decades, studies have investigated the incorporation of Artificial Intelligence (AI) into education. A recent prominent AI-powered technology that has impacted the education sector is ChatGPT. This article provides a systematic review of 14 empirical studies incorporating ChatGPT into various educational settings, published in 2022 and before the 10th of April 2023—the date of conducting the search process. It carefully followed the essential steps outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines, as well as Okoli’s (Okoli in Commun Assoc Inf Syst, 2015) steps for conducting a rigorous and transparent systematic review. In this review, we aimed to explore how students and teachers have utilized ChatGPT in various educational settings, as well as the primary findings of those studies. By employing Creswell’s (Creswell in Educational research: planning, conducting, and evaluating quantitative and qualitative research [Ebook], Pearson Education, London, 2015) coding techniques for data extraction and interpretation, we sought to gain insight into their initial attempts at ChatGPT incorporation into education. This approach also enabled us to extract insights and considerations that can facilitate its effective and responsible use in future educational contexts. The results of this review show that learners have utilized ChatGPT as a virtual intelligent assistant, where it offered instant feedback, on-demand answers, and explanations of complex topics. Additionally, learners have used it to enhance their writing and language skills by generating ideas, composing essays, summarizing, translating, paraphrasing texts, or checking grammar. Moreover, learners turned to it as an aiding tool to facilitate their directed and personalized learning by assisting in understanding concepts and homework, providing structured learning plans, and clarifying assignments and tasks. However, the results of specific studies (n = 3, 21.4%) show that overuse of ChatGPT may negatively impact innovative capacities and collaborative learning competencies among learners. Educators, on the other hand, have utilized ChatGPT to create lesson plans, generate quizzes, and provide additional resources, which helped them enhance their productivity and efficiency and promote different teaching methodologies. Despite these benefits, the majority of the reviewed studies recommend the importance of conducting structured training, support, and clear guidelines for both learners and educators to mitigate the drawbacks. This includes developing critical evaluation skills to assess the accuracy and relevance of information provided by ChatGPT, as well as strategies for integrating human interaction and collaboration into learning activities that involve AI tools. Furthermore, they also recommend ongoing research and proactive dialogue with policymakers, stakeholders, and educational practitioners to refine and enhance the use of AI in learning environments. This review could serve as an insightful resource for practitioners who seek to integrate ChatGPT into education and stimulate further research in the field.

Avoid common mistakes on your manuscript.

1 Introduction

Educational technology, a rapidly evolving field, plays a crucial role in reshaping the landscape of teaching and learning [ 82 ]. One of the most transformative technological innovations of our era that has influenced the field of education is Artificial Intelligence (AI) [ 50 ]. Over the last four decades, AI in education (AIEd) has gained remarkable attention for its potential to make significant advancements in learning, instructional methods, and administrative tasks within educational settings [ 11 ]. In particular, a large language model (LLM), a type of AI algorithm that applies artificial neural networks (ANNs) and uses massively large data sets to understand, summarize, generate, and predict new content that is almost difficult to differentiate from human creations [ 79 ], has opened up novel possibilities for enhancing various aspects of education, from content creation to personalized instruction [ 35 ]. Chatbots that leverage the capabilities of LLMs to understand and generate human-like responses have also presented the capacity to enhance student learning and educational outcomes by engaging students, offering timely support, and fostering interactive learning experiences [ 46 ].

The ongoing and remarkable technological advancements in chatbots have made their use more convenient, increasingly natural and effortless, and have expanded their potential for deployment across various domains [ 70 ]. One prominent example of chatbot applications is the Chat Generative Pre-Trained Transformer, known as ChatGPT, which was introduced by OpenAI, a leading AI research lab, on November 30th, 2022. ChatGPT employs a variety of deep learning techniques to generate human-like text, with a particular focus on recurrent neural networks (RNNs). Long short-term memory (LSTM) allows it to grasp the context of the text being processed and retain information from previous inputs. Also, the transformer architecture, a neural network architecture based on the self-attention mechanism, allows it to analyze specific parts of the input, thereby enabling it to produce more natural-sounding and coherent output. Additionally, the unsupervised generative pre-training and the fine-tuning methods allow ChatGPT to generate more relevant and accurate text for specific tasks [ 31 , 62 ]. Furthermore, reinforcement learning from human feedback (RLHF), a machine learning approach that combines reinforcement learning techniques with human-provided feedback, has helped improve ChatGPT’s model by accelerating the learning process and making it significantly more efficient.

This cutting-edge natural language processing (NLP) tool is widely recognized as one of today's most advanced LLMs-based chatbots [ 70 ], allowing users to ask questions and receive detailed, coherent, systematic, personalized, convincing, and informative human-like responses [ 55 ], even within complex and ambiguous contexts [ 63 , 77 ]. ChatGPT is considered the fastest-growing technology in history: in just three months following its public launch, it amassed an estimated 120 million monthly active users [ 16 ] with an estimated 13 million daily queries [ 49 ], surpassing all other applications [ 64 ]. This remarkable growth can be attributed to the unique features and user-friendly interface that ChatGPT offers. Its intuitive design allows users to interact seamlessly with the technology, making it accessible to a diverse range of individuals, regardless of their technical expertise [ 78 ]. Additionally, its exceptional performance results from a combination of advanced algorithms, continuous enhancements, and extensive training on a diverse dataset that includes various text sources such as books, articles, websites, and online forums [ 63 ], have contributed to a more engaging and satisfying user experience [ 62 ]. These factors collectively explain its remarkable global growth and set it apart from predecessors like Bard, Bing Chat, ERNIE, and others.

In this context, several studies have explored the technological advancements of chatbots. One noteworthy recent research effort, conducted by Schöbel et al. [ 70 ], stands out for its comprehensive analysis of more than 5,000 studies on communication agents. This study offered a comprehensive overview of the historical progression and future prospects of communication agents, including ChatGPT. Moreover, other studies have focused on making comparisons, particularly between ChatGPT and alternative chatbots like Bard, Bing Chat, ERNIE, LaMDA, BlenderBot, and various others. For example, O’Leary [ 53 ] compared two chatbots, LaMDA and BlenderBot, with ChatGPT and revealed that ChatGPT outperformed both. This superiority arises from ChatGPT’s capacity to handle a wider range of questions and generate slightly varied perspectives within specific contexts. Similarly, ChatGPT exhibited an impressive ability to formulate interpretable responses that were easily understood when compared with Google's feature snippet [ 34 ]. Additionally, ChatGPT was compared to other LLMs-based chatbots, including Bard and BERT, as well as ERNIE. The findings indicated that ChatGPT exhibited strong performance in the given tasks, often outperforming the other models [ 59 ].

Furthermore, in the education context, a comprehensive study systematically compared a range of the most promising chatbots, including Bard, Bing Chat, ChatGPT, and Ernie across a multidisciplinary test that required higher-order thinking. The study revealed that ChatGPT achieved the highest score, surpassing Bing Chat and Bard [ 64 ]. Similarly, a comparative analysis was conducted to compare ChatGPT with Bard in answering a set of 30 mathematical questions and logic problems, grouped into two question sets. Set (A) is unavailable online, while Set (B) is available online. The results revealed ChatGPT's superiority in Set (A) over Bard. Nevertheless, Bard's advantage emerged in Set (B) due to its capacity to access the internet directly and retrieve answers, a capability that ChatGPT does not possess [ 57 ]. However, through these varied assessments, ChatGPT consistently highlights its exceptional prowess compared to various alternatives in the ever-evolving chatbot technology.

The widespread adoption of chatbots, especially ChatGPT, by millions of students and educators, has sparked extensive discussions regarding its incorporation into the education sector [ 64 ]. Accordingly, many scholars have contributed to the discourse, expressing both optimism and pessimism regarding the incorporation of ChatGPT into education. For example, ChatGPT has been highlighted for its capabilities in enriching the learning and teaching experience through its ability to support different learning approaches, including adaptive learning, personalized learning, and self-directed learning [ 58 , 60 , 91 ]), deliver summative and formative feedback to students and provide real-time responses to questions, increase the accessibility of information [ 22 , 40 , 43 ], foster students’ performance, engagement and motivation [ 14 , 44 , 58 ], and enhance teaching practices [ 17 , 18 , 64 , 74 ].

On the other hand, concerns have been also raised regarding its potential negative effects on learning and teaching. These include the dissemination of false information and references [ 12 , 23 , 61 , 85 ], biased reinforcement [ 47 , 50 ], compromised academic integrity [ 18 , 40 , 66 , 74 ], and the potential decline in students' skills [ 43 , 61 , 64 , 74 ]. As a result, ChatGPT has been banned in multiple countries, including Russia, China, Venezuela, Belarus, and Iran, as well as in various educational institutions in India, Italy, Western Australia, France, and the United States [ 52 , 90 ].

Clearly, the advent of chatbots, especially ChatGPT, has provoked significant controversy due to their potential impact on learning and teaching. This indicates the necessity for further exploration to gain a deeper understanding of this technology and carefully evaluate its potential benefits, limitations, challenges, and threats to education [ 79 ]. Therefore, conducting a systematic literature review will provide valuable insights into the potential prospects and obstacles linked to its incorporation into education. This systematic literature review will primarily focus on ChatGPT, driven by the aforementioned key factors outlined above.

However, the existing literature lacks a systematic literature review of empirical studies. Thus, this systematic literature review aims to address this gap by synthesizing the existing empirical studies conducted on chatbots, particularly ChatGPT, in the field of education, highlighting how ChatGPT has been utilized in educational settings, and identifying any existing gaps. This review may be particularly useful for researchers in the field and educators who are contemplating the integration of ChatGPT or any chatbot into education. The following research questions will guide this study:

What are students' and teachers' initial attempts at utilizing ChatGPT in education?

What are the main findings derived from empirical studies that have incorporated ChatGPT into learning and teaching?

2 Methodology

To conduct this study, the authors followed the essential steps of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) and Okoli’s [ 54 ] steps for conducting a systematic review. These included identifying the study’s purpose, drafting a protocol, applying a practical screening process, searching the literature, extracting relevant data, evaluating the quality of the included studies, synthesizing the studies, and ultimately writing the review. The subsequent section provides an extensive explanation of how these steps were carried out in this study.

2.1 Identify the purpose

Given the widespread adoption of ChatGPT by students and teachers for various educational purposes, often without a thorough understanding of responsible and effective use or a clear recognition of its potential impact on learning and teaching, the authors recognized the need for further exploration of ChatGPT's impact on education in this early stage. Therefore, they have chosen to conduct a systematic literature review of existing empirical studies that incorporate ChatGPT into educational settings. Despite the limited number of empirical studies due to the novelty of the topic, their goal is to gain a deeper understanding of this technology and proactively evaluate its potential benefits, limitations, challenges, and threats to education. This effort could help to understand initial reactions and attempts at incorporating ChatGPT into education and bring out insights and considerations that can inform the future development of education.

2.2 Draft the protocol

The next step is formulating the protocol. This protocol serves to outline the study process in a rigorous and transparent manner, mitigating researcher bias in study selection and data extraction [ 88 ]. The protocol will include the following steps: generating the research question, predefining a literature search strategy, identifying search locations, establishing selection criteria, assessing the studies, developing a data extraction strategy, and creating a timeline.

2.3 Apply practical screen

The screening step aims to accurately filter the articles resulting from the searching step and select the empirical studies that have incorporated ChatGPT into educational contexts, which will guide us in answering the research questions and achieving the objectives of this study. To ensure the rigorous execution of this step, our inclusion and exclusion criteria were determined based on the authors' experience and informed by previous successful systematic reviews [ 21 ]. Table 1 summarizes the inclusion and exclusion criteria for study selection.

2.4 Literature search

We conducted a thorough literature search to identify articles that explored, examined, and addressed the use of ChatGPT in Educational contexts. We utilized two research databases: Dimensions.ai, which provides access to a large number of research publications, and lens.org, which offers access to over 300 million articles, patents, and other research outputs from diverse sources. Additionally, we included three databases, Scopus, Web of Knowledge, and ERIC, which contain relevant research on the topic that addresses our research questions. To browse and identify relevant articles, we used the following search formula: ("ChatGPT" AND "Education"), which included the Boolean operator "AND" to get more specific results. The subject area in the Scopus and ERIC databases were narrowed to "ChatGPT" and "Education" keywords, and in the WoS database was limited to the "Education" category. The search was conducted between the 3rd and 10th of April 2023, which resulted in 276 articles from all selected databases (111 articles from Dimensions.ai, 65 from Scopus, 28 from Web of Science, 14 from ERIC, and 58 from Lens.org). These articles were imported into the Rayyan web-based system for analysis. The duplicates were identified automatically by the system. Subsequently, the first author manually reviewed the duplicated articles ensured that they had the same content, and then removed them, leaving us with 135 unique articles. Afterward, the titles, abstracts, and keywords of the first 40 manuscripts were scanned and reviewed by the first author and were discussed with the second and third authors to resolve any disagreements. Subsequently, the first author proceeded with the filtering process for all articles and carefully applied the inclusion and exclusion criteria as presented in Table  1 . Articles that met any one of the exclusion criteria were eliminated, resulting in 26 articles. Afterward, the authors met to carefully scan and discuss them. The authors agreed to eliminate any empirical studies solely focused on checking ChatGPT capabilities, as these studies do not guide us in addressing the research questions and achieving the study's objectives. This resulted in 14 articles eligible for analysis.

2.5 Quality appraisal

The examination and evaluation of the quality of the extracted articles is a vital step [ 9 ]. Therefore, the extracted articles were carefully evaluated for quality using Fink’s [ 24 ] standards, which emphasize the necessity for detailed descriptions of methodology, results, conclusions, strengths, and limitations. The process began with a thorough assessment of each study's design, data collection, and analysis methods to ensure their appropriateness and comprehensive execution. The clarity, consistency, and logical progression from data to results and conclusions were also critically examined. Potential biases and recognized limitations within the studies were also scrutinized. Ultimately, two articles were excluded for failing to meet Fink’s criteria, particularly in providing sufficient detail on methodology, results, conclusions, strengths, or limitations. The review process is illustrated in Fig.  1 .

The study selection process

2.6 Data extraction

The next step is data extraction, the process of capturing the key information and categories from the included studies. To improve efficiency, reduce variation among authors, and minimize errors in data analysis, the coding categories were constructed using Creswell's [ 15 ] coding techniques for data extraction and interpretation. The coding process involves three sequential steps. The initial stage encompasses open coding , where the researcher examines the data, generates codes to describe and categorize it, and gains a deeper understanding without preconceived ideas. Following open coding is axial coding , where the interrelationships between codes from open coding are analyzed to establish more comprehensive categories or themes. The process concludes with selective coding , refining and integrating categories or themes to identify core concepts emerging from the data. The first coder performed the coding process, then engaged in discussions with the second and third authors to finalize the coding categories for the first five articles. The first coder then proceeded to code all studies and engaged again in discussions with the other authors to ensure the finalization of the coding process. After a comprehensive analysis and capturing of the key information from the included studies, the data extraction and interpretation process yielded several themes. These themes have been categorized and are presented in Table  2 . It is important to note that open coding results were removed from Table  2 for aesthetic reasons, as it included many generic aspects, such as words, short phrases, or sentences mentioned in the studies.

2.7 Synthesize studies

In this stage, we will gather, discuss, and analyze the key findings that emerged from the selected studies. The synthesis stage is considered a transition from an author-centric to a concept-centric focus, enabling us to map all the provided information to achieve the most effective evaluation of the data [ 87 ]. Initially, the authors extracted data that included general information about the selected studies, including the author(s)' names, study titles, years of publication, educational levels, research methodologies, sample sizes, participants, main aims or objectives, raw data sources, and analysis methods. Following that, all key information and significant results from the selected studies were compiled using Creswell’s [ 15 ] coding techniques for data extraction and interpretation to identify core concepts and themes emerging from the data, focusing on those that directly contributed to our research questions and objectives, such as the initial utilization of ChatGPT in learning and teaching, learners' and educators' familiarity with ChatGPT, and the main findings of each study. Finally, the data related to each selected study were extracted into an Excel spreadsheet for data processing. The Excel spreadsheet was reviewed by the authors, including a series of discussions to ensure the finalization of this process and prepare it for further analysis. Afterward, the final result being analyzed and presented in various types of charts and graphs. Table 4 presents the extracted data from the selected studies, with each study labeled with a capital 'S' followed by a number.

This section consists of two main parts. The first part provides a descriptive analysis of the data compiled from the reviewed studies. The second part presents the answers to the research questions and the main findings of these studies.

3.1 Part 1: descriptive analysis

This section will provide a descriptive analysis of the reviewed studies, including educational levels and fields, participants distribution, country contribution, research methodologies, study sample size, study population, publication year, list of journals, familiarity with ChatGPT, source of data, and the main aims and objectives of the studies. Table 4 presents a comprehensive overview of the extracted data from the selected studies.

3.1.1 The number of the reviewed studies and publication years

The total number of the reviewed studies was 14. All studies were empirical studies and published in different journals focusing on Education and Technology. One study was published in 2022 [S1], while the remaining were published in 2023 [S2]-[S14]. Table 3 illustrates the year of publication, the names of the journals, and the number of reviewed studies published in each journal for the studies reviewed.

3.1.2 Educational levels and fields

The majority of the reviewed studies, 11 studies, were conducted in higher education institutions [S1]-[S10] and [S13]. Two studies did not specify the educational level of the population [S12] and [S14], while one study focused on elementary education [S11]. However, the reviewed studies covered various fields of education. Three studies focused on Arts and Humanities Education [S8], [S11], and [S14], specifically English Education. Two studies focused on Engineering Education, with one in Computer Engineering [S2] and the other in Construction Education [S3]. Two studies focused on Mathematics Education [S5] and [S12]. One study focused on Social Science Education [S13]. One study focused on Early Education [S4]. One study focused on Journalism Education [S9]. Finally, three studies did not specify the field of education [S1], [S6], and [S7]. Figure  2 represents the educational levels in the reviewed studies, while Fig.  3 represents the context of the reviewed studies.

Educational levels in the reviewed studies

Context of the reviewed studies

3.1.3 Participants distribution and countries contribution

The reviewed studies have been conducted across different geographic regions, providing a diverse representation of the studies. The majority of the studies, 10 in total, [S1]-[S3], [S5]-[S9], [S11], and [S14], primarily focused on participants from single countries such as Pakistan, the United Arab Emirates, China, Indonesia, Poland, Saudi Arabia, South Korea, Spain, Tajikistan, and the United States. In contrast, four studies, [S4], [S10], [S12], and [S13], involved participants from multiple countries, including China and the United States [S4], China, the United Kingdom, and the United States [S10], the United Arab Emirates, Oman, Saudi Arabia, and Jordan [S12], Turkey, Sweden, Canada, and Australia [ 13 ]. Figures  4 and 5 illustrate the distribution of participants, whether from single or multiple countries, and the contribution of each country in the reviewed studies, respectively.

The reviewed studies conducted in single or multiple countries

The Contribution of each country in the studies

3.1.4 Study population and sample size

Four study populations were included: university students, university teachers, university teachers and students, and elementary school teachers. Six studies involved university students [S2], [S3], [S5] and [S6]-[S8]. Three studies focused on university teachers [S1], [S4], and [S6], while one study specifically targeted elementary school teachers [S11]. Additionally, four studies included both university teachers and students [S10] and [ 12 , 13 , 14 ], and among them, study [S13] specifically included postgraduate students. In terms of the sample size of the reviewed studies, nine studies included a small sample size of less than 50 participants [S1], [S3], [S6], [S8], and [S10]-[S13]. Three studies had 50–100 participants [S2], [S9], and [S14]. Only one study had more than 100 participants [S7]. It is worth mentioning that study [S4] adopted a mixed methods approach, including 10 participants for qualitative analysis and 110 participants for quantitative analysis.

3.1.5 Participants’ familiarity with using ChatGPT

The reviewed studies recruited a diverse range of participants with varying levels of familiarity with ChatGPT. Five studies [S2], [S4], [S6], [S8], and [S12] involved participants already familiar with ChatGPT, while eight studies [S1], [S3], [S5], [S7], [S9], [S10], [S13] and [S14] included individuals with differing levels of familiarity. Notably, one study [S11] had participants who were entirely unfamiliar with ChatGPT. It is important to note that four studies [S3], [S5], [S9], and [S11] provided training or guidance to their participants before conducting their studies, while ten studies [S1], [S2], [S4], [S6]-[S8], [S10], and [S12]-[S14] did not provide training due to the participants' existing familiarity with ChatGPT.

3.1.6 Research methodology approaches and source(S) of data

The reviewed studies adopted various research methodology approaches. Seven studies adopted qualitative research methodology [S1], [S4], [S6], [S8], [S10], [S11], and [S12], while three studies adopted quantitative research methodology [S3], [S7], and [S14], and four studies employed mixed-methods, which involved a combination of both the strengths of qualitative and quantitative methods [S2], [S5], [S9], and [S13].

In terms of the source(s) of data, the reviewed studies obtained their data from various sources, such as interviews, questionnaires, and pre-and post-tests. Six studies relied on interviews as their primary source of data collection [S1], [S4], [S6], [S10], [S11], and [S12], four studies relied on questionnaires [S2], [S7], [S13], and [S14], two studies combined the use of pre-and post-tests and questionnaires for data collection [S3] and [S9], while two studies combined the use of questionnaires and interviews to obtain the data [S5] and [S8]. It is important to note that six of the reviewed studies were quasi-experimental [S3], [S5], [S8], [S9], [S12], and [S14], while the remaining ones were experimental studies [S1], [S2], [S4], [S6], [S7], [S10], [S11], and [S13]. Figures  6 and 7 illustrate the research methodologies and the source (s) of data used in the reviewed studies, respectively.

Research methodologies in the reviewed studies

Source of data in the reviewed studies

3.1.7 The aim and objectives of the studies

The reviewed studies encompassed a diverse set of aims, with several of them incorporating multiple primary objectives. Six studies [S3], [S6], [S7], [S8], [S11], and [S12] examined the integration of ChatGPT in educational contexts, and four studies [S4], [S5], [S13], and [S14] investigated the various implications of its use in education, while three studies [S2], [S9], and [S10] aimed to explore both its integration and implications in education. Additionally, seven studies explicitly explored attitudes and perceptions of students [S2] and [S3], educators [S1] and [S6], or both [S10], [S12], and [S13] regarding the utilization of ChatGPT in educational settings.

3.2 Part 2: research questions and main findings of the reviewed studies

This part will present the answers to the research questions and the main findings of the reviewed studies, classified into two main categories (learning and teaching) according to AI Education classification by [ 36 ]. Figure  8 summarizes the main findings of the reviewed studies in a visually informative diagram. Table 4 provides a detailed list of the key information extracted from the selected studies that led to generating these themes.

The main findings in the reviewed studies

4 Students' initial attempts at utilizing ChatGPT in learning and main findings from students' perspective

4.1 virtual intelligent assistant.

Nine studies demonstrated that ChatGPT has been utilized by students as an intelligent assistant to enhance and support their learning. Students employed it for various purposes, such as answering on-demand questions [S2]-[S5], [S8], [S10], and [S12], providing valuable information and learning resources [S2]-[S5], [S6], and [S8], as well as receiving immediate feedback [S2], [S4], [S9], [S10], and [S12]. In this regard, students generally were confident in the accuracy of ChatGPT's responses, considering them relevant, reliable, and detailed [S3], [S4], [S5], and [S8]. However, some students indicated the need for improvement, as they found that answers are not always accurate [S2], and that misleading information may have been provided or that it may not always align with their expectations [S6] and [S10]. It was also observed by the students that the accuracy of ChatGPT is dependent on several factors, including the quality and specificity of the user's input, the complexity of the question or topic, and the scope and relevance of its training data [S12]. Many students felt that ChatGPT's answers were not always accurate and most of them believed that it requires good background knowledge to work with.

4.2 Writing and language proficiency assistant

Six of the reviewed studies highlighted that ChatGPT has been utilized by students as a valuable assistant tool to improve their academic writing skills and language proficiency. Among these studies, three mainly focused on English education, demonstrating that students showed sufficient mastery in using ChatGPT for generating ideas, summarizing, paraphrasing texts, and completing writing essays [S8], [S11], and [S14]. Furthermore, ChatGPT helped them in writing by making students active investigators rather than passive knowledge recipients and facilitated the development of their writing skills [S11] and [S14]. Similarly, ChatGPT allowed students to generate unique ideas and perspectives, leading to deeper analysis and reflection on their journalism writing [S9]. In terms of language proficiency, ChatGPT allowed participants to translate content into their home languages, making it more accessible and relevant to their context [S4]. It also enabled them to request changes in linguistic tones or flavors [S8]. Moreover, participants used it to check grammar or as a dictionary [S11].

4.3 Valuable resource for learning approaches

Five studies demonstrated that students used ChatGPT as a valuable complementary resource for self-directed learning. It provided learning resources and guidance on diverse educational topics and created a supportive home learning environment [S2] and [S4]. Moreover, it offered step-by-step guidance to grasp concepts at their own pace and enhance their understanding [S5], streamlined task and project completion carried out independently [S7], provided comprehensive and easy-to-understand explanations on various subjects [S10], and assisted in studying geometry operations, thereby empowering them to explore geometry operations at their own pace [S12]. Three studies showed that students used ChatGPT as a valuable learning resource for personalized learning. It delivered age-appropriate conversations and tailored teaching based on a child's interests [S4], acted as a personalized learning assistant, adapted to their needs and pace, which assisted them in understanding mathematical concepts [S12], and enabled personalized learning experiences in social sciences by adapting to students' needs and learning styles [S13]. On the other hand, it is important to note that, according to one study [S5], students suggested that using ChatGPT may negatively affect collaborative learning competencies between students.

4.4 Enhancing students' competencies

Six of the reviewed studies have shown that ChatGPT is a valuable tool for improving a wide range of skills among students. Two studies have provided evidence that ChatGPT led to improvements in students' critical thinking, reasoning skills, and hazard recognition competencies through engaging them in interactive conversations or activities and providing responses related to their disciplines in journalism [S5] and construction education [S9]. Furthermore, two studies focused on mathematical education have shown the positive impact of ChatGPT on students' problem-solving abilities in unraveling problem-solving questions [S12] and enhancing the students' understanding of the problem-solving process [S5]. Lastly, one study indicated that ChatGPT effectively contributed to the enhancement of conversational social skills [S4].

4.5 Supporting students' academic success

Seven of the reviewed studies highlighted that students found ChatGPT to be beneficial for learning as it enhanced learning efficiency and improved the learning experience. It has been observed to improve students' efficiency in computer engineering studies by providing well-structured responses and good explanations [S2]. Additionally, students found it extremely useful for hazard reporting [S3], and it also enhanced their efficiency in solving mathematics problems and capabilities [S5] and [S12]. Furthermore, by finding information, generating ideas, translating texts, and providing alternative questions, ChatGPT aided students in deepening their understanding of various subjects [S6]. It contributed to an increase in students' overall productivity [S7] and improved efficiency in composing written tasks [S8]. Regarding learning experiences, ChatGPT was instrumental in assisting students in identifying hazards that they might have otherwise overlooked [S3]. It also improved students' learning experiences in solving mathematics problems and developing abilities [S5] and [S12]. Moreover, it increased students' successful completion of important tasks in their studies [S7], particularly those involving average difficulty writing tasks [S8]. Additionally, ChatGPT increased the chances of educational success by providing students with baseline knowledge on various topics [S10].

5 Teachers' initial attempts at utilizing ChatGPT in teaching and main findings from teachers' perspective

5.1 valuable resource for teaching.

The reviewed studies showed that teachers have employed ChatGPT to recommend, modify, and generate diverse, creative, organized, and engaging educational contents, teaching materials, and testing resources more rapidly [S4], [S6], [S10] and [S11]. Additionally, teachers experienced increased productivity as ChatGPT facilitated quick and accurate responses to questions, fact-checking, and information searches [S1]. It also proved valuable in constructing new knowledge [S6] and providing timely answers to students' questions in classrooms [S11]. Moreover, ChatGPT enhanced teachers' efficiency by generating new ideas for activities and preplanning activities for their students [S4] and [S6], including interactive language game partners [S11].

5.2 Improving productivity and efficiency

The reviewed studies showed that participants' productivity and work efficiency have been significantly enhanced by using ChatGPT as it enabled them to allocate more time to other tasks and reduce their overall workloads [S6], [S10], [S11], [S13], and [S14]. However, three studies [S1], [S4], and [S11], indicated a negative perception and attitude among teachers toward using ChatGPT. This negativity stemmed from a lack of necessary skills to use it effectively [S1], a limited familiarity with it [S4], and occasional inaccuracies in the content provided by it [S10].

5.3 Catalyzing new teaching methodologies

Five of the reviewed studies highlighted that educators found the necessity of redefining their teaching profession with the assistance of ChatGPT [S11], developing new effective learning strategies [S4], and adapting teaching strategies and methodologies to ensure the development of essential skills for future engineers [S5]. They also emphasized the importance of adopting new educational philosophies and approaches that can evolve with the introduction of ChatGPT into the classroom [S12]. Furthermore, updating curricula to focus on improving human-specific features, such as emotional intelligence, creativity, and philosophical perspectives [S13], was found to be essential.

5.4 Effective utilization of CHATGPT in teaching

According to the reviewed studies, effective utilization of ChatGPT in education requires providing teachers with well-structured training, support, and adequate background on how to use ChatGPT responsibly [S1], [S3], [S11], and [S12]. Establishing clear rules and regulations regarding its usage is essential to ensure it positively impacts the teaching and learning processes, including students' skills [S1], [S4], [S5], [S8], [S9], and [S11]-[S14]. Moreover, conducting further research and engaging in discussions with policymakers and stakeholders is indeed crucial for the successful integration of ChatGPT in education and to maximize the benefits for both educators and students [S1], [S6]-[S10], and [S12]-[S14].

6 Discussion

The purpose of this review is to conduct a systematic review of empirical studies that have explored the utilization of ChatGPT, one of today’s most advanced LLM-based chatbots, in education. The findings of the reviewed studies showed several ways of ChatGPT utilization in different learning and teaching practices as well as it provided insights and considerations that can facilitate its effective and responsible use in future educational contexts. The results of the reviewed studies came from diverse fields of education, which helped us avoid a biased review that is limited to a specific field. Similarly, the reviewed studies have been conducted across different geographic regions. This kind of variety in geographic representation enriched the findings of this review.

In response to RQ1 , "What are students' and teachers' initial attempts at utilizing ChatGPT in education?", the findings from this review provide comprehensive insights. Chatbots, including ChatGPT, play a crucial role in supporting student learning, enhancing their learning experiences, and facilitating diverse learning approaches [ 42 , 43 ]. This review found that this tool, ChatGPT, has been instrumental in enhancing students' learning experiences by serving as a virtual intelligent assistant, providing immediate feedback, on-demand answers, and engaging in educational conversations. Additionally, students have benefited from ChatGPT’s ability to generate ideas, compose essays, and perform tasks like summarizing, translating, paraphrasing texts, or checking grammar, thereby enhancing their writing and language competencies. Furthermore, students have turned to ChatGPT for assistance in understanding concepts and homework, providing structured learning plans, and clarifying assignments and tasks, which fosters a supportive home learning environment, allowing them to take responsibility for their own learning and cultivate the skills and approaches essential for supportive home learning environment [ 26 , 27 , 28 ]. This finding aligns with the study of Saqr et al. [ 68 , 69 ] who highlighted that, when students actively engage in their own learning process, it yields additional advantages, such as heightened motivation, enhanced achievement, and the cultivation of enthusiasm, turning them into advocates for their own learning.

Moreover, students have utilized ChatGPT for tailored teaching and step-by-step guidance on diverse educational topics, streamlining task and project completion, and generating and recommending educational content. This personalization enhances the learning environment, leading to increased academic success. This finding aligns with other recent studies [ 26 , 27 , 28 , 60 , 66 ] which revealed that ChatGPT has the potential to offer personalized learning experiences and support an effective learning process by providing students with customized feedback and explanations tailored to their needs and abilities. Ultimately, fostering students' performance, engagement, and motivation, leading to increase students' academic success [ 14 , 44 , 58 ]. This ultimate outcome is in line with the findings of Saqr et al. [ 68 , 69 ], which emphasized that learning strategies are important catalysts of students' learning, as students who utilize effective learning strategies are more likely to have better academic achievement.

Teachers, too, have capitalized on ChatGPT's capabilities to enhance productivity and efficiency, using it for creating lesson plans, generating quizzes, providing additional resources, generating and preplanning new ideas for activities, and aiding in answering students’ questions. This adoption of technology introduces new opportunities to support teaching and learning practices, enhancing teacher productivity. This finding aligns with those of Day [ 17 ], De Castro [ 18 ], and Su and Yang [ 74 ] as well as with those of Valtonen et al. [ 82 ], who revealed that emerging technological advancements have opened up novel opportunities and means to support teaching and learning practices, and enhance teachers’ productivity.

In response to RQ2 , "What are the main findings derived from empirical studies that have incorporated ChatGPT into learning and teaching?", the findings from this review provide profound insights and raise significant concerns. Starting with the insights, chatbots, including ChatGPT, have demonstrated the potential to reshape and revolutionize education, creating new, novel opportunities for enhancing the learning process and outcomes [ 83 ], facilitating different learning approaches, and offering a range of pedagogical benefits [ 19 , 43 , 72 ]. In this context, this review found that ChatGPT could open avenues for educators to adopt or develop new effective learning and teaching strategies that can evolve with the introduction of ChatGPT into the classroom. Nonetheless, there is an evident lack of research understanding regarding the potential impact of generative machine learning models within diverse educational settings [ 83 ]. This necessitates teachers to attain a high level of proficiency in incorporating chatbots, such as ChatGPT, into their classrooms to create inventive, well-structured, and captivating learning strategies. In the same vein, the review also found that teachers without the requisite skills to utilize ChatGPT realized that it did not contribute positively to their work and could potentially have adverse effects [ 37 ]. This concern could lead to inequity of access to the benefits of chatbots, including ChatGPT, as individuals who lack the necessary expertise may not be able to harness their full potential, resulting in disparities in educational outcomes and opportunities. Therefore, immediate action is needed to address these potential issues. A potential solution is offering training, support, and competency development for teachers to ensure that all of them can leverage chatbots, including ChatGPT, effectively and equitably in their educational practices [ 5 , 28 , 80 ], which could enhance accessibility and inclusivity, and potentially result in innovative outcomes [ 82 , 83 ].

Additionally, chatbots, including ChatGPT, have the potential to significantly impact students' thinking abilities, including retention, reasoning, analysis skills [ 19 , 45 ], and foster innovation and creativity capabilities [ 83 ]. This review found that ChatGPT could contribute to improving a wide range of skills among students. However, it found that frequent use of ChatGPT may result in a decrease in innovative capacities, collaborative skills and cognitive capacities, and students' motivation to attend classes, as well as could lead to reduced higher-order thinking skills among students [ 22 , 29 ]. Therefore, immediate action is needed to carefully examine the long-term impact of chatbots such as ChatGPT, on learning outcomes as well as to explore its incorporation into educational settings as a supportive tool without compromising students' cognitive development and critical thinking abilities. In the same vein, the review also found that it is challenging to draw a consistent conclusion regarding the potential of ChatGPT to aid self-directed learning approach. This finding aligns with the recent study of Baskara [ 8 ]. Therefore, further research is needed to explore the potential of ChatGPT for self-directed learning. One potential solution involves utilizing learning analytics as a novel approach to examine various aspects of students' learning and support them in their individual endeavors [ 32 ]. This approach can bridge this gap by facilitating an in-depth analysis of how learners engage with ChatGPT, identifying trends in self-directed learning behavior, and assessing its influence on their outcomes.

Turning to the significant concerns, on the other hand, a fundamental challenge with LLM-based chatbots, including ChatGPT, is the accuracy and quality of the provided information and responses, as they provide false information as truth—a phenomenon often referred to as "hallucination" [ 3 , 49 ]. In this context, this review found that the provided information was not entirely satisfactory. Consequently, the utilization of chatbots presents potential concerns, such as generating and providing inaccurate or misleading information, especially for students who utilize it to support their learning. This finding aligns with other findings [ 6 , 30 , 35 , 40 ] which revealed that incorporating chatbots such as ChatGPT, into education presents challenges related to its accuracy and reliability due to its training on a large corpus of data, which may contain inaccuracies and the way users formulate or ask ChatGPT. Therefore, immediate action is needed to address these potential issues. One possible solution is to equip students with the necessary skills and competencies, which include a background understanding of how to use it effectively and the ability to assess and evaluate the information it generates, as the accuracy and the quality of the provided information depend on the input, its complexity, the topic, and the relevance of its training data [ 28 , 49 , 86 ]. However, it's also essential to examine how learners can be educated about how these models operate, the data used in their training, and how to recognize their limitations, challenges, and issues [ 79 ].

Furthermore, chatbots present a substantial challenge concerning maintaining academic integrity [ 20 , 56 ] and copyright violations [ 83 ], which are significant concerns in education. The review found that the potential misuse of ChatGPT might foster cheating, facilitate plagiarism, and threaten academic integrity. This issue is also affirmed by the research conducted by Basic et al. [ 7 ], who presented evidence that students who utilized ChatGPT in their writing assignments had more plagiarism cases than those who did not. These findings align with the conclusions drawn by Cotton et al. [ 13 ], Hisan and Amri [ 33 ] and Sullivan et al. [ 75 ], who revealed that the integration of chatbots such as ChatGPT into education poses a significant challenge to the preservation of academic integrity. Moreover, chatbots, including ChatGPT, have increased the difficulty in identifying plagiarism [ 47 , 67 , 76 ]. The findings from previous studies [ 1 , 84 ] indicate that AI-generated text often went undetected by plagiarism software, such as Turnitin. However, Turnitin and other similar plagiarism detection tools, such as ZeroGPT, GPTZero, and Copyleaks, have since evolved, incorporating enhanced techniques to detect AI-generated text, despite the possibility of false positives, as noted in different studies that have found these tools still not yet fully ready to accurately and reliably identify AI-generated text [ 10 , 51 ], and new novel detection methods may need to be created and implemented for AI-generated text detection [ 4 ]. This potential issue could lead to another concern, which is the difficulty of accurately evaluating student performance when they utilize chatbots such as ChatGPT assistance in their assignments. Consequently, the most LLM-driven chatbots present a substantial challenge to traditional assessments [ 64 ]. The findings from previous studies indicate the importance of rethinking, improving, and redesigning innovative assessment methods in the era of chatbots [ 14 , 20 , 64 , 75 ]. These methods should prioritize the process of evaluating students' ability to apply knowledge to complex cases and demonstrate comprehension, rather than solely focusing on the final product for assessment. Therefore, immediate action is needed to address these potential issues. One possible solution would be the development of clear guidelines, regulatory policies, and pedagogical guidance. These measures would help regulate the proper and ethical utilization of chatbots, such as ChatGPT, and must be established before their introduction to students [ 35 , 38 , 39 , 41 , 89 ].

In summary, our review has delved into the utilization of ChatGPT, a prominent example of chatbots, in education, addressing the question of how ChatGPT has been utilized in education. However, there remain significant gaps, which necessitate further research to shed light on this area.

7 Conclusions

This systematic review has shed light on the varied initial attempts at incorporating ChatGPT into education by both learners and educators, while also offering insights and considerations that can facilitate its effective and responsible use in future educational contexts. From the analysis of 14 selected studies, the review revealed the dual-edged impact of ChatGPT in educational settings. On the positive side, ChatGPT significantly aided the learning process in various ways. Learners have used it as a virtual intelligent assistant, benefiting from its ability to provide immediate feedback, on-demand answers, and easy access to educational resources. Additionally, it was clear that learners have used it to enhance their writing and language skills, engaging in practices such as generating ideas, composing essays, and performing tasks like summarizing, translating, paraphrasing texts, or checking grammar. Importantly, other learners have utilized it in supporting and facilitating their directed and personalized learning on a broad range of educational topics, assisting in understanding concepts and homework, providing structured learning plans, and clarifying assignments and tasks. Educators, on the other hand, found ChatGPT beneficial for enhancing productivity and efficiency. They used it for creating lesson plans, generating quizzes, providing additional resources, and answers learners' questions, which saved time and allowed for more dynamic and engaging teaching strategies and methodologies.

However, the review also pointed out negative impacts. The results revealed that overuse of ChatGPT could decrease innovative capacities and collaborative learning among learners. Specifically, relying too much on ChatGPT for quick answers can inhibit learners' critical thinking and problem-solving skills. Learners might not engage deeply with the material or consider multiple solutions to a problem. This tendency was particularly evident in group projects, where learners preferred consulting ChatGPT individually for solutions over brainstorming and collaborating with peers, which negatively affected their teamwork abilities. On a broader level, integrating ChatGPT into education has also raised several concerns, including the potential for providing inaccurate or misleading information, issues of inequity in access, challenges related to academic integrity, and the possibility of misusing the technology.

Accordingly, this review emphasizes the urgency of developing clear rules, policies, and regulations to ensure ChatGPT's effective and responsible use in educational settings, alongside other chatbots, by both learners and educators. This requires providing well-structured training to educate them on responsible usage and understanding its limitations, along with offering sufficient background information. Moreover, it highlights the importance of rethinking, improving, and redesigning innovative teaching and assessment methods in the era of ChatGPT. Furthermore, conducting further research and engaging in discussions with policymakers and stakeholders are essential steps to maximize the benefits for both educators and learners and ensure academic integrity.

It is important to acknowledge that this review has certain limitations. Firstly, the limited inclusion of reviewed studies can be attributed to several reasons, including the novelty of the technology, as new technologies often face initial skepticism and cautious adoption; the lack of clear guidelines or best practices for leveraging this technology for educational purposes; and institutional or governmental policies affecting the utilization of this technology in educational contexts. These factors, in turn, have affected the number of studies available for review. Secondly, the utilization of the original version of ChatGPT, based on GPT-3 or GPT-3.5, implies that new studies utilizing the updated version, GPT-4 may lead to different findings. Therefore, conducting follow-up systematic reviews is essential once more empirical studies on ChatGPT are published. Additionally, long-term studies are necessary to thoroughly examine and assess the impact of ChatGPT on various educational practices.

Despite these limitations, this systematic review has highlighted the transformative potential of ChatGPT in education, revealing its diverse utilization by learners and educators alike and summarized the benefits of incorporating it into education, as well as the forefront critical concerns and challenges that must be addressed to facilitate its effective and responsible use in future educational contexts. This review could serve as an insightful resource for practitioners who seek to integrate ChatGPT into education and stimulate further research in the field.

Data availability

The data supporting our findings are available upon request.

Abbreviations

• Artificial intelligence

AI in education

Large language model

Artificial neural networks

Chat Generative Pre-Trained Transformer

Recurrent neural networks

Long short-term memory

Reinforcement learning from human feedback

Natural language processing

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

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The paper is co-funded by the Academy of Finland (Suomen Akatemia) Research Council for Natural Sciences and Engineering for the project Towards precision education: Idiographic learning analytics (TOPEILA), Decision Number 350560.

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YA contributed to the literature search, data analysis, discussion, and conclusion. Additionally, YA contributed to the manuscript’s writing, editing, and finalization. MS contributed to the study’s design, conceptualization, acquisition of funding, project administration, allocation of resources, supervision, validation, literature search, and analysis of results. Furthermore, MS contributed to the manuscript's writing, revising, and approving it in its finalized state. NP contributed to the results, and discussions, and provided supervision. NP also contributed to the writing process, revisions, and the final approval of the manuscript in its finalized state. MT contributed to the study's conceptualization, resource management, supervision, writing, revising the manuscript, and approving it.

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See Table  4

The process of synthesizing the data presented in Table  4 involved identifying the relevant studies through a search process of databases (ERIC, Scopus, Web of Knowledge, Dimensions.ai, and lens.org) using specific keywords "ChatGPT" and "education". Following this, inclusion/exclusion criteria were applied, and data extraction was performed using Creswell's [ 15 ] coding techniques to capture key information and identify common themes across the included studies.

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Albadarin, Y., Saqr, M., Pope, N. et al. A systematic literature review of empirical research on ChatGPT in education. Discov Educ 3 , 60 (2024). https://doi.org/10.1007/s44217-024-00138-2

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Rapid literature review: definition and methodology

Beata smela.

a Assignity, Cracow, Poland

Mondher Toumi

b Public Health Department, Aix-Marseille University, Marseille, France

Karolina Świerk

Clement francois, małgorzata biernikiewicz.

c Studio Slowa, Wroclaw, Poland

Emilie Clay

d Clever-Access, Paris, France

Laurent Boyer

Introduction: A rapid literature review (RLR) is an alternative to systematic literature review (SLR) that can speed up the analysis of newly published data. The objective was to identify and summarize available information regarding different approaches to defining RLR and the methodology applied to the conduct of such reviews.

Methods: The Medline and EMBASE databases, as well as the grey literature, were searched using the set of keywords and their combination related to the targeted and rapid review, as well as design, approach, and methodology. Of the 3,898 records retrieved, 12 articles were included.

Results: Specific definition of RLRs has only been developed in 2021. In terms of methodology, the RLR should be completed within shorter timeframes using simplified procedures in comparison to SLRs, while maintaining a similar level of transparency and minimizing bias. Inherent components of the RLR process should be a clear research question, search protocol, simplified process of study selection, data extraction, and quality assurance.

Conclusions: There is a lack of consensus on the formal definition of the RLR and the best approaches to perform it. The evidence-based supporting methods are evolving, and more work is needed to define the most robust approaches.

Introduction

A systematic literature review (SLR) summarizes the results of all available studies on a specific topic and provides a high level of evidence. Authors of the SLR have to follow an advanced plan that covers defining a priori information regarding the research question, sources they are going to search, inclusion criteria applied to choose studies answering the research question, and information regarding how they are going to summarize findings [ 1 ].

The rigor and transparency of SLRs make them the most reliable form of literature review [ 2 ], providing a comprehensive, objective summary of the evidence for a given topic [ 3 , 4 ]. On the other hand, the SLR process is usually very time-consuming and requires a lot of human resources. Taking into account a high increase of newly published data and a growing need to analyze information in the fastest possible way, rapid literature reviews (RLRs) often replace standard SLRs.

There are several guidelines on the methodology of RLRs [ 5–11 ]; however, only recently, one publication from 2021 attempted to construct a unified definition [ 11 ]. Generally, by RLRs, researchers understand evidence synthesis during which some of the components of the systematic approach are being used to facilitate answering a focused research question; however, scope restrictions and a narrower search strategy help to make the project manageable in a shorter time and to get the key conclusions faster [ 4 ].

The objective of this research was to collect and summarize available information on different approaches to the definition and methodology of RLRs. An RLR has been run to capture publications providing data that fit the project objective.

To find publications reporting information on the methodology of RLRs, searches were run in the Medline and EMBASE databases in November 2022. The following keywords were searched for in titles and abstracts: ‘targeted adj2 review’ OR ‘focused adj2 review’ OR ‘rapid adj2 review’, and ‘methodology’ OR ‘design’ OR ‘scheme’ OR ‘approach’. The grey literature was identified using Google Scholar with keywords including ‘targeted review methodology’ OR ‘focused review methodology’ OR ‘rapid review methodology’. Only publications in English were included, and the date of publication was restricted to year 2016 onward in order to identify the most up-to-date literature. The reference lists of each included article were searched manually to obtain the potentially eligible articles. Titles and abstracts of the retrieved records were first screened to exclude articles that were evidently irrelevant. The full texts of potentially relevant papers were further reviewed to examine their eligibility.

A pre-defined Excel grid was developed to extract the following information related to the methodology of RLR from guidelines:

• Definition,
• Research question and searches,
• Studies selection,
• Data extraction and quality assessment,
• Additional information.

There was no restriction on the study types to be analyzed; any study reporting on the methodology of RLRs could be included: reviews, practice guidelines, commentaries, and expert opinions on RLR relevant to healthcare policymakers or practitioners. The data extraction and evidence summary were conducted by one analyst and further examined by a senior analyst to ensure that relevant information was not omitted. Disagreements were resolved by discussion and consensus.

Studies selection

A total of 3,898 records (3,864 articles from a database search and 34 grey literature from Google Scholar) were retrieved. After removing duplicates, titles and abstracts of 3,813 articles were uploaded and screened. The full texts of 43 articles were analyzed resulting in 12 articles selected for this review, including 7 guidelines [ 5–11 ] on the methodology of RLRs, together with 2 papers summarizing the results of the Delphi consensus on the topic [ 12 , 13 ], and 3 publications analyzing and assessing different approaches to RLRs [ 4 , 14 , 15 ].

Overall, seven guidelines were identified: from the World Health Organization (WHO) [ 5 ], National Collaborating Centre for Methods and Tools (NCCMT) [ 7 ], the UK government [ 8 ], the Oxford Centre for Evidence Based Medicine [ 9 ], the Cochrane group [ 6 , 11 ], and one multi-national review [ 10 ]. Among the papers that did not describe the guidelines, Gordon et al. [ 4 ] proposed 12 tips for conducting a rapid review in the right settings and discussed why these reviews may be more beneficial in some circumstances. The objective of work conducted by Tricco et al. [ 13 ] and Pandor et al. [ 12 ] was to collect and compare perceptions of rapid reviews from stakeholders, including researchers, policymakers, industry, journal editors, and healthcare providers, and to reach a consensus outlining the domains to consider when deciding on approaches for RLRs. Haby et al. [ 14 ] run a rapid review of systematic reviews and primary studies to find out the best way to conduct an RLR in health policy and practice. In Tricco et al. (2022) [ 15 ], JBI position statement for RLRs is presented.

From all the seven identified guidelines information regarding definitions the authors used for RLRs, approach to the PICOS criteria and search strategy development, studies selection, data extractions, quality assessment, and reporting were extracted.

Cochrane Rapid Reviews Methods Group developed methods guidance based on scoping review of the underlying evidence, primary methods studies conducted, as well as surveys sent to Cochrane representative and discussion among those with expertise [ 11 ]. They analyzed over 300 RLRs or RLR method papers and based on the methodology of those studies, constructed a broad definition RLR, one that meets a minimum set of requirements identified in the thematic analysis: ‘ A rapid review is a form of knowledge synthesis that accelerates the process of conducting a traditional systematic review through streamlining or omitting a variety of methods to produce evidence in a resource-efficient manner .’ This interpretation aligns with more than 50% of RLRs identified in this study. The authors additionally provided several other definitions, depending on specific situations or requirements (e.g., when RLR is produced on stakeholder’s request). It was additionally underlined that RLRs should be driven by the need of timely evidence for decision-making purposes [ 11 ].

Rapid reviews vary in their objective, format, and methods used for evidence synthesis. This is a quite new area, and still no agreement on optimal methods can be found [ 5 ]. All of the definitions are highlighting that RLRs are completed within shorter timeframes than SLRs, and also lack of time is one of the main reasons they are conducted. It has been suggested that most rapid reviews are conducted within 12 weeks; however, some of the resources suggest time between a few weeks to no more than 6 months [ 5 , 6 ]. Some of the definitions are highlighting that RLRs follow the SLR process, but certain phases of the process are simplified or omitted to retrieve information in a time-saving way [ 6 , 7 ]. Different mechanisms are used to enhance the timeliness of reviews. They can be used independently or concurrently: increasing the intensity of work by intensifying the efforts of multiple analysts by parallelization of tasks, using review shortcuts whereby one or more systematic review steps may be reduced, automatizing review steps by using new technologies [ 5 ]. The UK government report [ 8 ] referred to two different RLRs: in the form of quick scoping reviews (QSR) or rapid evidence assessments (REA). While being less resource and time-consuming compared to standard SLRs, QSRs and REAs are designed to be similarly transparent and to minimize bias. QSRs can be applied to rather open-ended questions, e.g., ‘what do we know about something’ but both, QSRs and REAs, provide an understanding of the volume and characteristics of evidence on a specific topic, allowing answering questions by maximizing the use of existing data, and providing a clear picture of the adequacy of existing evidence [ 8 ].

Research questions and searches

The guidelines suggest creating a clear research question and search protocol at the beginning of the project. Additionally, to not duplicate RLRs, the Cochrane Rapid Reviews Methods Group encourages all people working on RLRs to consider registering their search protocol with PROSPERO, the international prospective register of reviews; however, so far they are not formally registered in most cases [ 5 , 6 ]. They also recommend involving key stakeholders (review users) to set and refine the review question, criteria, and outcomes, as well as consulting them through the entire process [ 11 ].

Regarding research questions, it is better to structure them in a neutral way rather than focus on a specific direction for the outcome. By doing so, the researcher is in a better position to identify all the relevant evidence [ 7 ]. Authors can add a second, supportive research question when needed [ 8 ]. It is encouraged to limit the number of interventions, comparators and outcomes, to focus on the ones that are most important for decision-making [ 11 ]. Useful could be also reviewing additional materials, e.g., SLRs on the topic, as well as conducting a quick literature search to better understand the topic before starting with RLRs [ 7 ]. In SLRs researchers usually do not need to care a lot about time spent on creating PICOS, they need to make sure that the scope is broad enough, and they cannot use many restrictions. When working on RLRs, a reviewer may spend more or less time defining each of the components of the study question, and the main step is making sure that PICOS addresses the needs of those who requested the rapid review, and at the same time, it is feasible within the required time frame [ 7 ]. Search protocol should contain an outline of how the following review steps are to be carried out, including selected search keywords and a full strategy, a list of data sources, precise inclusion and exclusion criteria, a strategy for data extraction and critical appraisal, and a plan of how the information will be synthesized [ 8 ].

In terms of searches running, in most cases, an exhaustive process will not be feasible. Researchers should make sure that the search is effective and efficient to produce results in a timely manner. Cochrane Rapid Reviews Methods Group recommends involving an information specialist and conducting peer review of at least one search strategy [ 11 ]. According to the rapid review guidebook by McMaster University [ 7 ], it is important that RLRs, especially those that support policy and program decisions, are being fed by the results of a body of literature, rather than single studies, when possible. It would result in more generalizable findings applied at the level of a population and serve more realistic findings for program decisions [ 7 ]. It is important to document the search strategy, together with a record of the date and any date limits of the search, so that it can easily be run again, modified, or updated. Furthermore, the information on the individual databases included in platform services should always be reported, as this depends on organizations’ subscriptions and must be included for transparency and repeatability [ 7 , 8 ]. Good solution for RLRs is narrowing the scope or searching a limited number of databases and other sources [ 7 ]. Often, the authors use the PubMed/MEDLINE, Cochrane Library, and Embase databases. In most reviews, two or more databases are searched, and common limits are language (usually restricted to English), date, study design, and geographical area. Some RLRs include searching of grey literature; however, contact with authors is rather uncommon [ 5 , 8 ]. According to the flexible framework for restricted systematic review published by the University of Oxford, the search should be run in at least one major scientific database such as PubMed, and one other source, e.g., Google Scholar [ 9 ]. Grey literature and unpublished evidence may be particularly needed and important for intervention questions. It is related to the fact that studies that do not report the effects of interventions are less likely to be published [ 8 ]. If there is any type of evidence that will not be considered by the RLRs, e.g., reviews or theoretical and conceptual studies, it should also be stated in the protocol together with justification [ 8 ]. Additionally, authors of a practical guide published by WHO suggest using a staged search to identify existing SLRs at the beginning, and then focusing on studies with other designs [ 5 ]. If a low number of citations have been retrieved, it is acceptable to expand searches, remove some of the limits, and add additional databases and sources [ 7 ].

Searching for RLRs is an iterative process, and revising the approach is usually needed [ 7 ]. Changes should be confirmed with stakeholders and should be tracked and reflected in the final report [ 5 ].

The next step in the rapid review is the selection of studies consisting of two phases: screening of titles and abstracts, and analysis of full texts. Prior to screening initiation, it is recommended to conduct a pilot exercise using the same 30–50 abstracts and 5–10 full-texts for the entire screening team in order to calibrate and test the review form [ 11 ]. In contrast to SLRs, it can be done by one reviewer with or without verification by a second one. If verification is performed, usually the second reviewer checks only a subset of records and compares them. Cochrane Group, in contrast, recommends a stricter approach: at least 20% of references should be double-screened at titles and abstracts stage, and while the rest of the references may be screened by one reviewer, the excluded items need to be re-examined by second reviewer; similar approach is used in full-text screening [ 11 ]. This helps to ensure that bias was reduced and that the PICOS criteria are applied in a relevant way [ 5 , 8 , 9 , 11 ]. During the analysis of titles and abstracts, there is no need to report reasons for exclusion; however, they should be tracked for all excluded full texts [ 7 ].

Data extraction and quality assessment

According to the WHO guide, the most common method for data extraction in RLRs is extraction done by a single reviewer with or without partial verification. The authors point out that a reasonable approach is to use a second reviewer to check a random sample of at least 10% of the extractions for accuracy. Dual performance is more necessary for the extraction of quantitative results than for descriptive study information. In contrast, Cochrane group recommends that second reviewer should check the correctness and completeness of all data [ 11 ]. When possible, extractions should be limited to key characteristics and outcomes of the study. The same approach to data extraction is also suggested for a quality assessment process within rapid reviews [ 5 , 9 , 11 ]. Authors of the guidebook from McMaster University highlight that data extraction should be done ideally by two reviewers independently and consensus on the discrepancies should always be reached [ 7 ]. The final decision on the approach to this important step of review should depend on the available time and should also reflect the complexity of the research question [ 9 ].

For screening, analysis of full texts, extractions, and quality assessments, researchers can use information technologies to support them by making these review steps more efficient [ 5 ].

Before data reporting, a reviewer should prepare a document with key message headings, executive summary, background related to the topic and status of the current knowledge, project question, synthesis of findings, conclusions, and recommendations. According to the McMaster University guidebook, a report should be structured in a 1:2:20 format, that is, one page for key messages, two pages for an executive summary, and a full report of up to 20 pages [ 7 ]. All the limitations of the RLRs should be analyzed, and conclusions should be drawn with caution [ 5 ]. The quality of the accumulated evidence and the strength of recommendations can be assessed using, e.g., the GRADE system [ 5 ]. When working on references quoting, researchers should remember to use a primary source, not secondary references [ 7 ]. It would be worth considering the support of some software tools to automate reporting steps. Additionally, any standardization of the process and the usage of templates can support report development and enhance the transparency of the review [ 5 ].

Ideally, all the review steps should be completed during RLRs; however, often some steps may need skipping or will not be completed as thoroughly as should because of time constraints. It is always crucial to decide which steps may be skipped, and which are the key ones, depending on the project [ 7 ]. Guidelines suggest that it may be helpful to invite researchers with experience in the operations of SLRs to participate in the rapid review development [ 5 , 9 ]. As some of the steps will be completed by one reviewer only, it is important to provide them with relevant training at the beginning of the process, as well as during the review, to minimize the risk of mistakes [ 5 ].

Additional information

Depending on the policy goal and available resources and deadlines, methodology of the RLRs may be modified. Wilson et al. [ 10 ] provided extensive guidelines for performing RLR within days (e.g., to inform urgent internal policy discussions and/or management decisions), weeks (e.g., to inform public debates), or months (e.g., to inform policy development cycles that have a longer timeline, but that cannot wait for a traditional full systematic review). These approaches vary in terms of data synthesis, types of considered evidence and project management considerations.

In shortest timeframes, focused questions and subquestions should be formulated, typically to conduct a policy analysis; the report should consist of tables along with a brief narrative summary. Evidence from SLRs is often considered, as well as key informant interviews may be conducted to identify additional literature and insights about the topic, while primary studies and other types of evidence are not typically feasible due to time restrictions. The review would be best conducted with 1–2 reviewers sharing the work, enabling rapid iterations of the review. As for RLRs with longer timeline (weeks), these may use a mix of policy, systems and political analysis. Structure of the review would be similar to shorter RLRs – tabular with short narrative summary, as the timeline does not allow for comprehensive synthesis of data. Besides SLRs, primary studies and other evidence may be feasible in this timeframe, if obtained using the targeted searches in the most relevant databases. The review team should be larger, and standardized procedures for reviewing of the results and data extraction should be applied. In contrast to previous timeframe, merit review process may be feasible. For both timeframes, brief consultations with small transdisciplinary team should be conducted at the beginning and in the final stage of the review to discuss important matters.

For RLRs spanning several months, more comprehensive methodology may be adapted in terms of data synthesis and types of evidence. However, authors advise that review may be best conducted with a small review team in order to allow for more in-depth interpretation and iteration.

Studies analyzing methodology

There have been two interesting publications summarizing the results of Delphi consensus on the RLR methodology identified and included in this review [ 12 , 13 ].

Tricco et al. [ 13 ] first conducted an international survey and scoping review to collect information on the possible approaches to the running of rapid reviews, based on which, they employed a modified Delphi method that included inputs from 113 stakeholders to explore the most optimized approach. Among the six most frequent rapid review approaches (not all detailed here) being evaluated, the approach that combines inclusion of published literature only, a search of more than one database and limitations by date and language, study selection by one analyst, data extraction, and quality assessment by one analyst and one verifier, was perceived as the most feasible approach (72%, 81/113 responses) with the potentially lowest risk of bias (12%, 12/103). The approach ranked as the first one when considering timelines assumes updating of the search from a previously published review, no additional limits on search, studies selection and data extraction done by one reviewer, and no quality assessment. Finally, based on the publication, the most comprehensive RLRs can be made by moving on with the following rules: searching more than one database and grey literature and using date restriction, and assigning one reviewer working on screening, data extraction, and risk of bias assessment ( Table 1 ). Pandor et al. [ 12 ] introduced a decision tool for SelecTing Approaches for Rapid Reviews (STARR) that were produced through the Delphi consensus of international experts through an iterative and rigorous process. Participants were asked to assess the importance of predefined items in four domains related to the rapid review process: interaction with commissioners, understanding the evidence base, data extraction and synthesis methods, and reporting of rapid review methods. All items assigned to four domains achieved > 70% of consensus, and in that way, the first consensus-driven tool has been created that supports authors of RLRs in planning and deciding on approaches.

Six most frequent approaches to RLRs (adapted from Tricco et al. [ 13 ]).

Haby et al. [ 14 ] run searches of 11 databases and two websites and developed a comprehensive overview of the methodology of RLRs. With five SLRs and one RCT being finally included, they identified the following approaches used in RLRs to make them faster than full SLRs: limiting the number and scope of questions, searching fewer databases, limited searching of grey literature, restrictions on language and date (e.g., English only, most recent publications), updating the existing SLRs, eliminating or limiting hand searches of reference lists, noniterative search strategies, eliminating consultation with experts, limiting dual study selection, data extraction and quality assessment, minimal data synthesis with short concise conclusions or recommendations. All the SLRs included in this review were consistent in stating that no agreed definition of rapid reviews is available, and there is still no final agreement on the best methodological rules to be followed.

Gordon et al. [ 4 ] explained the advantages of performing a focused review and provided 12 tips for its conduction. They define focused reviews as ‘a form of knowledge synthesis in which the components of the systematic process are applied to facilitate the analysis of a focused research question’. The first tip presented by the authors is related to deciding if a focused review is a right solution for the considered project. RLRs will suit emerging topics, approaches, or assessments where early synthesis can support doctors, policymakers, etc., but also can direct future research. The second, third, and fourth tips highlight the importance of running preliminary searches and considering narrowing the results by using reasonable constraints taking into account the local context, problems, efficiency perspectives, and available time. Further tips include creating a team of experienced reviewers working on the RLRs, thinking about the target journal from the beginning of work on the rapid review, registering the search protocol on the PROSPERO registry, and the need for contacting authors of papers when data available in publications are missing or incongruent. The last three tips are related to the choice of evidence synthesis method, using the visual presentation of data, and considering and describing all the limitations of the focused review.

Finally, a new publication by Tricco et al. from 2022, describing JBI position statement [ 15 ] underlined that for the time being, there is no specific tool for critical appraisal of the RLR’s methodological quality. Instead, reviewers may use available tools to assess the risk of bias or quality of SLRs, like ROBIS, the JBI critical appraisal tools, or the assessment of multiple systematic reviews (AMSTAR).

Inconsistency in the definitions and methodologies of RLR

Although RLR was broadly perceived as an approach to quicken the conduct of conventional SLR, there is a lack of consensus on the formal definition of the RLR, so as to the best approaches to perform it. Only in 2021, a study proposing unified definition was published; however, it is important to note that the most accurate definition was only matching slightly over 50% of papers analysed by the authors, which underlines the lack of homogeneity in the field [ 11 ]. The evidence-based supporting methods are evolving, and more evidence is needed to define the most robust approaches [ 5 ].

Diverse terms are used to describe the RLR, including ‘rapid review’, focused systematic review’, ‘quick scoping reviews’, and ‘rapid evidence assessments’. Although the general principles of conducting RLR are to accelerate the whole process, complexity was seen in the methodologies used for RLRs, as reflected in this study. Also, inconsistencies related to the scope of the questions, search strategies, inclusion criteria, study screening, full-text review, quality assessment, and evidence presentation were implied. All these factors may hamper decision-making about optimal methodologies for conducting rapid reviews, and as a result, the efficiency of RLR might be decreased. Additionally, researchers may tend to report the methodology of their reviews without a sufficient level of detail, making it difficult to appraise the quality and robustness of their work.

Advantages and weaknesses of RLR

Although RLR used simplified approaches for evidence synthesis compared with SLR, the methodologies for RLR should be replicable, rigorous, and transparent to the greatest extent [ 16 ]. When time and resources are limited, RLR could be a practical and efficient tool to provide the summary of evidence that is critical for making rapid clinical or policy-related decisions [ 5 ]. Focusing on specific questions that are of controversy or special interest could be powerful in reaffirming whether the existing recommendation statements are still appropriate [ 17 ].

The weakness of RLR should also be borne in mind, and the trade-off of using RLR should be carefully considered regarding the thoroughness of the search, breadth of a research question, and depth of analysis [ 18 ]. If allowed, SLR is preferred over RLR considering that some relevant studies might be omitted with narrowed search strategies and simplified screening process [ 14 ]. Additionally, omitting the quality assessment of included studies could result in an increased risk of bias, making the comprehensiveness of RLR compromised [ 13 ]. Furthermore, in situations that require high accuracy, for example, where a small relative difference in an intervention has great impacts, for the purpose of drafting clinical guidelines, or making licensing decisions, a comprehensive SLR may remain the priority [ 19 ]. Therefore, clear communications with policymakers are recommended to reach an agreement on whether an RLR is justified and whether the methodologies of RLR are acceptable to address the unanswered questions [ 18 ].

Disclosure statement

No potential conflict of interest was reported by the author(s).

• Open access
• Published: 21 May 2024

A scoping review on bovine tuberculosis highlights the need for novel data streams and analytical approaches to curb zoonotic diseases

• Kimberly Conteddu   ORCID: orcid.org/0000-0002-3883-4137 1 ,
• Holly M. English 1 ,
• Andrew W. Byrne 2 ,
• Bawan Amin 1 ,
• Laura L. Griffin 1 ,
• Prabhleen Kaur 3 ,
• Virginia Morera-Pujol 1 ,
• Kilian J. Murphy 1 ,
• Michael Salter-Townshend 3 ,
• Adam F. Smith 4 , 5 , 6 &
• Simone Ciuti 1  

Veterinary Research volume  55 , Article number:  64 ( 2024 ) Cite this article

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Zoonotic diseases represent a significant societal challenge in terms of their health and economic impacts. One Health approaches to managing zoonotic diseases are becoming more prevalent, but require novel thinking, tools and cross-disciplinary collaboration. Bovine tuberculosis (bTB) is one example of a costly One Health challenge with a complex epidemiology involving humans, domestic animals, wildlife and environmental factors, which require sophisticated collaborative approaches. We undertook a scoping review of multi-host bTB epidemiology to identify trends in species publication focus, methodologies, and One Health approaches. We aimed to identify knowledge gaps where novel research could provide insights to inform control policy, for bTB and other zoonoses. The review included 532 articles. We found different levels of research attention across episystems, with a significant proportion of the literature focusing on the badger-cattle-TB episystem, with far less attention given to tropical multi-host episystems. We found a limited number of studies focusing on management solutions and their efficacy, with very few studies looking at modelling exit strategies. Only a small number of studies looked at the effect of human disturbances on the spread of bTB involving wildlife hosts. Most of the studies we reviewed focused on the effect of badger vaccination and culling on bTB dynamics with few looking at how roads, human perturbations and habitat change may affect wildlife movement and disease spread. Finally, we observed a lack of studies considering the effect of weather variables on bTB spread, which is particularly relevant when studying zoonoses under climate change scenarios. Significant technological and methodological advances have been applied to bTB episystems, providing explicit insights into its spread and maintenance across populations. We identified a prominent bias towards certain species and locations. Generating more high-quality empirical data on wildlife host distribution and abundance, high-resolution individual behaviours and greater use of mathematical models and simulations are key areas for future research. Integrating data sources across disciplines, and a “virtuous cycle” of well-designed empirical data collection linked with mathematical and simulation modelling could provide additional gains for policy-makers and managers, enabling optimised bTB management with broader insights for other zoonoses.

1 Introduction

Emerging infectious diseases represent a significant public health concern as they become more prevalent worldwide [ 1 , 2 , 3 ]. It is estimated that about 60% of emerging infectious diseases are zoonotic, 72% of which have been estimated to originate from wildlife [ 2 , 3 ]. In 2019, thirteen different zoonoses had confirmed cases in humans within the European Union [ 4 ]. This has likely been accelerated by exponential growth in global population size and mobility with associated increases in urbanisation and concurrent loss of natural habitats. It has also led to increasing occurrences of human-wildlife interactions (e.g., improper waste disposal, intentional feeding of wildlife, movement of wildlife to human-dominated areas) and, therefore, exposure to zoonotic diseases [ 5 , 6 , 7 ]. Contact between humans, livestock and other captive animals, and wildlife species is only expected to keep increasing, leading to concerns about increased incidences of zoonotic disease transfer [ 6 , 8 , 9 ]. The question, however, remains of how to best track and manage emerging diseases.

A critical example is Zoonotic Tuberculosis (zoonotic TB), which was estimated in 2016 to be linked to 147 000 human cases and 12 500 deaths worldwide [ 10 ]. Zoonotic TB is driven mainly by Mycobacterium bovis (i.e., the causative agent of Bovine Tuberculosis—also as bovine TB or bTB), which is transmitted by several wildlife hosts and livestock. Britain and Ireland, as well as many other countries worldwide [ 10 ], have been increasingly impacted by bTB, resulting in significant economic loss. In Ireland, for instance, 4.89% of cattle herds tested positive for bTB in 2023, leading to the humane killing of 28 868 cattle [ 11 ]. This is in addition to the economic costs associated with the national bTB eradication program with €92 million spent in 2018 alone [ 12 ]. Similar trends can be observed in the UK, with £70 million spent annually for bTB prevention and control [ 13 ]. This disease also raises welfare concerns for wildlife hosts, especially considering its high prevalence in the wild. Badgers ( Meles meles ), for example, have been shown to have a bTB prevalence exceeding 40% in hotspot areas in Ireland [ 14 ], and red deer in Spain have been estimated to have a prevalence of up to 50% [ 15 ].

Bovine TB eradication is prioritised by governments and researchers due to the significant health concerns and economic (trade) impacts. Despite decades of control efforts in several countries, the pathogen has successfully avoided eradication. There are complex reasons as to why this is the case [ 16 ], but a primary factor relates to its complex dynamics of transmission and maintenance across differing hosts and the environment. Therefore, new thinking may be required to further investigate if disease control can be driven toward eradication. Detecting gaps in the current bTB literature is an essential step required to identify target areas for future research and to further hone government eradication strategies.

One way in which this may be addressed, and which requires assessment as to its prevalence in the literature, is through multidisciplinary, coordinated collaborations between the public health sector, veterinarians, ecologists and wildlife managers. The importance of interdisciplinary approaches is highlighted by the interlinked nature of human, animal and ecosystem health, which led to the concept of “One World One Health™” [ 17 , 18 ]. Despite such multidisciplinary efforts, the effect of stressors (i.e., direct and/or indirect disturbances such as hunting, habitat loss, and more broadly habitat and climate change) on animal ecology within human-dominated landscapes and the potential emergence of zoonotic disease is still understudied [ 1 ]. For example, we are aware that human-driven changes in the environment can modify interactions between hosts, change host and vector densities, and alter host longevity and movement [ 19 , 20 ]. A study by Castillo-Neyra et al. showed that rabies transmission was spatially linked to water channels, which act as ecological corridors connecting multiple susceptible populations and facilitating pathogen spread and persistence [ 20 ]. However, with cities expanding and providing urban corridors to wildlife, pathogen persistence could become even more of an issue [ 20 ], confirming the importance of studying the effect of human perturbations on animal ecology and related implications in disease ecology.

Additionally, transmission of different zoonoses often involve multiple agents including humans and a diverse range of wild and domestic animals. In order to understand the processes behind their transmission, it is essential to clearly disentangle the role of each agent involved [ 19 ]. Due to the complexity of disease transmission and the maintenance of infection within multiple wildlife hosts, for example between bovine and badger populations in the case of bTB, the individual components of the transmission chain are often studied separately. This can limit our understanding of the subtle underlying effects explaining disease emergence and transmission. Therefore, a holistic approach is essential to develop a complete picture of the transmission dynamics of zoonotic diseases like bTB [ 19 ]; for example, recent research on rabies has shown how empirical data can be used to elucidate epidemiological dynamics [ 21 ].

However, even in cases where empirical data is used, there may be limited power, which can impact results and interpretation. In these cases, evidence from empirical data can now be boosted by mathematical simulations, which are powerful tools for predicting disease transmission trajectories [ 22 ]. Simulations of disease transmission through compartmental models (e.g., the Susceptible, Infectious, and/or Removed (SIR) model and its variations) have been used in a variety of disease systems, including the recent COVID-19 pandemic. COVID-19, however, is exceptional in the level of global concern garnered and resultant significant investment in funding. This meant that large empirical datasets were also made readily accessible, which made direct complex modelling possible [ 23 ]. Other zoonoses are typically more difficult to model this way due to the lack of empirical data on disease transmission and associated hosts [ 24 ]. Mathematical simulations, using for example SIR models, therefore create opportunities to also model these zoonoses. In addition, such simulations allow us to undertake experiments that are currently logistically unfeasible, too costly, too complex or on “unobservable” phenomena [ 22 , 25 ].

As mentioned, lack of information on associated hosts and transmission pathways is often a limiting factor in modelling zoonoses and may potentially also be an issue in bTB research. Studying interactions between and within host species, as well as the role played by each host in the transmission chain, can enable us to better understand zoonotic disease dynamics. While simulations can achieve much, it is important to note that interactions amongst wild animals are heterogeneous by nature and vary significantly between different populations as well as individuals. Therefore, it is important to account for this variability to understand the mechanisms behind transmission and subsequently be able to predict and control disease spread [ 8 ]. This can be achieved by using network modelling, where heterogeneous contacts between animals can be used to simulate disease transmission [ 8 , 24 ], for example using social network analysis (SNA) [ 8 ]. SNA can be beneficial for disease management since it enables us to identify “super-spreaders” (i.e., highly connected individuals) which can then be targeted for vaccination, allowing for a dramatic reduction in transmission [ 1 , 8 ]. In addition, new research is looking at integrating SNA with molecular epidemiology (phylodynamics) to better estimate transmission pathways and direction of transmission between individuals [ 26 ].

Finally, it is of key importance that models of disease risk and distribution consider variances across space and time [ 27 , 28 ], which enables us to identify disease clusters [ 5 ] and model host abundance [ 29 ]. As ecological processes occur at different scales (from single study sites to macroecological scales), the spatial scale used for disease distribution modelling is crucial in understanding how these processes exacerbate the spread of zoonotic diseases, such as bTB [ 30 , 31 ]. Large spatial scales (i.e., global, continental) can examine the broader picture and disentangle how host abundance and abiotic factors influence disease prevalence [ 19 ]. Smaller spatial scales (i.e., country, region) can be used to examine population dynamics and pathogen genetic diversity at the local level [ 19 ]. Temporal patterns are important to consider as many zoonotic diseases show seasonal variations (e.g., Zoonotic enteric diseases such as Salmonella spp, Escherichia coli , Giardia spp) as well as daily variations (i.e., due to the circadian rhythm of microbes and pathogens as well as chronobiology of wildlife hosts) in their infection patterns [ 32 , 33 , 34 ]. It is of key importance that any gaps in bTB research pertaining to factors discussed above be identified, in order to inform future research direction.

Here, we aimed to uncover empirical and methodological gaps in the peer-reviewed literature on bTB. Our intention is to use bTB as an example of a complex multihost zoonotic disease for which recent developments with sampling design, animal monitoring tools and technology, and mathematical modelling has helped to fill the gaps in knowledge and improve our understanding and ability to combat zoonotic diseases more generally.

To achieve our goal, we developed a scoping review of bTB multihost epidemiology focusing on 18 research questions (reported in Table  1 and conceptually summarised in Figure 1 ) regarding the type of study, whether, which and how wildlife species have been monitored, what kind of sampling designs and methodological approaches have been used, and whether epidemiological empirical data have been collected. We then gathered data from the peer-reviewed literature on the mechanisms driving inter- and intraspecies bTB transmission, looking in particular at novel and multi-disciplinary approaches. Our goal is that our work will spark renewed discussion on how to monitor and deal with zoonotic diseases, direct future research, and stimulate focused funding efforts (Figure 2 ).

Key host species and topics of interest we screened for in the bovine tuberculosis scientific literature published between 1981 and 2022 . bTB host species include cattle as well as a range of wild species: badger, wild boar, cervid species (with the following species identified in the literature screened: white-tailed deer, red deer, fallow deer, roe deer, wapiti elk, sika deer and muntjac deer), brush-tailed possum and wild buffalo. The circles on the outside illustrate the key information sought in peer-reviewed papers dealing with bTB, which has been expanded and clarified in Table  1 : type of data collected by researchers; whether spatial analyses were carried out (i.e., in cattle and or wildlife); what type of spatial and temporal scales were considered; whether environmental variables were taken into account (i.e., environment in the farm, environment around the farm and/or weather variables); whether the methodological approach captured the direction of disease transmission; whether the study used common epidemiological modelling techniques (i.e., compartmental models, transmission rates), or whether the study included intra/interspecies interactions in their methodology (i.e., what type of interactions did they look at - e.g., direct and/or indirect, what type of equipment was used to get interactions data and what methodology was used to analyse the data); finally, if human perturbations (i.e., forest felling, culling, vaccination) were taken into account when looking at variables affecting bTB spread, and management solutions to offset the spread of bTB, if any. Animal silhouettes were downloaded from PhyloPic [ 134 ]. Cattle, cervid, brushed-tailed possum and wild boar silhouettes are under: CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. Buffalo silhouette is by Jan A. Venter, Herbert H. T. Prins, David A. Balfour & Rob Slotow (vectorized by T. Michael Keesey) under: Attribution 3.0 Unported (CC BY 3.0) [ 135 ]. Badger silhouette is by Anthony Caravaggi under: Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0) [ 135 ]

Cascade diagram of the process used in the selection of relevant papers.

We conducted a scoping review (as per PRISMA guidelines) [ 35 ] by sourcing peer-reviewed papers using Web of Science (Clarivate, 2021 Online Version) focusing on bovine tuberculosis, and more specifically its most common cause, Mycobacterium bovis , in cattle and several key wildlife hosts. The search terms and list of articles have been summarised in Additional file 1 . We identified 3531 potentially relevant papers (i.e., the search included all years of publication) which were uploaded and screened for duplicates using EndNote (Clarivate, Version 20.1.0.15341)(Figure  2 ). Relevant articles were then selected using a PEO (Population, Exposure, Outcomes) eligibility criterium structure [ 36 ]. The aim of the PEO is to identify articles of interest by selecting the “Population” (i.e., the subject being affected by the disease/health condition) for a particular “Exposure” (i.e., a disease/health condition) and either a particular “Outcome” or “Themes’’ to examine [ 36 , 37 ]. The PEO eligibility criterium was chosen since it was in line with the recommendations given for scoping reviews that target literature on etiology and risk factors, such as a particular disease. We decided to use a modified version of the PEO framework structure which also includes themes of interest as potential “Outcomes” [ 37 ], as summarised in Table  2 to aid reproducibility. All papers that did not meet the eligibility criteria listed in Table  2 were removed (Figure 2 ). The papers were screened by one researcher who coded 18 variables (stored in an excel spreadsheet) to answer the questions of interest summarised in Table  1 . The results were then imported and plotted using ggplot2 in R version 4.1.1 [ 38 ].

Our results are based on 532 peer-reviewed papers published between 1981 and 2022. The study location of the papers was representative of 6 continents and 52 different countries (Figure 3 ). The continent with the highest number of studies on bTB is Europe ( n  = 303, 169 of which were from the UK), significantly higher than those carried out in much larger continents such as Africa, Asia, and both Americas (Figure 3 ). We screened all papers for 18 different variables (addressing our 18 questions, see Table  1 ) which we summarised in the following section under the heading: 3.1 general characteristics (Sub-headings: “Study species and wildlife species”; “Management and data type”), 3.2 data analysis (Sub-headings: “Spatial analysis, spatial scale and temporal scale”; “Farm environment and human perturbations”), and 3.3 epidemiological analysis (Sub-headings: “Intra- and interspecies interactions”, “Direction of transmission and compartmental models”). Note that most plots presented below have a sample size of n  = 532, corresponding to the number of papers screened, with a few exceptions where this sample size is higher (for example, in relation to temporal scale included in the study, if a paper reported multiple temporal scales, therefore contributing to multiple levels of a category) or lower (for example, in relation to epidemiology, where variables of interest were analysed only in the subset of papers describing studies that included epidemiological interactions).

World map showing number of papers screened per country . Number of papers per continent: Europe (303), Africa (68), Oceania (60), North America (53), South America (29), Asia (26).

3.1 General characteristics

3.1.1 study species and wildlife species.

We found that 41% of bTB papers focused on cattle only, whereas 30% of them included both cattle and wildlife species and 29% targeted only wildlife species (Figure 4 A). Among those papers reporting wildlife data, we found that the European badger attracted most research effort (50% of wildlife studies), followed by cervid species (28%: 13% red deer, 11% white-tailed deer, 5% fallow deer, 3% roe deer, 2% wapiti elk, from hereinafter referred to as simply elk, and < 1% of studies including sika and muntjac deer), wild boar (18%), brushed tailed possum (17%) and buffalo (4%) (Figure 4 B).

Species, data and study type . Number of papers screened and reporting data on A study species type (whether the study was on cattle and/or wildlife), B wildlife species, C management (whether a paper investigated potential management solutions and their efficacy), D and data type. Animal silhouettes were downloaded from PhyloPic [ 134 ]. Cattle, cervid, brushed-tailed possum and wild boar silhouettes are under: CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. Buffalo silhouette is by Jan A. Venter, Herbert H. T. Prins, David A. Balfour & Rob Slotow (vectorized by T. Michael Keesey) under: Attribution 3.0 Unported (CC BY 3.0) [ 135 ]. Badger silhouette is by Anthony Caravaggi under: Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0) [ 135 ].

3.1.2 Management and data type

Our results highlighted that only 25% of the studies dealt with management solutions (Figure 4 C). Management strategies mainly included culling (18%) or vaccination (6%), with 5% looking at other strategies (e.g., fencing, sterilisation). We also found that most papers gathered original empirical data (79%), and papers only using simulations were limited (4%), with a remaining 17% of papers combining empirical data and simulations (Figure 4 D).

3.2 Data analysis

3.2.1 spatial analysis, spatial scale and temporal scale.

We found that the majority of papers did not include any spatial analysis. Those that did focused on spatial patterns in wildlife (30%, Figure 5 B) slightly more than cattle (28%, Figure 5 A). Among the 149 papers that investigated spatial analysis in cattle, 58% looked at bTB risk and probability of infection; 16% looked at cattle interactions with wildlife, 13% analysed the spatial distribution of bTB positive biological samples, 11% investigated cattle movement outside the farm. Interactions between farm animals and cattle movement inside the farm were included in 5% and 1% of papers, respectively. Among the 161 studies which investigated spatial behaviour in wildlife (Figure 5 B), analysis was undertaken using a variety of methodologies; direct observations (36%), satellite GPS telemetry (19%), spatial patterns predicted by future scenarios modelling or mathematical simulations (19%), genetic samples (11%), camera traps (7%), proximity loggers (4%) and indirect observations (e.g., faecal samples for population density estimations; 1%).

Spatial and temporal analysis . Number of papers screened and reporting data on A spatial analysis of cattle (whether the study included any type of spatial analysis), B spatial analysis of wildlife, C spatial scale, and D temporal scale.

We also found that most papers included spatial scales at the regional level or smaller (72%), with less than 4% papers looking at national and/or international spatial scales (Figure 5 C). In regard to temporal scales, 36% of the studies considered interannual variability, whereas 17% tackled intra-annual variability. Thirty-six percent of the studies did not analyse any intra- or interannual temporal variability (Figure 5 D). Only 4% of the studies looked at fine-scale variability (e.g., days), whereas in a few instances the year of study was not reported at all (4%). Finally, 12% of papers included predictions for temporal patterns into future scenarios.

3.2.2 Farm environment and human perturbations

When looking at farm characteristics, 50% of the studies included some type of herd data (e.g., herd size, bTB history), with 44% not including any type of in-farm environmental variables (Figure 6 A) and 24% of papers incorporating other types of farm characteristics. These included environmental conditions on the farm (e.g., natural habitats, land-fragmentation; 13%), farm location in respect to other farms (11%) and farm location in respect to wildlife (6%).

Environmental variables . Number of papers screened and reporting data on A in-farm environment (whether the paper analysis included variables explaining environmental characteristics inside the farm), B outside farm environment, C human perturbations (whether the paper analysed the effect of human disturbances on bTB transmission dynamics), and D weather variables.

Environmental conditions outside the farm were included in 33% of the papers’ data analysis (Figure 6 B). These studies mainly looked at habitat characteristics around the farm (e.g., wildlife presence, natural habitats), with two papers also including variables focusing on habitat variation (e.g., forest clearfell, new artificial plantations). We also looked at weather variables (e.g., temperature, rainfall) and observed that 4% of papers included these as part of their analysis (Figure 6 D). Finally, 25% of the papers screened included human perturbation variables with the vast majority looking at the effect of vaccination and culling on transmission dynamics (Figure 6 C).

3.3 Epidemiological analysis

3.3.1 intra- and interspecies interactions.

We found that most papers (69%) did not include an analysis on interactions, with 25% of papers looking at intraspecies transmission and 14% at interspecies transmission (Figure 7 A). Among the interaction studies, 33% included direct interactions, 17% included indirect interactions and 51% included both (Figure  7 B). In addition, interaction data were mostly collected using simulations (39%), followed by technological tools (29%; e.g., GPS, proximity loggers, camera traps), and direct observations (23%), with genetic sampling used in 7% of papers (Figure 7 C). The methodology used to analyse interaction data also varied between papers with 28% of papers using differential equations (e.g., SIR models, discreate models), 19% social network analysis, 18% linear models (i.e., including generalized mixed models as well as simple linear regressions) and 38% using a variety of statistical techniques (e.g., t-test/ANOVA, stochastic models) (Figure 7 D).

Interaction analysis . Number of papers screened and reporting data on A interactions (inclusion of interaction analysis i.e., intra- and/or interspecies interactions), B interaction type, C the way interactions were monitored, and D interaction data analysis statistical approach.

3.3.2 Direction of transmission and compartmental models

We found that a limited proportion of the papers (8%) included direction of transmission in their analysis (Figure  8 A). We also found that epidemiological modelling techniques (e.g., compartmental models and transmission rates) were adopted in 15% of the studies (Figure 8 B).

Epidemiological analysis . Number of papers screened and reporting data on A direction of transmission (whether this was analysed in the paper, e.g., transmission across species), B epidemiological modelling (i.e., papers included compartmental models and/or transmission rates in the analysis).

4 Discussion

In this review we found that there has been significant research focusing on the badger-cattle bTB episystem. We acknowledge, however, that we also found a very limited number of studies on other episystems [ 39 , 40 , 41 ]. Our spatially-explicit overview of bTB research efforts (Figure 3 ) highlights how the badger-cattle episystem has been the focus of most research done to date, highlighting a huge amount of money and research effort on bTB transmission dynamics across Europe and particularly in Britain and Ireland. However, there has been far less attention given on other multi-host episystems of countries in southern Africa, Asia and both South and North America. We believe we have more to learn from these chronically understudied systems.

Our scoping review found a limited number of studies focusing on management solutions and their efficacy, with very few looking at modelling exit strategies [ 42 , 43 ]. This is due to the paucity of studies using mathematical simulations, not only to better understand and predict possible outputs of management solutions, but also to explore long-term bTB dynamics under different scenarios (e.g. [ 44 , 45 ]). Only a small number of studies have looked at the effect of human disturbances on the spread of bTB in wildlife host species, and this knowledge gap needs to be tackled as we are aware that human perturbations may exacerbate zoonotic outbreaks and spread [ 46 , 47 , 48 ]. Most of the studies we reviewed have focused on the effect of badger vaccination and culling on bTB dynamics with only three studies looking at how other human perturbations may affect these dynamics [ 49 , 50 , 51 ]. Additionally, only two focused on the effect of habitat change (e.g., clearfell forest operations) on bTB breakdowns [ 52 , 53 ]. Finally, we observed that there is only a few studies looking at the effect of weather variables (i.e., rainfall, soil humidity, temperature etc.) on bTB spread or risk [ 54 ]. This is especially important when considering wildlife-cattle transmission since it is now thought to occur also through environmental sources [ 55 ].

We have carefully evaluated the outcome of our scoping review, and in the following sections we have summarised data types and methodological approaches which, we believe, could contribute to gaining further insights into bTB epidemiology. Based on our review, we have identified a significant gap when it comes to prediction and simulation models, which would be a useful tool for managers to assess disease risk under different land use and climate change scenarios. Another major gap is the lack of integration between empirically-informed tactical (short-term decision support) and strategic (larger spatial scales and longer term) models being used concurrently in single studies (though we do note that there are exceptions, for instance Brooks-Pollock et al. [ 56 ]). Future research should include compartmental models fitted across space, linked via meta-populations and/or real-landscape multi-host episystems; or agent-based models (ABMs) with empirical data feedback loops. We describe such modelling approaches and their prerequisites in the following sections, beginning with data and monitoring programs, and we continue with recent advances in technology, mathematical tools and analytical solutions.

4.1 Empirical data and long-term monitoring programs: involving stakeholders and setting up fixed long-term monitoring stations across large spatial scales

As good quality data is required to generate informed strategies on wildlife interventions, we need reliable data sources to model spatial distribution and abundance of the host species involved in transmission. In reference to the badger-cattle bTB episystem in western Europe, both badgers (with several examples among the literature: [ 57 , 58 ]), and cattle [ 59 ] have been extensively monitored. However, in some populations, it is possible that deer and wild boar may also play a role in the local spread and maintenance of infection [ 60 ]. In Britain and Ireland, the significance of deer as a wildlife host impacting bTB epidemiology has been uncertain [ 61 ]. However, recent research is starting to uncover the role deer may play at local scales where conditions favour the transmission between badgers-deer-cattle [ 62 ]. There could be opportunities to gather data in collaboration with hunters (as has occurred in France [ 63 ] and Spain [ 64 ], for example) to have access to a high number of deer samples, within and across countries (large spatial scales) and across years (long-term temporal scales). Involving stakeholders like hunters may provide the unique opportunity to collect pictures of clearly infected animals (e.g., small to large white, tan, or yellow lesions on the lungs, rib cage, or in the chest cavity) - to be submitted via smartphone applications (see [ 65 ]). These stakeholders may also gather biological samples to be collected by government officials at ad hoc collecting centres. This type of information would boost opportunities for monitoring the dynamics of the disease across multiple spatio-temporal scales and in relation to bTB occurrence in the two other hosts in the system (badgers and cattle). The ability to involve stakeholders across large spatial scales (e.g., hunters, farmers, foresters) may help to establish systematic, relatively inexpensive, and long-term monitoring programmes. These can provide species presence-only and presence-absence data for Bayesian species and disease distribution models (described in Sect. " Modelling and mathematical simulations: social network analysis Bayesian species distribution models, and agent based models "), allowing managers to access up to date risk scenarios. This approach can also highlight hotspots of disease outbreak that could drive focused longitudinal studies using satellite telemetry on multiple species simultaneously. This would enable us to better disentangle species overlaps and contact rates [ 66 , 67 ]. The role of stakeholders/citizen scientists in this bTB example could be confirming infection, which is almost never inexpensive, although there is the hope that cheaper field tests will be released in the next decade. The veracity of the data collected and level of engagement from stakeholders/citizen could also be a problem which needs to be taken into consideration. For the time being, a well distributed number of samples could be collected from hunters to cover large areas systematically and limit the costs required for testing.

When it comes to establishing long-term monitoring programs, fixed long-term sampling stations across large-spatial scales can capture wildlife population spatio-temporal dynamics. This can, on one hand, provide data on occurrence, relative density, and spatio-temporal overlaps of the host species and, on the other hand, gather key empirical data required to parameterise mathematical simulations. Camera traps are a popular and effective tool for estimating state variables of wildlife populations [ 68 ]. For ungulates, they have successfully been used to understand temporal behaviour (e.g., diel activity patterns, [ 69 ]), spatial behaviour (e.g., occupancy, [ 70 ]), and abundance (e.g., density, [ 71 ]). Camera traps have been used for quantifying temporal and spatial overlap of wild ungulates with domestic animals in open systems [ 72 , 73 ] with varying results [ 74 ]. Kukielka et al. demonstrated their use in identifying hotspots of indirect wildlife–livestock overlap for the prevention of bTB crossover [ 72 ]. For wildlife, especially ungulates, camera traps offer powerful monitoring solutions not only to measure abundance and spatial overlap, but also to understand behavioural dynamics that may align closely with disease risk. An example is the use of camera traps to individually recognise animals, which has been shown to be possible in a recent study by Hinojo et al. [ 75 ]. They demonstrated how roe deer ( Capreolus capreolus ) antler shapes could be used to identify distinct individuals. This data could be used to obtain better estimates of abundance as well as to build wildlife social networks (which will be discussed in more detail in Sect. " Modelling and mathematical simulations: social network analysis, Bayesian species distribution models, and agent based models ") and therefore provide information on contact rates between and within species. The parameters from these analyses would be useful as an input for mathematical simulations to help better understand disease transmission dynamics in wildlife populations.

The use of camera traps as well as satellite telemetry can be quite challenging to use in developing countries since they can be extremely expensive (satellite telemetry, in particular) as well as difficult to use when collecting data in remote locations (camera traps, in particular). In addition, the invasive nature of satellite telemetry - which requires trapping animals - often makes it hard to collect data from enough individuals from an ethical, logistical and administrative points of view. Therefore, to improve our understanding of episystems in developing nations, advances in non-invasive diagnostic techniques and eDNA (i.e., a genetic sampling technique that uses environmental sources - such as water and soil - to extract genetic information used for biosecurity and biomonitoring purposes) are essential [ 76 , 77 , 78 , 79 ]. An example of a widely used non-invasive sampling technique is faecal sampling [ 76 , 77 , 78 , 79 , 80 , 81 ]. Faecal samples are a relatively inexpensive way of monitoring diseases and health status in wildlife species. It is also possible to collect a high number of samples in a short period of time, which is especially important for long-term monitoring programs of wildlife hosts. Collecting eDNA can be even faster and is especially useful for long term spatio-temporal dynamics of infectious pathogens at the wider scale, which can improve the monitoring of zoonoses at the country and continental level [ 77 ].

However, timing is key when monitoring diseases as infectious pathogens can mutate and be rapidly transmitted between wildlife, humans, and domestic animals, with potentially devastating impacts on human health and animal welfare. Therefore, novel and rapid genetic techniques, such as culture-free pathogen genetic sequencing [ 82 ], can greatly benefit disease surveillance by decreasing the time needed to sequence pathogens and, consequently, the time needed to make essential ecological management decisions and activate public health responses. In addition, these new sequencing technologies can be very useful during wildlife field studies in isolated areas since they can be rapidly deployed and need limited laboratory equipment for processing [ 82 ]. In addition, when monitoring zoonosis such as bTB and collecting related data (invasively or not) it is important to recall the characteristics of the bacterium itself, Mycobacterium bovis . For example, different lineages exist across the globe [ 83 ] with different strains potentially showing different evolutionary [clock] rates. This greatly affects the rate at which the bacterium needs to be monitored among countries, and we believe that faster sequencing technologies will be of great help in tracking the evolution and spread of different lineages, informing adaptive management of bTB (and zoonosis in general) at the local level.

4.2 Recent advances with technology can help to gather data for mathematical simulations: interindividual variability within animal populations and human socio-economic factors matter and should be taken into account

Animal-attached sensors, i.e., biologging [ 84 , 85 ], can allow us to disentangle animal behaviour and the movement patterns that promote disease transmission. GPS units are the most widely used of these sensors, providing data on animal space use. Proximity sensors can detect when two or more sensor-equipped animals interact and can be used to detect direct encounters which may result in disease transmission. Collars with both GPS units and proximity sensors have been used concurrently on badgers and cattle uncovering that, while badgers show a habitat preference for cattle pastures, there were rare to no direct contacts between the two species [ 86 , 87 ]. This indicates that environmental transmission may play an important role in the case of bTB [ 87 ]. As such, proximity sensors allow insights which are not obtainable through investigating shared space use alone. When the disease state of an individual is known, proximity sensors can also provide information on if and how the duration of exposure to said individual affects transmission rate to other members of the population [ 88 ]. Other biologging sensors, including accelerometers, magnetometers, and gyroscopes, are used to classify distinct behaviours from logged datasets [ 85 ]. Behaviour classification allows activity budgets to be built so that behaviours which increase the likelihood of acquiring or transmitting pathogens can be detected and mapped in the landscape. Accelerometers have also been used to compare micro-movements in diseased and healthy animals, with diseased animals exhibiting differences in posture, gait dynamism (e.g., the “bounce” in subsequent walking steps) and energy levels [ 89 ]. Monitoring such micro-movements in cattle could act as a warning sign to test herds for bTB when signs of illness are detected, e.g. by adapting existing systems in place to monitor lameness through accelerometry [ 90 ]. These effects of disease on the internal state of animals yield important insights into how disease status impacts animal movement patterns and therefore disease spread.

Biologging and satellite telemetry monitoring can, on one hand, provide answers aimed at understanding the transmission dynamics within multi-host disease systems [ 87 , 91 , 92 ] and, on the other hand, provide highly valuable empirical data that are strongly needed by parameter hungry mathematical simulations [ 88 ]. However, when tracking animals, special care should be taken to understand the behaviour of those animals that we are monitoring, and specifically whether we are following a bolder subset of the overall population that are easier to trap. This applies also to where we study animals which will provide empirical data for mathematical simulations, because behaviour and movement ecology may vary significantly depending on the level of human disturbance. We are aware that tracking multiple individuals of multiple species can be expensive and not accessible unless large amounts of funding is available. However, recent technological advances with satellite telemetry using LoRaWAN transmission technology [ 93 , 94 ] have been developed to monitor livestock at affordable prices (e.g. less than 100 euros for 1 GPS unit), opening up new opportunities for extensive monitoring programmes in wildlife, within and across species.

The concept of One Health has highlighted the role that human activities play in the spread of zoonotic diseases [ 95 ]. For example, urbanisation, improper waste disposal, and the intentional feeding of wildlife have been shown to result in wildlife movement into human-dominated areas [ 7 ], which may facilitate disease transfer to humans and other animal communities [ 96 ]. However, evidence has shown that only a select proportion of individuals within wildlife populations will engage in interactions with humans [ 97 ] or utilise these human-dominated areas [ 7 , 98 ]. Individual variation in movement patterns [ 99 ], sociability [ 100 ], and immunological defence [ 101 ], among others, impacts disease transmission and spread [ 102 ]. There is also evidence that certain behavioural types have higher infection rates than others (e.g. [ 103 , 104 ]), although the causal direction may be difficult to determine since infections also alter host behaviour [ 103 , 105 ]. Regardless, to gain a more complete understanding of disease spread, future studies should incorporate this individual variation. These studies often utilise direct behavioural observations, since these are an invaluable data source that can be used to determine which individuals in a known population are more likely to engage in close-contact interactions with humans [ 97 ] or access human areas (e.g., farmland) [ 106 ]. This can provide us with information on which individuals in a population may be at “higher risk” of transferring disease to humans or to other animal populations.

Nevertheless, considering human behaviour is also fundamental in infectious disease transmission. The One Health definition has changed in 2022 accordingly and now it includes the importance of society and its diversity in values and beliefs in effectively fighting zoonoses [ 107 , 108 ]. Collaboration between scientific disciplines is not enough to improve current and emerging infectious disease transmission. It is fundamental that community members and expertise at every level, from village to continent, be included if we wish to equitably improve human health and animal welfare [ 107 ]. In this way we may also improve the effectiveness of disease management solutions by tailoring them to communities instead of trying to use the same solutions in different areas without taking into account socio-economical differences.

4.3 Modelling and mathematical simulations: social network analysis, Bayesian species distribution models, and agent based models

Social network analysis (SNA) is a powerful tool in uncovering the causes and consequences of disease transmission within animal communities [ 109 , 110 ]. In the past decade SNA has mainly focused on understanding contact and transport networks of cattle and livestock movements, as well as wildlife movements [ 111 , 112 , 113 ]. Nonetheless, it could be expanded to better unravel the dynamics of disease transmission between wildlife populations and livestock [ 110 ]. Unlike in domestic cattle, the movements and interactions of wildlife can be challenging to track. As a result, a small proportion of individuals are typically monitored using biologging and satellite telemetry, as discussed earlier. Recent advances in statistical analysis of social networks have paved the way to obtain better inferences from limited data [ 114 , 115 , 116 ]. The first step is to identify the network metrics affecting disease transmission dynamics that best suits the disease system under study (e.g., transitivity, betweenness centrality) [ 114 , 115 ]. Using global metrics of a social network, for example, can help estimate potential changes in the overall structure of the wildlife population. A commonly used global metric when studying disease transmission dynamics is transitivity, which represents the tendency of a population to cluster together and is considered to be negatively correlated with disease transmission rates [ 113 ]. Local network metrics, on the other hand, can help in understanding social characteristics at the individual level. A type of local metric is betweenness centrality, which represents the tendency of an individual to serve as a bridge between one part of the community and another (i.e., a community in SNA is a group of nodes, for example individual animals, with denser connections between each other compared to other nodes in the network), helping the selection of individuals to be vaccinated/removed from the population.

Once we have selected the metrics to use, they can be tested via pre-network permutations of available observations to ensure that the available data sufficiently captures non-random interactions among the animals. However, when using small samples for SNA we also must be careful on what we infer from it. Recent research [ 115 ] has shown that estimates may be inaccurate, or “noisy”, at low sample sizes. Therefore, stable metrics with respect to low sample sizes should be identified before making inferences. Research on data collected for wild ungulates [ 115 ], for example, shows that the betweenness centrality values of smaller samples remain well correlated with those in larger samples, indicating that this metric can be used even when the social network is built using a small sample of the population. Similar correlation analysis can be done for other network metrics, mainly in cases of limited data availability for disease transmission. Whenever limited animals from a population are monitored, confidence intervals around the network metrics should also be obtained to make informed decisions using statistical evidence.

Using the methodologies discussed above (see Silk et al. and Kaur et al. for more details, [ 113 , 115 ]) we now have the possibility of analysing all telemetry data collected thus far on species involved in bTB transmission (e.g., badgers, wild boar; but also applicable to species from other disease systems) to test hypotheses on disease transmission dynamics. For example, we can now use these statistical techniques to better understand behavioural patterns of wildlife species, as well as comparing networks overtime and how wildlife behaviour can be affected by perturbations in the environment (e.g., climate change, land-use change or other type of anthropogenic factors) even when only limited data is available [ 115 ]. In addition, it will help in collecting future data since these methodologies can be used to estimate the minimum number of individuals needed in order to reliably build a social network, which can vary enormously depending on the scope of the project as well as the wildlife species of interest. This will, for example, help in answering specific questions regarding the role of deer species in bTB transmission by simultaneously collecting telemetry data on badgers and deer species in Ireland.

Knowing the distribution and abundance of wildlife vectors (i.e., a living agent that carries and transmits pathogens - e.g. HIV, Covid-19, bTB - to other living beings) is also essential when aiming to reduce zoonotic risk [ 117 , 118 ]. To that aim, Species Distribution Models (SDMs) can be used to produce models of the distribution and abundance of species based on occurrence data [ 119 ]. In recent years spatial modelling has undergone a conceptual and technical revolution. New modelling techniques within Bayesian [ 120 ] and Machine Learning frameworks [ 121 ] allow us to develop spatially explicit models of animal abundance and distributions with unprecedented accuracy, and the improvement of computational power allows computers to rise to the challenge and cope with the high computational demands of these models. The flexibility of the new techniques allows us to use different types of data (e.g., individual tracking data, survey data, and even citizen science data) and combine them in what are called Integrated Species Distribution Models (ISDMs), while still taking into account the different observational processes of each type of data, to produce accurate models even in data scarce systems [ 122 ]. In addition, these new techniques also allow for the calculation of uncertainty in a spatially explicit manner, which will help us evaluate the quality of the models and better interpret the results. Bayesian ISDMs using INLA (i.e., Integrated Nested Laplace Approximation) [ 123 ] were used to model the distribution of red, sika and fallow deer in Ireland, which are vectors of bTB [ 65 ]. The models produced, for the first time, relative abundance and distribution maps for each species, which will be an essential tool for deer population management and thus towards bTB eradication. They are already being used to determine high sika-density areas for a pilot study on the effect of deer on biodiversity, which will provide further management tools for the overabundant deer populations in Ireland. In addition, hierarchical Bayesian models are also the basis of a new project aimed at modelling European badger sett distribution, badger density, and their body condition. These three models will be linked to bTB infection in badgers and outbreaks in cattle, in an attempt for the first time to link badger spatial ecology to bTB management and eradication in Ireland (V. Morera-Pujol 2023, personal communication).

Agent-based simulations are another useful modelling approach, or complementary tool to traditional methods, when data is limited/not available; helping elucidate transient effects of wildlife disease transmission in human-dominated landscapes [ 25 ]. These models serve as a computational laboratory that allow researchers to plug-in available real-world data and parameterise both agents (for instance, a badger) and the environment (for instance, a mosaic of natural habitats and farms). This enables researchers to empirically test if animal behaviour in response to landscape change or management interventions modulates disease risk dynamics over time and space [ 124 ]. Recent technological advancements have bolstered agent-based simulations allowing for high-resolution spatio-temporal models that incorporate geographic information systems (GIS) data to create hyper realistic environments, and machine learning algorithms to introduce cognition and applied decision making for agents. Furthermore, process-driven agent-based models (e.g., disease transmission) can be integrated into larger mechanistic agent-based models (e.g., ecosystem scale epi-dynamics) for increasingly higher-resolution models that reduce uncertainty and overly-theoretical parameterisation of model entities [ 25 ]. The development of highly-realistic agent-based simulations, parameterised with high-resolution data, for the management of bovine tuberculosis in multi-host systems can contribute to answering important policy questions and how best to select management directions. In practice, this allows for the totality of data collected in complex multi-host systems to be incorporated into a single environment where they may be measured against one another in the simulation to deduce the possible effects of each predictor. Take for example the European badger as the primary wildlife host in Ireland as a case study. Badgers are prevalent in the agroecological mosaic of natural habitats and farms in Ireland. Agent-based simulations can utilise data from badger tracking studies [ 51 , 125 , 126 ], habitat suitability [ 127 ], culling and vaccination programmes [ 128 ] and disturbance regimes [ 52 , 129 ] to simulate badger movement and behaviour realistically. GIS data for farm location and characteristics [ 130 ], as well as ecological and environmental data streams, can then place the badger agent into a highly realistic environment to examine how these factors affect badger movement, behaviour, and other parameters, for instance, contact rates with domestic animals. Interactions between agents and the environment can be modulated by sub-models to further increase the strength of the model. For instance, weather sub-models (e.g., rainfall) may influence agricultural practice and thus contact rates, as well as the length of time Mycobacterium bovis persists in the environment. Alternatively, disease transmission could also be sub-modelled so that contact rates may/may not result in infection [ 25 ]. Finally, management decisions can be trialled within the simulation to see how likely decisions change the status of disease within the study system, allowing for “What if?” scenarios to play out without risk to animal or human welfare or livelihoods.

5 Conclusion

Our exploration of the recent literature on multi-host bTB episystems, as an example of zoonotic One Health challenges, has revealed a significant body of work utilising a diversity of methodologies at different spatio-temporal scales and subjects (individual vs. group) levels. There was a significant bias in the literature towards one particular episystem, the badger-cattle system that predominates in north-western Europe, reflecting large financial burdens (for both governments as well as the agricultural sector) and research funding investments. Alternatively, there were comparatively less publications from the global south, especially in complex muti-host episystems in southern Africa and India. In such episystems, the cost-effective and efficient collection, collation, and use of data are essential to drive greatest added value to inform on policy options.

Given the results from our scoping review, we reflect on several areas where progress could be made. This includes the need for high-quality data on wildlife hosts, even in episystems where significant research investments have already been made. Such careful collection and utilisation of empirical data could then help feed the development of social network analyses, Bayesian distribution models and eventually mathematical and simulation-based models. Mathematical simulations, such as ABMs trained on synthetic data and parameterised by real empirical data, can answer questions that would otherwise be too costly, unethical, or both. Such models can also be used to explore different scenarios in an increasingly human-dominated world, under different levels of land-use and climate change, or with the appearance of invasive species in already complex multi-host epidemiological systems. In addition, it can help build cross-disciplinary bridges with other areas, deriving significant insights into interspecific transmission like phylodynamic modelling.

We have used our Irish experience to inspire researchers from across the globe; Ireland invests considerably in surveying, culling, and vaccinating badgers [ 131 , 132 ]. However, the question remains - which applies to other countries and zoonotic episystems - should we be doing more or can we be smarter with the data we already have? We suggest the latter. Yes, there is a need to be smarter, arranging ad hoc data collections using the latest technological tools to estimate unknown or uncertain parameters. But we also have to focus our efforts on mathematical modelling (ABMs, INLA-Bayesian) to optimise our information gain from the large, high-quality datasets collected over the last few decades (and for sparser datasets, taking advantage of recently developed statistical tools for enhanced inferences, see [ 54 , 68 ]). We have (almost) all the data required to parameterise simulations with significant utility: this should be one main focus in future research. We believe that, ideally, the feedback of simulation and mathematical tools to inform data collection, and the “virtuous cycle” of feeding this new data to improve the next generation of model is a priority for decision making tools for policy makers and programme managers.

Availability of data and materials

The datasets generated and/or analysed during the current study are available in the “A scoping review on bovine tuberculosis highlights the need for novel data streams and analytical approaches to curb zoonotic diseases” repository [ 133 ].

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Acknowledgements

We would like to acknowledge Sylvia Power for helping in reviewing the final version of the manuscript.

This publication has emanated from research conducted with the financial support of Science Foundation Ireland under Grant number 18/CRT/6049. For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. In addition, HME is funded by an Irish Research Council Government of Ireland postgraduate scholarship. KJM and VMP is funded by the Department of Agriculture, Food and the Marine (DAFM) in Ireland through the research grant 2019-R-417.

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KC: conceived and designed the review, acquired, analysed and interpreted the data, wrote the first draft of the manuscript. HME, LLG: edited and revised the manuscript and contributed to the interpretation of the data. AWB: edited and revised the manuscript and contributed to the interpretation of the data, drafted the policy and research implications. BA, PK, VMP, KJM, AFS, MST: edited specific sections of the discussion and revised the whole manuscript. SC: conceived and designed the review, edited the manuscript and supervised the process. All Authors read and approved the final manuscript.

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Conteddu, K., English, H.M., Byrne, A.W. et al. A scoping review on bovine tuberculosis highlights the need for novel data streams and analytical approaches to curb zoonotic diseases. Vet Res 55 , 64 (2024). https://doi.org/10.1186/s13567-024-01314-w

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