research studies on higher education

The Review of Higher Education

Penny A. Pasque, The Ohio State University; Thomas F. Nelson Laird, Indiana University, Bloomington

Journal Details

The Review of Higher Education  is interested in empirical research studies, empirically-based historical and theoretical articles, and scholarly reviews and essays that move the study of colleges and universities forward. The most central aspect of  RHE  is the saliency of the subject matter to other scholars in the field as well as its usefulness to academic leaders and public policymakers. Manuscripts submitted for  RHE  need to extend the literature in the field of higher education and may connect across fields and disciplines when relevant. Selection of articles for publication is based solely on the merits of the manuscripts with regards to conceptual or theoretical frameworks, methodological accurateness and suitability, and/or the clarity of ideas and gathered facts presented. Additionally, our publications center around issues within US Higher Education and any manuscript that we send for review must have clear implications for US Higher Education. 

Guidelines for Contributors

Manuscripts should be typed, serif or san serif text as recommended by APA 7th edition (e.g., 11-point Calibri, 11-point Arial, and 10-point Lucida Sans Unicode, 12-point Times New Roman, 11-point Georgia, 10-point Computer Modern) double-spaced throughout, including block quotes and references. Each page should be numbered on the top right side of the page consecutively and include a running head. Please supply the title of your submission, an abstract of 100 or fewer words, and keywords as the first page of your manuscript submission (this page does not count towards your page limit). The names, institutional affiliations, addresses, phone numbers, email addresses and a short biography of authors should appear on a separate cover page to aid proper masking during the review process. Initial and revised submissions should not run more than 32 pages (excluding abstract, keywords, and references; including tables, figures and appendices). Authors should follow instructions in the 7th edition Publication Manual of the American Psychological Association; any manuscripts not following all APA guidelines will not be reviewed. Please do not change fonts, spacing, or margins or use style formatting features at any point in the manuscript except for tables. All tables should be submitted in a mutable format (i.e. not a fixed image). Please upload your manuscript as a word document. All supporting materials (i.e., tables, figures, appendices) should be editable in the manuscript or a separate word document (i.e., do not embedded tables or figures). For a fixed image, please upload a separate high-resolution JPEG.

Authors should use their best judgment when masking citations. Masking some or all citations that include an author’s name can help prevent reviewers from knowing the identities of the authors. However, in certain circumstances masking citations is unnecessary or could itself reveal the identities of manuscript authors. Because authors are in the best position to know when masking citations will be effective, the editorial team will generally defer to them for these decisions.

Manuscripts are to be submitted in Word online at  mc.manuscriptcentral.com/rhe . (If you have not previously registered on this website, click on the “Register here” link to create a new account.) Once you log on, click on the “Author Center” link and then follow the printed instructions to submit your manuscript.

The term “conflict of interest” means any financial or other interest which conflicts with the work of the individual because it (1) could significantly impair the individual’s objectivity or (2) could create an unfair advantage for any person or organization. We recommend all authors review and adhere to the ASHE Conflict of Interest Policy before submitting any and all work. Please refer to the policy at  ashe.ws/ashe_coi

Please note that  The Review of Higher Education  does not require potential contributors to pay an article submission fee in order to be considered for publication.  Any other website that purports to be affiliated with the Journal and that requires you to pay an article submission fee is fraudulent. Do not provide payment information. Instead, we ask that you contact the  RHE  editorial office at  [email protected]  or William Breichner the Journals Publisher at the Johns Hopkins University Press  [email protected] .

Author Checklist for New Submissions

Page Limit.  Manuscripts should not go over 32 pages (excluding abstract, keywords, and references; including tables, figures and appendices.)

Masked Review.  All author information (i.e., name, affiliation, email, phone number, address) should appear on a separate cover page of the manuscript. The manuscript should have no indication of authorship. Any indication of authorship will result in your manuscript being unsubmitted.

Formatting.  Manuscripts should be typed, serif or san serif text as recommended by APA 7th edition (e.g., 11-point Calibri, 11-point Arial, and 10-point Lucida Sans Unicode, 12-point Times New Roman, 11-point Georgia, 10-point Computer Modern), double-spaced throughout, including block quotes and references, and each page should be numbered on the top right side of the page consecutively. Authors should follow instructions in the 7th edition Publication Manual of the American Psychological Association; this includes running heads, heading levels, spacing, margins, etc.. Any manuscripts not following APA 7th edition will be unsubmitted. [Please note, the  RHE  editorial team recommends 12-pt Times New Roman font to ensure proper format conversion within the ScholarOne system.]

Abstract.  All manuscripts must include an abstract of 100 words or fewer, and keywords as the first page of your manuscript submission (this page does not count towards your page limit).

Author Note.  An Author’s note may include Land Acknowledgments, Disclosure Statement (i.e., funding sources), or other acknowledgments. This should appear on your title page (not in the masked manuscript).  

Tables.  All tables should be editable. Tables may be uploaded in the manuscript itself or in a separate word document. All tables must be interpretable by readers without the reference to the manuscript. Do not duplicate information from the manuscript into tables. Tables must present additional information from what has already been stated in the manuscript.

Figures.  Figures should be editable in the manuscript or a separate word document (i.e., no embedded tables). For fixed images, please upload high-resolution JPEGs separately.

References.  The reference page should follow 7th edition APA guidelines and be double spaced throughout (reference pages do not count toward your page limit). 

Appendices.  Appendices should generally run no more than 3 manuscript pages. 

Additional Checklist for Revised Submissions

Revised manuscripts should follow the checklist above, with the following additional notes: 

Page Limit.  Revised manuscripts should stay within the page limit for new submissions (32 pages). However, we do realize that this is not always possible, and we may allow for a couple of extra pages for your revisions. Extensions to your page length will be subject to editor approval upon resubmission, but may not exceed 35 pages (excluding abstract, keywords, and references).

• Author Response to Reviewer Comments.  At the beginning of your revised manuscript file, please include a separate masked statement that indicates fully [1] all changes that have been made in response to the reviewer and editor suggestions and the pages on which those changes may be found in the revised manuscript and [2] those reviewer and editor suggestions that are not addressed in the revised manuscript and a rationale for why you think such revisions are not necessary. This can be in the form of a table or text paragraphs and must appear at the front of your revised manuscript document. Your response to reviewer and editor comments will not count toward your manuscript page limit. Please note that, because you will be adding your response to the reviewer and editor feedback to the beginning of your submission, this may change the page numbers of your document unless you change the pagination and start your manuscript itself on page 1. The choice is yours but either way, please ensure that you reference the appropriate page numbers within your manuscript in these responses. Additionally, when you submit your revised manuscript, there will be a submission box labeled “Author Response to Decision Letter”. You are not required to duplicate information already provided in the manuscript, but instead may use this to send a note to the reviewer team (e.g., an anonymous cover letter or note of appreciation for feedback). Please maintain anonymity throughout the review process by NOT including your name or by masking any potentially identifying information when providing your response to the reviewer's feedback (both in documents and the ScholarOne system).

Editorial Correspondence

Please address all correspondence about submitting articles (no subscriptions, please) to one or both of the following editors:

Dr. Penny A. Pasque, PhD Editor, Review of Higher Education 341 C Ramseyer Hall 29 W. Woodruff Avenue The Ohio State University Columbus, OH 43210 email:  [email protected]

Dr. Thomas F. Nelson Laird, PhD Editor, Review of Higher Education 201 North Rose Avenue Indiana University School of Education Bloomington, IN 47405-100 email:  [email protected]

Submission Policy

RHE publishes original works that are not available elsewhere. We ask that all manuscripts submitted to our journal for review are not published, in press or submitted to other journals while under our review. Additionally, reprints and translations of previously published articles will not be accepted.

Type of Preliminary Review

RHE utilizes a collaborative review process that requires several members of the editorial team to ensure that submitted manuscripts are suitable before being sent out for masked peer-review. Members of this team include a Editor, Associate Editor and Managing Editors. Managing Editors complete an initial review of manuscripts to ensure authors meet RHE ’s Author Guidelines and work with submitting authors to address preliminary issues and concerns (i.e., APA formatting). Editors and Associate Editors work together to decide whether it should be sent out for review and select appropriate reviewers for the manuscript.

Type of Review

When a manuscript is determined as suitable for review by the collaborative decision of the editorial team, Editors and/or Associate Editors will assign reviewers. Both the authors’ and reviewers’ are masked throughout the review and decision process.

Criteria for Review

Criteria for review include, but are not limited to, the significance of the topic to higher education, completeness of the literature review, appropriateness of the research methods or historical analysis, and the quality of the discussion concerning the implications of the findings for theory, research, and practice. In addition, we look for the congruence of thought and approach throughout the manuscript components.

Type of Revisions Process

Some authors will receive a “Major Revision” or “Minor Revision” decision. Authors who receive such decisions are encouraged to carefully attend to reviewer’s comments and recommendations and resubmit their revised manuscripts for another round of reviews. When submitting their revised manuscripts, authors are asked to include a response letter and indicate how they have responded to reviewer comments and recommendations. In some instances, authors may be asked to revise and resubmit a manuscript more than once.

Review Process Once Revised

Revised manuscripts are sent to the reviewers who originally made comments and recommendations regarding the manuscript, whenever possible. We rely on our editorial board and ad-hoc reviewers who volunteer their time and we give those reviewers a month to provide thorough feedback. Please see attached pdf for a visual representation of the RHE workflow .

Timetable (approx.)

• Managing Editor Technical Checks – 1-3 days
• Editor reviews and assigns manuscript to Associate Editors – 3-5 days
• Associate Editor reviews and invites reviewers – 3-5 days
• Reviewer comments due – 30 days provided for reviews
• Associate Editor makes a recommendation –  5-7 days
• Editor makes decision – 5-7 days
• If R&R, authors revise and resubmit manuscript – 90 days provided for revisions
• Repeat process above until manuscript is accepted or rejected -

Type of review for book reviews

Book reviews are the responsibility of the associate editor of book reviews. Decisions about acceptance of a book review are made by that associate editor.

The Hopkins Press Journals Ethics and Malpractice Statement can be found at the ethics-and-malpractice  page.

The Review of Higher Education expects all authors to review and adhere to ASHE’s Conflict of Interest Policy before submitting any and all work. The term “conflict of interest” means any financial or other interest which conflicts with the work of the individual because it (1) could significantly impair the individual’s objectivity or (2) could create an unfair advantage for any person or organization. Please refer to the policy at ashe.ws/ashe_coi .

Guidelines for Book Reviews

RHE publishes book reviews of original research, summaries of research, or scholarly thinking in book form. We do not publish reviews of books or media that would be described as expert opinion or advice for practitioners.

The journal publishes reviews of current books, meaning books published no more than 12 months prior to submission to the associate editor in charge of book reviews.

If you want to know whether the RHE would consider a book review before writing it, you may email the associate editor responsible for book reviews with the citation for the book.

Reviewers should have scholarly expertise in the higher education research area they are reviewing.

Graduate students are welcome to co-author book reviews, but with faculty or seasoned research professionals as first authors.

Please email the review to the associate editor in charge of book reviews (Timothy Reese Cain, [email protected] ), who will work through necessary revisions with you if your submission is accepted for publishing.

In general, follow the APA Publication Manual, 7th edition.

Provide a brief but clear description and summary of the contents so that the reader has a good idea of the scope and organization of the book. This is especially important when reviewing anthologies that include multiple sections with multiple authors.

Provide an evaluation of the book, both positive and negative points. What has been done well? Not so well? For example the following are some questions that you can address (not exclusively), as appropriate:

What are the important contributions that this book makes?

What contributions could have been made, but were not made?

What arguments or claims were problematic, weak, etc.?

How is the book related to, how does it supplement, or how does it complicate current work on the topic?

To which audience(s) will this book be most helpful?

How well has the author achieved their stated goals?

Use quotations efficiently to provide a flavor of the writing style and/or statements that are particularly helpful in illustrating the author(s) points. 

If you cite any other published work, please provide a complete reference.

Please include a brief biographical statement immediately after your name, usually title and institution. Follow the same format for co authored reviews. The first author is the contact author.

Please follow this example for the headnote of the book(s) you are reviewing: Stefan M. Bradley. Upending the Ivory Tower: Civil Rights, Black Power, and the Ivy League. New York: New York University Press, 2018. 465 pp. $35. ISBN 97814798739999.

Our preferred length is 2,000–2,500 words in order for authors to provide a complete, analytical, review. Reviews of shorter books may not need to be of that length.

The term “conflict of interest” means any financial or other interest which conflicts with the work of the individual because it (1) could significantly impair the individual’s objectivity or (2) could create an unfair advantage for any person or organization. We recommend all book reviewers read and adhere to the ASHE Conflict of Interest Policy before submitting any and all work. Please refer to the policy at ashe.ws/ashe_coi

NOTE: If the Editor has sent a book to an author for review, but the author is unable to complete the review within a reasonable timeframe, we would appreciate the return of the book as soon as possible; thanks for your understanding.

Please send book review copies to the contact above. Review copies received by the Johns Hopkins University Press office will be discarded.

Penny A. Pasque,         The Ohio State University

Thomas F. Nelson Laird,         Indiana University-Bloomington

Associate Editors

Angela Boatman,         Boston College

Timothy Reese Cain (including Book Reviews),         University of Georgia

Milagros Castillo-Montoya,         University of Connecticut

Tania D. Mitchell,         University of Minnesota

Chrystal George Mwangi       George Mason University

Federick Ngo,        University of Nevada, Las Vegas

Managing Editors

Stephanie Nguyen,         Indiana University Bloomington

Monica Quezada Barrera,         The Ohio State University

Editorial Board

Sonja Ardoin,         Clemson University

Peter Riley Bahr,        University of Michigan

Vicki Baker,      Albion College

Allison BrckaLorenz,        Indiana University Bloomington

Nolan L. Cabrera,        The University of Arizona

Brendan Cantwell,        Michigan State University

Rozana Carducci,        Elon University

Deborah Faye Carter,         Claremont Graduate University

Ashley Clayton,         Louisiana State University

Regina Deil-Amen,         The University of Arizona 

Jennifer A. Delaney,     University of Illinois Urbana Champaign

Erin E. Doran,    Iowa State University

Antonio Duran,   Arizona State University 

Michelle M. Espino,        University of Maryland 

Claudia García-Louis,        University of Texas, San Antonio

Deryl Hatch-Tocaimaza,        University of Nebraska-Lincoln

Nicholas Hillman,        University of Wisconsin-Madison

Cindy Ann Kilgo,        Indiana University-Bloomington

Judy Marquez Kiyama,  University of Arizona

Román Liera,        Montclair State University

Angela Locks,        California State University, Long Beach

Demetri L. Morgan,  Loyola University Chicago

Rebecca Natow,         Hofstra University 

Z Nicolazzo,        The University of Arizona

Elizabeth Niehaus,        University of Nebraska-Lincoln

Robert T. Palmer,        Howard University

Rosemary Perez,        University of Michigan

OiYan Poon,         Spencer Foundation 

Kelly Rosinger,        The Pennsylvania State University

Vanessa Sansone,         The University of Texas at San Antonio

Tricia Seifert,        Montana State University

Barrett Taylor,         University of North Texas 

Annemarie Vaccaro,  University of Rhode Island

Xueli Wang,        University of Wisconsin-Madison

Stephanie Waterman,         University of Toronto 

Rachelle Winkle-Wagner,         University of Wisconsin-Madison

Association for the Study of Higher Education Board of Directors

The Review of Higher Education is the journal of Association for the Study Higher Education (ASHE) and follows the ASHE Bylaws and Statement on Diversity. 

ASHE Board of Directors

Abstracting & Indexing Databases

• Current Contents
• Web of Science
• Dietrich's Index Philosophicus
• IBZ - Internationale Bibliographie der Geistes- und Sozialwissenschaftlichen Zeitschriftenliteratur
• Internationale Bibliographie der Rezensionen Geistes- und Sozialwissenschaftlicher Literatur
• Academic Search Alumni Edition, 9/1/2003-
• Academic Search Complete, 9/1/2003-
• Academic Search Elite, 9/1/2003-
• Academic Search Premier, 9/1/2003-
• Current Abstracts, 9/1/2003-
• Education Research Complete, 3/1/1997-
• Education Research Index, Sep.2003-
• Education Source, 3/1/1997-
• Educational Administration Abstracts, 3/1/1991-
• ERIC (Education Resources Information Center), 1977-
• MLA International Bibliography (Modern Language Association)
• Poetry & Short Story Reference Center, 3/1/1997-
• PsycINFO, 2001-, dropped
• Russian Academy of Sciences Bibliographies
• TOC Premier (Table of Contents), 9/1/2003-
• Scopus, 1996-
• Gale Academic OneFile
• Gale OneFile: Educator's Reference Complete, 12/2001-
• Higher Education Abstracts (Online)
• ArticleFirst, vol.15, no.3, 1992-vol.35, no.2, 2011
• Electronic Collections Online, vol.20, no.1, 1996-vol.35, no.2, 2011
• Periodical Abstracts, v.26, n.4, 2003-v.33, n.3, 2010
• PsycFIRST, vol.24, no.3, 2001-vol.33, no.1, 2009
• Personal Alert (E-mail)
• Education Collection, 7/1/2003-
• Education Database, 7/1/2003-
• Health Research Premium Collection, 7/1/2003-
• Hospital Premium Collection, 7/1/2003-
• Periodicals Index Online, 1/1/1981-7/1/2000
• Professional ProQuest Central, 07/01/2003-
• ProQuest 5000, 07/01/2003-
• ProQuest 5000 International, 07/01/2003-
• ProQuest Central, 07/01/2003-
• Psychology Database, 7/1/2003-
• Research Library, 07/01/2003-
• Social Science Premium Collection, 07/01/2003-
• Educational Research Abstracts Online
• Research into Higher Education Abstracts (Online)
• Studies on Women and Gender Abstracts (Online)

Abstracting & Indexing Sources

• Contents Pages in Education   (Ceased)  (Print)
• Family Index   (Ceased)  (Print)
• Psychological Abstracts   (Ceased)  (Print)

Source: Ulrichsweb Global Serials Directory.

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Also of Interest

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• Research article
• Open access
• Published: 24 April 2023

Artificial intelligence in higher education: the state of the field

• Helen Crompton   ORCID: orcid.org/0000-0002-1775-8219 1 , 3 &
• Diane Burke 2  

International Journal of Educational Technology in Higher Education volume  20 , Article number:  22 ( 2023 ) Cite this article

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This systematic review provides unique findings with an up-to-date examination of artificial intelligence (AI) in higher education (HE) from 2016 to 2022. Using PRISMA principles and protocol, 138 articles were identified for a full examination. Using a priori, and grounded coding, the data from the 138 articles were extracted, analyzed, and coded. The findings of this study show that in 2021 and 2022, publications rose nearly two to three times the number of previous years. With this rapid rise in the number of AIEd HE publications, new trends have emerged. The findings show that research was conducted in six of the seven continents of the world. The trend has shifted from the US to China leading in the number of publications. Another new trend is in the researcher affiliation as prior studies showed a lack of researchers from departments of education. This has now changed to be the most dominant department. Undergraduate students were the most studied students at 72%. Similar to the findings of other studies, language learning was the most common subject domain. This included writing, reading, and vocabulary acquisition. In examination of who the AIEd was intended for 72% of the studies focused on students, 17% instructors, and 11% managers. In answering the overarching question of how AIEd was used in HE, grounded coding was used. Five usage codes emerged from the data: (1) Assessment/Evaluation, (2) Predicting, (3) AI Assistant, (4) Intelligent Tutoring System (ITS), and (5) Managing Student Learning. This systematic review revealed gaps in the literature to be used as a springboard for future researchers, including new tools, such as Chat GPT.

A systematic review examining AIEd in higher education (HE) up to the end of 2022.

Unique findings in the switch from US to China in the most studies published.

A two to threefold increase in studies published in 2021 and 2022 to prior years.

AIEd was used for: Assessment/Evaluation, Predicting, AI Assistant, Intelligent Tutoring System, and Managing Student Learning.

Introduction

The use of artificial intelligence (AI) in higher education (HE) has risen quickly in the last 5 years (Chu et al., 2022 ), with a concomitant proliferation of new AI tools available. Scholars (viz., Chen et al., 2020 ; Crompton et al., 2020 , 2021 ) report on the affordances of AI to both instructors and students in HE. These benefits include the use of AI in HE to adapt instruction to the needs of different types of learners (Verdú et al., 2017 ), in providing customized prompt feedback (Dever et al., 2020 ), in developing assessments (Baykasoğlu et al., 2018 ), and predict academic success (Çağataylı & Çelebi, 2022 ). These studies help to inform educators about how artificial intelligence in education (AIEd) can be used in higher education.

Nonetheless, a gap has been highlighted by scholars (viz., Hrastinski et al., 2019 ; Zawacki-Richter et al., 2019 ) regarding an understanding of the collective affordances provided through the use of AI in HE. Therefore, the purpose of this study is to examine extant research from 2016 to 2022 to provide an up-to-date systematic review of how AI is being used in the HE context.

Artificial intelligence has become pervasive in the lives of twenty-first century citizens and is being proclaimed as a tool that can be used to enhance and advance all sectors of our lives (Górriz et al., 2020 ). The application of AI has attracted great interest in HE which is highly influenced by the development of information and communication technologies (Alajmi et al., 2020 ). AI is a tool used across subject disciplines, including language education (Liang et al., 2021 ), engineering education (Shukla et al., 2019 ), mathematics education (Hwang & Tu, 2021 ) and medical education (Winkler-Schwartz et al., 2019 ),

Artificial intelligence

The term artificial intelligence is not new. It was coined in 1956 by McCarthy (Cristianini, 2016 ) who followed up on the work of Turing (e.g., Turing, 1937 , 1950 ). Turing described the existence of intelligent reasoning and thinking that could go into intelligent machines. The definition of AI has grown and changed since 1956, as there has been significant advancements in AI capabilities. A current definition of AI is “computing systems that are able to engage in human-like processes such as learning, adapting, synthesizing, self-correction and the use of data for complex processing tasks” (Popenici et al., 2017 , p. 2). The interdisciplinary interest from scholars from linguistics, psychology, education, and neuroscience who connect AI to nomenclature, perceptions and knowledge in their own disciplines could create a challenge when defining AI. This has created the need to create categories of AI within specific disciplinary areas. This paper focuses on the category of AI in Education (AIEd) and how AI is specifically used in higher educational contexts.

As the field of AIEd is growing and changing rapidly, there is a need to increase the academic understanding of AIEd. Scholars (viz., Hrastinski et al., 2019 ; Zawacki-Richter et al., 2019 ) have drawn attention to the need to increase the understanding of the power of AIEd in educational contexts. The following section provides a summary of the previous research regarding AIEd.

Extant systematic reviews

This growing interest in AIEd has led scholars to investigate the research on the use of artificial intelligence in education. Some scholars have conducted systematic reviews to focus on a specific subject domain. For example, Liang et. al. ( 2021 ) conducted a systematic review and bibliographic analysis the roles and research foci of AI in language education. Shukla et. al. ( 2019 ) focused their longitudinal bibliometric analysis on 30 years of using AI in Engineering. Hwang and Tu ( 2021 ) conducted a bibliometric mapping analysis on the roles and trends in the use of AI in mathematics education, and Winkler-Schwartz et. al. ( 2019 ) specifically examined the use of AI in medical education in looking for best practices in the use of machine learning to assess surgical expertise. These studies provide a specific focus on the use of AIEd in HE but do not provide an understanding of AI across HE.

On a broader view of AIEd in HE, Ouyang et. al. ( 2022 ) conducted a systematic review of AIEd in online higher education and investigated the literature regarding the use of AI from 2011 to 2020. The findings show that performance prediction, resource recommendation, automatic assessment, and improvement of learning experiences are the four main functions of AI applications in online higher education. Salas-Pilco and Yang ( 2022 ) focused on AI applications in Latin American higher education. The results revealed that the main AI applications in higher education in Latin America are: (1) predictive modeling, (2) intelligent analytics, (3) assistive technology, (4) automatic content analysis, and (5) image analytics. These studies provide valuable information for the online and Latin American context but not an overarching examination of AIEd in HE.

Studies have been conducted to examine HE. Hinojo-Lucena et. al. ( 2019 ) conducted a bibliometric study on the impact of AIEd in HE. They analyzed the scientific production of AIEd HE publications indexed in Web of Science and Scopus databases from 2007 to 2017. This study revealed that most of the published document types were proceedings papers. The United States had the highest number of publications, and the most cited articles were about implementing virtual tutoring to improve learning. Chu et. al. ( 2022 ) reviewed the top 50 most cited articles on AI in HE from 1996 to 2020, revealing that predictions of students’ learning status were most frequently discussed. AI technology was most frequently applied in engineering courses, and AI technologies most often had a role in profiling and prediction. Finally, Zawacki-Richter et. al. ( 2019 ) analyzed AIEd in HE from 2007 to 2018 to reveal four primary uses of AIEd: (1) profiling and prediction, (2) assessment and evaluation, (3) adaptive systems and personalization, and (4) intelligent tutoring systems. There do not appear to be any studies examining the last 2 years of AIEd in HE, and these authors describe the rapid speed of both AI development and the use of AIEd in HE and call for further research in this area.

Purpose of the study

The purpose of this study is in response to the appeal from scholars (viz., Chu et al., 2022 ; Hinojo-Lucena et al., 2019 ; Zawacki-Richter et al., 2019 ) to research to investigate the benefits and challenges of AIEd within HE settings. As the academic knowledge of AIEd HE finished with studies examining up to 2020, this study provides the most up-to-date analysis examining research through to the end of 2022.

The overarching question for this study is: what are the trends in HE research regarding the use of AIEd? The first two questions provide contextual information, such as where the studies occurred and the disciplines AI was used in. These contextual details are important for presenting the main findings of the third question of how AI is being used in HE.

In what geographical location was the AIEd research conducted, and how has the trend in the number of publications evolved across the years?

What departments were the first authors affiliated with, and what were the academic levels and subject domains in which AIEd research was being conducted?

Who are the intended users of the AI technologies and what are the applications of AI in higher education?

A PRISMA systematic review methodology was used to answer three questions guiding this study. PRISMA principles (Page et al., 2021 ) were used throughout the study. The PRISMA extension Preferred Reporting Items for Systematic Reviews and Meta-Analysis for Protocols (PRISMA-P; Moher et al., 2015 ) were utilized in this study to provide an a priori roadmap to conduct a rigorous systematic review. Furthermore, the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA principles; Page et al., 2021 ) were used to search, identify, and select articles to be included in the research were used for searching, identifying, and selecting articles, then in how to read, extract, and manage the secondary data gathered from those studies (Moher et al., 2015 , PRISMA Statement, 2021 ). This systematic review approach supports an unbiased synthesis of the data in an impartial way (Hemingway & Brereton, 2009 ). Within the systematic review methodology, extracted data were aggregated and presented as whole numbers and percentages. A qualitative deductive and inductive coding methodology was also used to analyze extant data and generate new theories on the use of AI in HE (Gough et al., 2017 ).

The research begins with the search for the research articles to be included in the study. Based on the research question, the study parameters are defined including the search years, quality and types of publications to be included. Next, databases and journals are selected. A Boolean search is created and used for the search of those databases and journals. Once a set of publications are located from those searches, they are then examined against an inclusion and exclusion criteria to determine which studies will be included in the final study. The relevant data to match the research questions is then extracted from the final set of studies and coded. This method section is organized to describe each of these methods with full details to ensure transparency.

Search strategy

Only peer-reviewed journal articles were selected for examination in this systematic review. This ensured a level of confidence in the quality of the studies selected (Gough et al., 2017 ). The search parameters narrowed the search focus to include studies published in 2016 to 2022. This timeframe was selected to ensure the research was up to date, which is especially important with the rapid change in technology and AIEd.

The data retrieval protocol employed an electronic and a hand search. The electronic search included educational databases within EBSCOhost. Then an additional electronic search was conducted of Wiley Online Library, JSTOR, Science Direct, and Web of Science. Within each of these databases a full text search was conducted. Aligned to the research topic and questions, the Boolean search included terms related to AI, higher education, and learning. The Boolean search is listed in Table 1 . In the initial test search, the terms “machine learning” OR “intelligent support” OR “intelligent virtual reality” OR “chatbot” OR “automated tutor” OR “intelligent agent” OR “expert system” OR “neural network” OR “natural language processing” were used. These were removed as they were subcategories of terms found in Part 1 of the search. Furthermore, inclusion of these specific AI terms resulted in a large number of computer science courses that were focused on learning about AI and not the use of AI in learning.

Part 2 of the search ensured that articles involved formal university education. The terms higher education and tertiary were both used to recognize the different terms used in different countries. The final Boolean search was “Artificial intelligence” OR AI OR “smart technologies” OR “intelligent technologies” AND “higher education” OR tertiary OR graduate OR undergraduate. Scholars (viz., Ouyang et al., 2022 ) who conducted a systematic review on AIEd in HE up to 2020 noted that they missed relevant articles from their study, and other relevant journals should intentionally be examined. Therefore, a hand search was also conducted to include an examination of other journals relevant to AIEd that may not be included in the databases. This is important as the field of AIEd is still relatively new, and journals focused on this field may not yet be indexed in databases. The hand search included: The International Journal of Learning Analytics and Artificial Intelligence in Education, the International Journal of Artificial Intelligence in Education, and Computers & Education: Artificial Intelligence.

Electronic and hand searches resulted in 371 articles for possible inclusion. The search parameters within the electronic database search narrowed the search to articles published from 2016 to 2022, per-reviewed journal articles, and duplicates. Further screening was conducted manually, as each of the 138 articles were reviewed in full by two researchers to examine a match against the inclusion and exclusion criteria found in Table 2 .

The inter-rater reliability was calculated by percentage agreement (Belur et al., 2018 ). The researchers reached a 95% agreement for the coding. Further discussion of misaligned articles resulted in a 100% agreement. This screening process against inclusion and exclusion criteria resulted in the exclusion of 237 articles. This included the duplicates and those removed as part of the inclusion and exclusion criteria, see Fig.  1 . Leaving 138 articles for inclusion in this systematic review.

(From: Page et al., 2021 )

PRISMA flow chart of article identification and screening

The 138 articles were then coded to answer each of the research questions using deductive and inductive coding methods. Deductive coding involves examining data using a priori codes. A priori are pre-determined criteria and this process was used to code the countries, years, author affiliations, academic levels, and domains in the respective groups. Author affiliations were coded using the academic department of the first author of the study. First authors were chosen as that person is the primary researcher of the study and this follows past research practice (e.g., Zawacki-Richter et al., 2019 ). Who the AI was intended for was also coded using the a priori codes of Student, Instructor, Manager or Others. The Manager code was used for those who are involved in organizational tasks, e.g., tracking enrollment. Others was used for those not fitting the other three categories.

Inductive coding was used for the overarching question of this study in examining how the AI was being used in HE. Researchers of extant systematic reviews on AIEd in HE (viz., Chu et al., 2022 ; Zawacki-Richter et al., 2019 ) often used an a priori framework as researchers matched the use of AI to pre-existing frameworks. A grounded coding methodology (Strauss & Corbin, 1995 ) was selected for this study to allow findings of the trends on AIEd in HE to emerge from the data. This is important as it allows a direct understanding of how AI is being used rather than how researchers may think it is being used and fitting the data to pre-existing ideas.

Grounded coding process involved extracting how the AI was being used in HE from the articles. “In vivo” (Saldana, 2015 ) coding was also used alongside grounded coding. In vivo codes are when codes use language directly from the article to capture the primary authors’ language and ensure consistency with their findings. The grounded coding design used a constant comparative method. Researchers identified important text from articles related to the use of AI, and through an iterative process, initial codes led to axial codes with a constant comparison of uses of AI with uses of AI, then of uses of AI with codes, and codes with codes. Codes were deemed theoretically saturated when the majority of the data fit with one of the codes. For both the a priori and the grounded coding, two researchers coded and reached an inter-rater percentage agreement of 96%. After discussing misaligned articles, a 100% agreement was achieved.

Findings and discussion

The findings and discussion section are organized by the three questions guiding this study. The first two questions provide contextual information on the AIEd research, and the final question provides a rigorous investigation into how AI is being used in HE.

RQ1. In what geographical location was the AIEd research conducted, and how has the trend in the number of publications evolved across the years?

The 138 studies took place across 31 countries in six of seven continents of the world. Nonetheless, that distribution was not equal across continents. Asia had the largest number of AIEd studies in HE at 41%. Of the seven countries represented in Asia, 42 of the 58 studies were conducted in Taiwan and China. Europe, at 30%, was the second largest continent and had 15 countries ranging from one to eight studies a piece. North America, at 21% of the studies was the continent with the third largest number of studies, with the USA producing 21 of the 29 studies in that continent. The 21 studies from the USA places it second behind China. Only 1% of studies were conducted in South America and 2% in Africa. See Fig.  2 for a visual representation of study distribution across countries. Those continents with high numbers of studies are from high income countries and those with low numbers have a paucity of publications in low-income countries.

Geographical distribution of the AIEd HE studies

Data from Zawacki-Richter et. al.’s ( 2019 ) 2007–2018 systematic review examining countries found that the USA conducted the most studies across the globe at 43 out of 146, and China had the second largest at eleven of the 146 papers. Researchers have noted a rapid trend in Chinese researchers publishing more papers on AI and securing more patents than their US counterparts in a field that was originally led by the US (viz., Li et al., 2021 ). The data from this study corroborate this trend in China leading in the number of AIEd publications.

With the accelerated use of AI in society, gathering data to examine the use of AIEd in HE is useful in providing the scholarly community with specific information on that growth and if it is as prolific as anticipated by scholars (e.g., Chu et al., 2022 ). The analysis of data of the 138 studies shows that the trend towards the use of AIEd in HE has greatly increased. There is a drop in 2019, but then a great rise in 2021 and 2022; see Fig.  3 .

Chronological trend in AIEd in HE

Data on the rise in AIEd in HE is similar to the findings of Chu et. al. ( 2022 ) who noted an increase from 1996 to 2010 and 2011–2020. Nonetheless Chu’s parameters are across decades, and the rise is to be anticipated with a relatively new technology across a longitudinal review. Data from this study show a dramatic rise since 2020 with a 150% increase from the prior 2 years 2020–2019. The rise in 2021 and 2022 in HE could have been caused by the vast increase in HE faculty having to teach with technology during the pandemic lockdown. Faculty worldwide were using technologies, including AI, to explore how they could continue teaching and learning that was often face-to-face prior to lockdown. The disadvantage of this rapid adoption of technology is that there was little time to explore the possibilities of AI to transform learning, and AI may have been used to replicate past teaching practices, without considering new strategies previously inconceivable with the affordances of AI.

However, in a further examination of the research from 2021 to 2022, it appears that there are new strategies being considered. For example, Liu et. al.’s, 2022 study used AIEd to provide information on students’ interactions in an online environment and examine their cognitive effort. In Yao’s study in 2022, he examined the use of AI to determine student emotions while learning.

RQ2. What departments were the first authors affiliated with, and what were the academic levels and subject domains in which AIEd research was being conducted?

Department affiliations

Data from the AIEd HE studies show that of the first authors were most frequently from colleges of education (28%), followed by computer science (20%). Figure  4 presents the 15 academic affiliations of the authors found in the studies. The wide variety of affiliations demonstrate the variety of ways AI can be used in various educational disciplines, and how faculty in diverse areas, including tourism, music, and public affairs were interested in how AI can be used for educational purposes.

Research affiliations

In an extant AIED HE systematic review, Zawacki-Richter et. al.’s ( 2019 ) named their study Systematic review of research on artificial intelligence applications in higher education—where are the educators? In this study, the authors were keen to highlight that of the AIEd studies in HE, only six percent were written by researchers directly connected to the field of education, (i.e., from a college of education). The researchers found a great lack in pedagogical and ethical implications of implementing AI in HE and that there was a need for more educational perspectives on AI developments from educators conducting this work. It appears from our data that educators are now showing greater interest in leading these research endeavors, with the highest affiliated group belonging to education. This may again be due to the pandemic and those in the field of education needing to support faculty in other disciplines, and/or that they themselves needed to explore technologies for their own teaching during the lockdown. This may also be due to uptake in professors in education becoming familiar with AI tools also driven by a societal increased attention. As the focus of much research by education faculty is on teaching and learning, they are in an important position to be able to share their research with faculty in other disciplines regarding the potential affordances of AIEd.

Academic levels

The a priori coding of academic levels show that the majority of studies involved undergraduate students with 99 of the 138 (72%) focused on these students. This was in comparison to the 12 of 138 (9%) for graduate students. Some of the studies used AI for both academic levels: see Fig.  5

Academic level distribution by number of articles

This high percentage of studies focused on the undergraduate population was congruent with an earlier AIED HE systematic review (viz., Zawacki-Richter et al., 2019 ) who also reported student academic levels. This focus on undergraduate students may be due to the variety of affordances offered by AIEd, such as predictive analytics on dropouts and academic performance. These uses of AI may be less required for graduate students who already have a record of performance from their undergraduate years. Another reason for this demographic focus can also be convenience sampling, as researchers in HE typically has a much larger and accessible undergraduate population than graduates. This disparity between undergraduates and graduate populations is a concern, as AIEd has the potential to be valuable in both settings.

Subject domains

The studies were coded into 14 areas in HE; with 13 in a subject domain and one category of AIEd used in HE management of students; See Fig.  6 . There is not a wide difference in the percentages of top subject domains, with language learning at 17%, computer science at 16%, and engineering at 12%. The management of students category appeared third on the list at 14%. Prior studies have also found AIEd often used for language learning (viz., Crompton et al., 2021 ; Zawacki-Richter et al., 2019 ). These results are different, however, from Chu et. al.’s ( 2022 ) findings that show engineering dramatically leading with 20 of the 50 studies, with other subjects, such as language learning, appearing once or twice. This study appears to be an outlier that while the searches were conducted in similar databases, the studies only included 50 studies from 1996 to 2020.

Subject domains of AIEd in HE

Previous scholars primarily focusing on language learning using AI for writing, reading, and vocabulary acquisition used the affordances of natural language processing and intelligent tutoring systems (e.g., Liang et al., 2021 ). This is similar to the findings in studies with AI used for automated feedback of writing in a foreign language (Ayse et al., 2022 ), and AI translation support (Al-Tuwayrish, 2016 ). The large use of AI for managerial activities in this systematic review focused on making predictions (12 studies) and then admissions (three studies). This is positive to see this use of AI to look across multiple databases to see trends emerging from data that may not have been anticipated and cross referenced before (Crompton et al., 2022 ). For example, to examine dropouts, researchers may consider examining class attendance, and may not examine other factors that appear unrelated. AI analysis can examine all factors and may find that dropping out is due to factors beyond class attendance.

RQ3. Who are the intended users of the AI technologies and what are the applications of AI in higher education?

Intended user of AI

Of the 138 articles, the a priori coding shows that 72% of the studies focused on Students, followed by a focus on Instructors at 17%, and Managers at 11%, see Fig.  7 . The studies provided examples of AI being used to provide support to students, such as access to learning materials for inclusive learning (Gupta & Chen, 2022 ), provide immediate answers to student questions, self-testing opportunities (Yao, 2022 ), and instant personalized feedback (Mousavi et al., 2020 ).

Intended user

The data revealed a large emphasis on students in the use of AIEd in HE. This user focus is different from a recent systematic review on AIEd in K-12 that found that AIEd studies in K-12 settings prioritized teachers (Crompton et al., 2022 ). This may appear that HE uses AI to focus more on students than in K-12. However, this large number of student studies in HE may be due to the student population being more easily accessibility to HE researchers who may study their own students. The ethical review process is also typically much shorter in HE than in K-12. Therefore, the data on the intended focus should be reviewed while keeping in mind these other explanations. It was interesting that Managers were the lowest focus in K-12 and also in this study in HE. AI has great potential to collect, cross reference and examine data across large datasets that can allow data to be used for actionable insight. More focus on the use of AI by managers would tap into this potential.

How is AI used in HE

Using grounded coding, the use of AIEd from each of the 138 articles was examined and six major codes emerged from the data. These codes provide insight into how AI was used in HE. The five codes are: (1) Assessment/Evaluation, (2) Predicting, (3) AI Assistant, (4) Intelligent Tutoring System (ITS), and (5) Managing Student Learning. For each of these codes there are also axial codes, which are secondary codes as subcategories from the main category. Each code is delineated below with a figure of the codes with further descriptive information and examples.

Assessment/evaluation

Assessment and Evaluation was the most common use of AIEd in HE. Within this code there were six axial codes broken down into further codes; see Fig.  8 . Automatic assessment was most common, seen in 26 of the studies. It was interesting to see that this involved assessment of academic achievement, but also other factors, such as affect.

Codes and axial codes for assessment and evaluation

Automatic assessment was used to support a variety of learners in HE. As well as reducing the time it takes for instructors to grade (Rutner & Scott, 2022 ), automatic grading showed positive use for a variety of students with diverse needs. For example, Zhang and Xu ( 2022 ) used automatic assessment to improve academic writing skills of Uyghur ethnic minority students living in China. Writing has a variety of cultural nuances and in this study the students were shown to engage with the automatic assessment system behaviorally, cognitively, and affectively. This allowed the students to engage in self-regulated learning while improving their writing.

Feedback was a description often used in the studies, as students were given text and/or images as feedback as a formative evaluation. Mousavi et. al. ( 2020 ) developed a system to provide first year biology students with an automated personalized feedback system tailored to the students’ specific demographics, attributes, and academic status. With the unique feature of AIEd being able to analyze multiple data sets involving a variety of different students, AI was used to assess and provide feedback on students’ group work (viz., Ouatik et al., 2021 ).

AI also supports instructors in generating questions and creating multiple question tests (Yang et al., 2021 ). For example, (Lu et al., 2021 ) used natural language processing to create a system that automatically created tests. Following a Turing type test, researchers found that AI technologies can generate highly realistic short-answer questions. The ability for AI to develop multiple questions is a highly valuable affordance as tests can take a great deal of time to make. However, it would be important for instructors to always confirm questions provided by the AI to ensure they are correct and that they match the learning objectives for the class, especially in high value summative assessments.

The axial code within assessment and evaluation revealed that AI was used to review activities in the online space. This included evaluating student’s reflections, achievement goals, community identity, and higher order thinking (viz., Huang et al., 2021 ). Three studies used AIEd to evaluate educational materials. This included general resources and textbooks (viz., Koć‑Januchta et al., 2022 ). It is interesting to see the use of AI for the assessment of educational products, rather than educational artifacts developed by students. While this process may be very similar in nature, this shows researchers thinking beyond the traditional use of AI for assessment to provide other affordances.

Predicting was a common use of AIEd in HE with 21 studies focused specifically on the use of AI for forecasting trends in data. Ten axial codes emerged on the way AI was used to predict different topics, with nine focused on predictions regarding students and the other on predicting the future of higher education. See Fig.  9 .

Predicting axial codes

Extant systematic reviews on HE highlighted the use of AIEd for prediction (viz., Chu et al., 2022 ; Hinojo-Lucena et al., 2019 ; Ouyang et al., 2022 ; Zawacki-Richter et al., 2019 ). Ten of the articles in this study used AI for predicting academic performance. Many of the axial codes were often overlapping, such as predicting at risk students, and predicting dropouts; however, each provided distinct affordances. An example of this is the study by Qian et. al. ( 2021 ). These researchers examined students taking a MOOC course. MOOCs can be challenging environments to determine information on individual students with the vast number of students taking the course (Krause & Lowe, 2014 ). However, Qian et al., used AIEd to predict students’ future grades by inputting 17 different learning features, including past grades, into an artificial neural network. The findings were able to predict students’ grades and highlight students at risk of dropping out of the course.

In a systematic review on AIEd within the K-12 context (viz., Crompton et al., 2022 ), prediction was less pronounced in the findings. In the K-12 setting, there was a brief mention of the use of AI in predicting student academic performance. One of the studies mentioned students at risk of dropping out, but this was immediately followed by questions about privacy concerns and describing this as “sensitive”. The use of prediction from the data in this HE systematic review cover a wide range of AI predictive affordances. students Sensitivity is still important in a HE setting, but it is positive to see the valuable insight it provides that can be used to avoid students failing in their goals.

AI assistant

The studies evaluated in this review indicated that the AI Assistant used to support learners had a variety of different names. This code included nomenclature such as, virtual assistant, virtual agent, intelligent agent, intelligent tutor, and intelligent helper. Crompton et. al. ( 2022 ), described the difference in the terms to delineate the way that the AI appeared to the user. For example, if there was an anthropomorphic presence to the AI, such as an avatar, or if the AI appeared to support via other means, such as text prompt. The findings of this systematic review align to Crompton et. al.’s ( 2022 ) descriptive differences of the AI Assistant. Furthermore, this code included studies that provide assistance to students, but may not have specifically used the word assistance. These include the use of chatbots for student outreach, answering questions, and providing other assistance. See Fig.  10 for the axial codes for AI Assistant.

AI assistant axial codes

Many of these assistants offered multiple supports to students, such as Alex , the AI described as a virtual change agent in Kim and Bennekin’s ( 2016 ) study. Alex interacted with students in a college mathematics course by asking diagnostic questions and gave support depending on student needs. Alex’s support was organized into four stages: (1) goal initiation (“Want it”), (2) goal formation (“Plan for it”), (3) action control (“Do it”), and (4) emotion control (“Finish it”). Alex provided responses depending on which of these four areas students needed help. These messages supported students with the aim of encouraging persistence in pursuing their studies and degree programs and improving performance.

The role of AI in providing assistance connects back to the seminal work of Vygotsky ( 1978 ) and the Zone of Proximal Development (ZPD). ZPD highlights the degree to which students can rapidly develop when assisted. Vygotsky described this assistance often in the form of a person. However, with technological advancements, the use of AI assistants in these studies are providing that support for students. The affordances of AI can also ensure that the support is timely without waiting for a person to be available. Also, assistance can consider aspects on students’ academic ability, preferences, and best strategies for supporting. These features were evident in Kim and Bennekin’s ( 2016 ) study using Alex.

Intelligent tutoring system

The use of Intelligent Tutoring Systems (ITS) was revealed in the grounded coding. ITS systems are adaptive instructional systems that involve the use of AI techniques and educational methods. An ITS system customizes educational activities and strategies based on student’s characteristics and needs (Mousavinasab et al., 2021 ). While ITS may be an anticipated finding in AIED HE systematic reviews, it was interesting that extant reviews similar to this study did not always describe their use in HE. For example, Ouyang et. al. ( 2022 ), included “intelligent tutoring system” in search terms describing it as a common technique, yet ITS was not mentioned again in the paper. Zawacki-Richter et. al. ( 2019 ) on the other hand noted that ITS was in the four overarching findings of the use of AIEd in HE. Chu et. al. ( 2022 ) then used Zawacki-Richter’s four uses of AIEd for their recent systematic review.

In this systematic review, 18 studies specifically mentioned that they were using an ITS. The ITS code did not necessitate axial codes as they were performing the same type of function in HE, namely, in providing adaptive instruction to the students. For example, de Chiusole et. al. ( 2020 ) developed Stat-Knowlab, an ITS that provides the level of competence and best learning path for each student. Thus Stat-Knowlab personalizes students’ learning and provides only educational activities that the student is ready to learn. This ITS is able to monitor the evolution of the learning process as the student interacts with the system. In another study, Khalfallah and Slama ( 2018 ) built an ITS called LabTutor for engineering students. LabTutor served as an experienced instructor in enabling students to access and perform experiments on laboratory equipment while adapting to the profile of each student.

The student population in university classes can go into the hundreds and with the advent of MOOCS, class sizes can even go into the thousands. Even in small classes of 20 students, the instructor cannot physically provide immediate unique personalize questions to each student. Instructors need time to read and check answers and then take further time to provide feedback before determining what the next question should be. Working with the instructor, AIEd can provide that immediate instruction, guidance, feedback, and following questioning without delay or becoming tired. This appears to be an effective use of AIEd, especially within the HE context.

Managing student learning

Another code that emerged in the grounded coding was focused on the use of AI for managing student learning. AI is accessed to manage student learning by the administrator or instructor to provide information, organization, and data analysis. The axial codes reveal the trends in the use of AI in managing student learning; see Fig.  11 .

Learning analytics was an a priori term often found in studies which describes “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs” (Long & Siemens, 2011 , p. 34). The studies investigated in this systematic review were across grades and subject areas and provided administrators and instructors different types of information to guide their work. One of those studies was conducted by Mavrikis et. al. ( 2019 ) who described learning analytics as teacher assistance tools. In their study, learning analytics were used in an exploratory learning environment with targeted visualizations supporting classroom orchestration. These visualizations, displayed as screenshots in the study, provided information such as the interactions between the students, goals achievements etc. These appear similar to infographics that are brightly colored and draw the eye quickly to pertinent information. AI is also used for other tasks, such as organizing the sequence of curriculum in pacing guides for future groups of students and also designing instruction. Zhang ( 2022 ) described how designing an AI teaching system of talent cultivation and using the digital affordances to establish a quality assurance system for practical teaching, provides new mechanisms for the design of university education systems. In developing such a system, Zhang found that the stability of the instructional design, overcame the drawbacks of traditional manual subjectivity in the instructional design.

Another trend that emerged from the studies was the use of AI to manage student big data to support learning. Ullah and Hafiz ( 2022 ) lament that using traditional methods, including non-AI digital techniques, asking the instructor to pay attention to every student’s learning progress is very difficult and that big data analysis techniques are needed. The ability to look across and within large data sets to inform instruction is a valuable affordance of AIEd in HE. While the use of AIEd to manage student learning emerged from the data, this study uncovered only 19 studies in 7 years (2016–2022) that focused on the use of AIEd to manage student data. This lack of the use was also noted in a recent study in the K-12 space (Crompton et al., 2022 ). In Chu et. al.’s ( 2022 ) study examining the top 50 most cited AIEd articles, they did not report the use of AIEd for managing student data in the top uses of AIEd HE. It would appear that more research should be conducted in this area to fully explore the possibilities of AI.

Gaps and future research

From this systematic review, six gaps emerged in the data providing opportunities for future studies to investigate and provide a fuller understanding of how AIEd can used in HE. (1) The majority of the research was conducted in high income countries revealing a paucity of research in developing countries. More research should be conducted in these developing countries to expand the level of understanding about how AI can enhance learning in under-resourced communities. (2) Almost 50% of the studies were conducted in the areas of language learning, computer science and engineering. Research conducted by members from multiple, different academic departments would help to advance the knowledge of the use of AI in more disciplines. (3) This study revealed that faculty affiliated with schools of education are taking an increasing role in researching the use of AIEd in HE. As this body of knowledge grows, faculty in Schools of Education should share their research regarding the pedagogical affordances of AI so that this knowledge can be applied by faculty across disciplines. (4) The vast majority of the research was conducted at the undergraduate level. More research needs to be done at the graduate student level, as AI provides many opportunities in this environment. (5) Little study was done regarding how AIEd can assist both instructors and managers in their roles in HE. The power of AI to assist both groups further research. (6) Finally, much of the research investigated in this systematic review revealed the use of AIEd in traditional ways that enhance or make more efficient current practices. More research needs to focus on the unexplored affordances of AIEd. As AI becomes more advanced and sophisticated, new opportunities will arise for AIEd. Researchers need to be on the forefront of these possible innovations.

In addition, empirical exploration is needed for new tools, such as ChatGPT that was available for public use at the end of 2022. With the time it takes for a peer review journal article to be published, ChatGPT did not appear in the articles for this study. What is interesting is that it could fit with a variety of the use codes found in this study, with students getting support in writing papers and instructors using Chat GPT to assess students work and with help writing emails or descriptions for students. It would be pertinent for researchers to explore Chat GPT.

Limitations

The findings of this study show a rapid increase in the number of AIEd studies published in HE. However, to ensure a level of credibility, this study only included peer review journal articles. These articles take months to publish. Therefore, conference proceedings and gray literature such as blogs and summaries may reveal further findings not explored in this study. In addition, the articles in this study were all published in English which excluded findings from research published in other languages.

In response to the call by Hinojo-Lucena et. al. ( 2019 ), Chu et. al. ( 2022 ), and Zawacki-Richter et. al. ( 2019 ), this study provides unique findings with an up-to-date examination of the use of AIEd in HE from 2016 to 2022. Past systematic reviews examined the research up to 2020. The findings of this study show that in 2021 and 2022, publications rose nearly two to three times the number of previous years. With this rapid rise in the number of AIEd HE publications, new trends have emerged.

The findings show that of the 138 studies examined, research was conducted in six of the seven continents of the world. In extant systematic reviews showed that the US led by a large margin in the number of studies published. This trend has now shifted to China. Another shift in AIEd HE is that while extant studies lamented the lack of focus on professors of education leading these studies, this systematic review found education to be the most common department affiliation with 28% and computer science coming in second at 20%. Undergraduate students were the most studied students at 72%. Similar to the findings of other studies, language learning was the most common subject domain. This included writing, reading, and vocabulary acquisition. In examination of who the AIEd was intended for, 72% of the studies focused on students, 17% instructors, and 11% managers.

Grounded coding was used to answer the overarching question of how AIEd was used in HE. Five usage codes emerged from the data: (1) Assessment/Evaluation, (2) Predicting, (3) AI Assistant, (4) Intelligent Tutoring System (ITS), and (5) Managing Student Learning. Assessment and evaluation had a wide variety of purposes, including assessing academic progress and student emotions towards learning, individual and group evaluations, and class based online community assessments. Predicting emerged as a code with ten axial codes, as AIEd predicted dropouts and at-risk students, innovative ability, and career decisions. AI Assistants were specific to supporting students in HE. These assistants included those with an anthropomorphic presence, such as virtual agents and persuasive intervention through digital programs. ITS systems were not always noted in extant systematic reviews but were specifically mentioned in 18 of the studies in this review. ITS systems in this study provided customized strategies and approaches to student’s characteristics and needs. The final code in this study highlighted the use of AI in managing student learning, including learning analytics, curriculum sequencing, instructional design, and clustering of students.

The findings of this study provide a springboard for future academics, practitioners, computer scientists, policymakers, and funders in understanding the state of the field in AIEd HE, how AI is used. It also provides actionable items to ameliorate gaps in the current understanding. As the use AIEd will only continue to grow this study can serve as a baseline for further research studies in the use of AIEd in HE.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Under the surface of teacher occupational wellness and effectiveness in higher education: a look into the mediator roles of work passion and emotion regulation via SEM analysis

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Teacher occupational wellness and effectiveness are crucial aspects of a teacher's capacity to contribute to the advancement of excellence in education. Nevertheless, there is a dearth of considerable studies regarding the interconnections between work passion and emotion regulation in higher education. This study developed a model to demonstrate the interplay between the above-mentioned constructs to fill this research gap. To gather this information, the required scales were sent to 401 different university professors. Based on the findings of Structural Equation Modelling (SEM) and Confirmatory Factor Analysis (CFA), it is suggested that work passion and emotion regulation have the potential to enhance teacher occupational wellness and effectiveness in higher education. In the end, implications and directions for the future were presented to educators and researchers who are enthused about the potential of work passion, emotion regulation, and self-compassion for improving instructive practices.

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Introduction

Educating in higher education is intrinsically complex due to the multitude of external and internal variables involved. The outcome is contingent upon the self-perception of language teachers and the instructional techniques they use with their students. Typically, educators devise many teaching methods, influenced by the interplay of their individual and institutional dimensions [ 1 , 2 ]. Educators have a crucial function in the educational system. The significance of university teachers in molding the student intellects should never be undervalued. Furthermore, it is evident that some professors possess a greater ability to exert influence on students compared to their peers.

In the course of this inquiry with educators, the first idea that is taken into consideration is work passion (WP). The notion of WP has garnered increased attention in the twenty-first century, as evidenced by the proliferation of research that highlights its advantageous consequences and how organizations can profit from employing an impassioned staff [ 3 ]. WP is a disposition toward action or endeavor that individuals esteem highly, find enjoyable, and devote a substantial amount of time and effort to [ 4 ]. Passion increases well-being, motivates individuals, and imparts significance to their existence. WP among university teachers may also lead to teacher effectiveness (TE). The efficacy of education is contingent upon the efficacy of instructors [ 5 ]. In such a case, it becomes essential to conceptualize the meaning of TE. The National Comprehensive Centre for Teacher Quality has developed a concise definition of TE, consisting of five characteristics [ 6 ]. Firstly, an effective teacher sets high expectations for all students and supports them in achieving their goals. Secondly, an effective teacher positively influences students' academic, social, and attitudinal outcomes. Thirdly, an effective teacher utilizes a variety of resources to plan and structure learning opportunities. Fourthly, an effective teacher promotes diversity and civic-mindedness within schools. Lastly, an effective teacher collaborates with colleagues, parents, and school administration.

Efficiency and production are two factors that teachers may use to determine whether or not they will be successful in their careers. In their definition of teacher effectiveness, [ 7 ] outlined the interplay between internal factors (such as instructors' motivation, beliefs, and dispositions) and external factors (such as students' cultural, social, and educational backgrounds) that influence students' final results. Given that discoveries about TE have significant implications for education policy and reform, the correlates of TE become a matter of utmost importance. For teachers to be able to teach in a manner that is inventive, motivating, and meaningful, they need to be in excellent emotional and mental health. In the course of their trip through the world of education, teachers could feel a wide range of emotions. These events and feelings have a significant influence on their ability to succeed as well as on the achievement of their students. It is believed that teachers who effectively regulate their emotions throughout their work are more accomplished [ 8 ]. In accordance with the definition provided by [ 9 ], the concept of teacher emotion regulation (ER) refers to the capacity of a professor to control and administer their own emotional experiences and expressions. Teacher ER gives educators the ability to control the intensity and length of their emotional contact in the context of their professional work [ 10 ]. It will be substantially more difficult for teachers to show their efficacy as a result of these changes. It is necessary to do further study on the subject of ER since it is still in its infancy, especially in the realm of higher education [ 11 ].

Occupational wellness ( OW) involves maintaining a healthy balance between work and leisure activities to promote well-being, personal fulfillment, and financial success. The OW component is influenced by nurturing. The dimension recognizes personal satisfaction and improvement in an individual's life via employment. In a study by [ 12 ], four interconnected resources that contributed to the teachers' OW are highlighted: Psychological, social, human, and health capitals. Research to depict OW in the realm of language teaching in particular higher education is scarce. To fill this lacuna, the current research has constructed a mediation model to investigate the potential transmission of OW and TE to WP and ER within the context of higher education. This investigation offers an opportunity to enhance the understanding by examining the underlined connections. The data collected have sparked a discussion and created possibilities for subsequent studies.

Literature review

As a motivational process, WP helps teachers tackle different activities successfully. WP manifests itself in their propensity to engage in physically demanding tasks, which they eventually come to consider as fundamental to who they are [ 13 ]. As per the dichotomous paradigm for passion established by [ 14 ], passion may be categorized into two distinct forms: harmonious and obsessive. Harmonious passion is the result of an individual's autonomous engagement in an activity and its assimilation into their character. Harmonious passion refers to the deliberate acceptance of behaviors that are deemed important and meaningful, promoting a feeling of unity with one's whole being. Obsessive passion occurs when an individual internalizes an activity to the point that it becomes incorporated into their psyche, resulting in a sense of control. This preoccupation is often initiated by internal pressures and/or external factors linked to self-esteem or societal validation, or by the excessive level of enthusiasm generated by the engaged activities [ 15 ]. An increasing number of scholars have directed their attention towards investigating the impacts of passion in an academic setting. These researchers have established connections between passion and various academic outcomes, including students' academic achievement, intentional effort, perseverance, goal-oriented thinking, resilience in learning, and overall well-being [ 13 , 16 ] Research has indicated that an increase in a learner's passion correlates positively with their propensity to maintain concentration on enhancing their self-competence [ 17 , 18 ].

Considering TE, the educational frameworks developed by [ 6 ] and [ 19 ] are extensively used in educational environments to ascertain the efficacy of instructors in the modern day [ 18 ] 's procedure defines the four categories that are used to assess teachers' effectiveness: organizing and preparing, the setting of the classroom, teaching, and job responsibilities. Similarly, [ 6 ] presented his framework as ten inquiries that represent the correct order for effective educational design. These inquiries include setting learning goals, providing opportunities for learners to apply what they have learned and deepen their comprehension, facilitating student interaction with new knowledge, interacting with learners in the educational process, fostering productive relationships between learners and educators, implementing successful instructional strategies, communicating high expectations for learners, and employing effective, standards-based formative and summative assessment methods that utilize multiple indicators of student competency. According to [ 20 ], TE refers to their capacity to teach successfully in the classroom. Moreover, [ 21 , 22 ] argued that efficiency for instruction is a complex concept that is difficult to define due to its intangible nature. The present research is grounded on self-efficacy theory [ 23 ] and productivity theory [ 24 ]. Self-efficacy, as defined by Bandura, refers to an individual's belief in their ability to effectively affect their activities, leading to successful outcomes.

ER, the last construct in this research, refers to a broad framework that involves psychological, cognitive, and biological elements. It is used to effectively change conditions of emotion [ 25 ]. The concept of ER is not static, but rather a fluid process that impacts and motivates individuals' emotional experiences and expressions [ 26 ]. ER affects not only the initiation, but also the length and delay of emotional reactions, as well as cognitive, emotional, and bodily functions [ 27 ]. Creating a favorable emotional atmosphere enables instructors to better regulate not only their own emotions but also the emotions of their students. To elucidate the construct of teacher ER, [ 28 ] devised a model consisting of six dimensions. These dimensions are Situation selection, Situation alteration, Attention deployment, Reappraisal, Suppression, and Seeking social support. The first three dimensions of the model were formulated based on Gross' process model of ER [ 29 , 30 ]. The reappraisal and suppression aspects were developed based on the results of [ 26 ]. In their pursuit of social support, they used the research conducted by [ 2 ] and [ 31 ]. This model was applied in the present research. One study that looked at the link between burnout, classroom norms about emotional expression, and ER methods was the one conducted by [ 32 ] among teachers. The findings of [ 11 ] in higher education evidenced that university instructors who possess a comprehensive understanding of productive immunity and ER exhibit more resilience and autonomy. More precisely, ER provides university professors with the means to effectively address the challenges and difficult circumstances that arise in their careers. Moreover, the study by [ 33 ] uncovered that the emotional regulation, reflective teaching, self-efficacy, and identity of language instructors could be important factors influencing their psychological well-being. This study emphasizes the need to include reflective practices, emotional management skills, self-efficacy beliefs, and identity reconstruction within teacher training programmers' curricula.

Methodology

Research questions and aims.

Researchers have not yet examined the connections between OW, TE, WP, and ER in terms of their effectiveness in helping EFL teachers in higher education. Given the scarcity of research in this particular domain and the criticality of the enumerated elements in higher education, the objective of this investigation was to assess the effects of WP and ER on OW and TE among EFL instructors. The results of this research could potentially yield beneficial consequences for educators and learners, encompassing both theoretical and practical domains. In consideration of these perspectives, the subsequent areas of inquiry are suggested:

RQ1: Are work passion and emotion regulation for EFL university teachers indicative of their occupational wellness?

RQ2: Are work passion and emotion regulation for EFL university teachers indicative of their effectiveness?

Context and participants

There was a total of 401 individuals who took part in this research; among the language teachers, 250 were men and 151 were women. In China, they were teaching in higher education. Their ages range from twenty-nine to fifty-one, and their years of teaching experience range from a year to twenty-five. Among the participants, 401 had a PhD degree, while the remaining individuals had a master's degree in Applied Linguistics.

Research tools and procedures

The occupational well-being scale.

The 12-item Occupational Well-Being Scale (OWS) developed by [ 34 ] was used to assess teachers' well-being. This measure is designed to assess the overall health and happiness of educators. The participants were asked to rate how much they felt anxious, content, depressed, or enthusiastic concerning their occupation in the previous week. Anxiety is defined as a state of tension-ridden unease, or worry; contentment is defined as a state of being at ease, happy, or relaxed; and despair is defined as a state of sad, depression, or drained. From 1 (never) to 6 (always), there were a total of six possible answers. Based on Table 1 , dependability in this investigation was good.

The teacher effectiveness scale

The evaluation of TE was carried out with the assistance of the scale that was manufactured and verified by [ 35 ]. On this scale, which is comprised of 25 items that are separated into five sub-sections, some of the characteristics that are included are preparation and planning for teaching, classroom management, mastery of subject matter and its delivery, teacher attributes, and interpersonal interactions. In all, five different replies could be given, ranging from 1 (never) to 5 (always). In line with Cronbach's alpha, which varied from 0.806 to 0.881, the ABS dependability in this investigation was good.

The work passion scale

The work passion scale (WPS) by [ 3 ] was used for evaluating WP. The scale has 14 questions, with 7 items measuring harmonious passion and the remaining 7 items evaluating obsessive passion. The answer scale spanned from 1 ("Strongly disagree") to 7 ("Strongly agree"). According to Table 1 , the internal consistency of WPS is acceptable.

The language teacher emotion regulation inventory

The language teacher's emotion regulation inventory (LTERI) evaluated participants' ER techniques [ 28 ] developed this scale, which includes 27 questions and six sub-factors: scenario selection, situation alteration, attention deployment, reappraisal, suppression, and seeking social support. The LTERI questions were designed to be answered on a five-point Likert scale, with 1 indicating "never" and 5 representing "always". Cronbach's alpha scores ranged from 0.788 to 0.924, indicating excellent reliability for the LTERI in this study.

A web-based platform was created to facilitate the data collection process for this research project over a period of five months in 2023. The objectives of this four-part survey are to evaluate OW, TE, WP, and ER. To Analyze the data, the data was screened to check its normality using the Kolmogorov-Smirnov (K-S) test. Once it was determined that the data followed a normal distribution, parametric procedures were suggested for data analysis. The analysis was performed using CFA and SEM with Linear Structural Relations (LISREL) 8.80. As stated by [ 36 ], SEM is a reliable multivariate process that is used to verify the suggested structural theory using a confirmatory hypothesis-taking strategy. The measurement model and the structural model are the two components of a SEM model [ 37 ]. The links between the latent variables and the observable variables are investigated using the measurement model. The associations between the latent variables are measured using the structural model. It is recommended to use CFA to assess all latent variables before testing a structural model [ 38 ].

This section presents a concise overview of the data analysis, along with an elaborate explanation of every element of the report. Firstly, the gathered data underwent the K-S test to identify any patterns in the recurring presentations.

Table 2 shows that the significance values of all the instruments and their components were more than 0.05. This suggests that the findings follow a normal distribution, which justifies the use of parametric approaches for analyzing the data.

After that, the relationships between OW, TE, WP, and ER were investigated using structural equation modeling and a causal analytic framework. A statistical program termed LISREL 8.80 was employed to conduct these experiments. The chi-squared magnitude, RMSEA, CFI, GFI, and NFI were among the metrics used to assess the reliability of the model's predictions in relation to the actual data.

Figures  1 and 2 vividly illustrate the relationship between the factors. Table 3 presents standardized estimates and t-values to analyze the impact of OW, TE, WP, and ER. WP has a significant positive effect on occupational wellness with a beta coefficient of 0.59 and a t-value of 7.12. Similarly, WP also has a significant positive effect on TE with a beta coefficient of 0.52 and a t-value of 6.30. Additionally, ER has a significant positive effect on OW with a beta coefficient of 0.72 and a t-value of 15.66. ER has a positive impact on TE (β = 0.81, t = 21.53).

The Path Coefficient Values for the Connection among OW, TE, WP, and ER (Model 1)

Path Coefficients with T Significance Values (Model 1)

Figures  3 and  4 , together with Table 4 , demonstrate the connections found between the WP and ER components as well as OW and TE. A correlation was discovered between OW and Harmonious Passion (β = 0.54, t = 6.12), Obsessive Passion (β = 0.51, t = 5.97), Situation Selection (β = 0.80, t = 20.59), Situation Modification (β = 0.71, t = 14.08), Attention Deployment (β = 0.67, t = 11.45), Reappraisal (β = 0.74, t = 14.35), Suppression (β = 0.63, t = 9.75), and Seeking Social Support (β = 0.70, t = 12.69). The relationships between TE and Harmonious Passion (β = 0.54, t = 6.12), Obsessive Passion (β = 0.51, t = 5.97), Situation Selection (β = 0.80, t = 20.59), Situation Modification (β = 0.83, t = 22.51), Attention Deployment (β = 0.82, t = 21.76), Reappraisal (β = 0.85, t = 24.47), Suppression (β = 0.76, t = 16.47), and Seeking Social Support (β = 0.78, t = 18.63) are also valid.

The Path Coefficient Values for the Interconnections between OW, TE, WP, and ER (Model 2)

Furthermore, according to [ 39 ], the chi-square is considered to be insignificant, and the ratio of chi-square to degrees of freedom ought to be less than three. It is also noted that the root means square error of approximation (RMSEA) should be less than 0.1. Additionally, a good match is indicated by the NFI where the cut value is larger than 0.90, the GFI where the cut value is higher than 0.90, and the CFI where the cut value is greater than 0.90 [ 39 ].

As can be shown in Table 5 , Model 1 fit criteria are met by the chi-square/df ratio of 2.819, the RMSEA of 0.067, the GFI of 0.925, the NFI of 0.934, and the CFI of 0.959. Additionally, Table 5 provides a brief overview of the fact that every model fit index associated with Model 2 is suitable. Examples include the RMSEA (0.070), the GFI (0.945), the NFI (0.938), the CFI (0.952), and the chi-square/df ratio (2.908).

Moreover, this research used a Pearson product-moment correlation to examine the association between OW, TE, WP, and ER.

Table 6 displays the connections between OW, TE, WP, and ER that were determined to be significant. It was found that WP was associated with OW ( r = 0.615) and TE ( r = 0.602). The connections between ER and OW ( r = 0.745) and TE ( r = 0.841) were also confirmed. The details of these connections are illustrated in Table 6 .

Table 7 illustrates the positive and statistically significant link that exists between the OW, TE, WP, and ER subcomponents. OW and harmonious passion ( r = 0.593), obsessive passion ( r =0.624), Situation Selection ( r =0.678), Situation Modification ( r = 0.734), Attention Deployment ( r =0.703), Reappraisal ( r =0.760), Suppression ( r = 0.655), and Seeking Social Support ( r = 0.721), all supported this conclusion. Furthermore, it was established that the subsequent correlations between TE, WP, and ER subfactors were positive and statistically significant: harmonious passion ( r = 0.560), obsessive passion ( r =0.531), Situation Selection ( r =0.824), Situation Modification ( r = 0.856), Attention Deployment ( r =0.841), Reappraisal ( r =0.879), Suppression ( r =0.780), and Seeking Social Support ( r = 0.804).

The overall theme of this research was to investigate the impact of EFL university teachers' WP and ER on their OW and TE. The results, particularly Model 1, indicate that WP and ER are important factors in predicting OW and TE. In accordance with the findings, notably Model 1, WP and ER are significant determinants in determining the likelihood of OW and TE that will occur. Self-determination, autonomy, enthusiasm, resilience, and persistence are all traits that may be developed via the cultivation of a powerful degree of protection and a skilled ability to manage emotions. Disregarding one's emotional balance and displaying maladaptive emotional intelligence, on the other hand, might have negative consequences. As a result, teachers in higher education need to use more ways that are contemplative and self-analytical to face the complex challenges and changes that are occurring in educational contexts [ 40 ]. It is of the utmost importance to improve teachers' understanding of the underlying principles that underlie the notions of WP, ER, OW, and TE, as well as the critical role that these competencies play in their job performance.

Firstly, it was revealed that enthusiasm for teaching and emotion management was an essential predictor of teacher OW (QR1: Are work passion and emotion regulation for EFL university teachers indicative of their occupational wellness?). This conclusion is corroborated by various researchers who have emphasized the relevance of teachers’ emotion control in classroom environments and its consequences on teacher welfare [ 41 , 42 ]. It is plausible to suggest that instructors’ coping methods could enhance the association between emotional control and emotional health. For instance, teachers who apply more suitable emotional control tactics may suffer less fear from disordered learning situations and learners’ disobedience as they are adept at addressing stresses that arise in the educational setting. As a consequence, the perception of capability may make it possible for educators to achieve a higher level of psychological well-being, which in turn helps them to increase the likelihood that they will be satisfied with their job and the teaching they do in the classroom [ 42 , 43 ]. On the other hand, when EFL instructors are unable to successfully manage their emotions, they are unable to successfully cope with the challenges that arise in the classroom; thus, they may consider their job to be emotionally draining [ 12 , 43 , 44 ]. In addition, teachers who can control their emotions can devise strategies that are suitable for their emotional state and establish a profound and pleasurable connection with their students. Consequently, teachers are more likely to experience satisfaction and joy in their work, and they are also more likely to boost their personal growth as they achieve strong emotional well-being throughout their careers. This conclusion is congruent with study findings of [ 45 ] which indicated that instructors’ effectiveness judgments were connected with their psychological and physical participation in instructional activities.

Secondly, it was established that teacher perception of effectiveness may strongly predict the psychological wellness of EFL instructors. This conclusion corresponds with the outcomes of a substantial body of literature indicating that high levels of teachers’ self-efficacy are connected with an elevated state of emotional wellness [ 46 ]. For instance, [ 47 ] observed that the self-worth of teachers was connected to their overall mental health. In other words, instructors with high self-efficacy reported greater degrees of good emotions and contentment and had a lower proportion of unfavorable sentiments. A possible reason could be that educators with a higher level of optimism (e.g., perceptions that they have a substantial effect on student’s development and learning) may be extremely motivated and exceedingly satisfied with their profession, which in turn may enhance their emotional health. This is corroborated by [ 48 ] who suggested that one’s intrinsic drive adds to their psychological wellness. Additionally, instructors’ enthusiastic mindset about their instruction could assist them in nurturing their teaching abilities and instructional efficacy by palliating their emotional burdens and problems. It may also be suggested that educators have a feeling of fulfillment and suffer less fatigue if they are equipped with stronger efficacy views and confidence in their skills to teach effectively and energetically engage their learners.

It may be suggested that educators’ substantial degree of competency and efficacy in their teaching abilities would lessen the levels of fear, dissatisfaction, and sadness. This explanation fits with the findings of several recent research that identified teacher self-efficacy as a negative indicator of disengagement and burnout [ 49 ]. From this approach, greater degrees of self-worth in teaching could be associated with increased work satisfaction and positive employment desires. The truth is, that teachers with a higher degree of instructional efficacy may grow confidence-boosting in employing methods for emotional regulation if they run into challenging and demanding situations; subsequently, they encounter fewer concerns in their position than instructors who have lower degrees of self-worth. As a consequence, teachers’ good feelings (i.e., reduced worry and further job satisfaction) might boost their psychological welfare as well as optimum functioning, pushing them to further devotion to instructional operations and work commitment. This is corroborated by [ 50 ] who illustrated that teachers’ contentment in educational settings is highly connected with their involvement levels. This research provides important insights on OW and TE with the mediator roles of WP and ER, and it has significant implications for policymakers, teacher educators, and other relevant stakeholders who are interested in understanding how to help language teachers in their professional duties.

All in all, under the surface layer of teacher occupational wellness and effectiveness different factors are hidden. The study findings uncover that WP and ER are critical in determining the state of OW and TE in higher education. These results have significant implications for instructional administrators as a whole and the executive boards of higher education institutions specifically. The outcomes confirmed the significance of a teacher's WP and ER as strategic instruments for enhancing their OW and TE. Therefore, based on the results of this study, it is recommended that educators and strategists in higher education institutions focus on enhancing students' WP and ER by investing in the development of teachers' skills in this regard. This can be achieved by increasing teachers' knowledge through opportunities by providing them with training specifically tailored for teaching in higher education. The results of this research also provide evidence that emotional contagion fully mediates the transmission of a teacher's professional enthusiasm. This discovery indicates that a teacher's intense enthusiasm for their job may influence the ways they teach and act. Higher education planners must prioritize teachers' education. The results of this study indicate that the transmission of a teacher's WP and ER to OW and TE is more pronounced when the instructor has a Ph.D. degree. The results suggest that tactics in educational contributions and recruiters/hiring managers in higher education institutions should review faculty recruitment and selection policies. This will help them assign the most competent applicants with higher education and requirements, ensuring the delivery of high-quality education.

Higher education faculty may benefit from considering the instructional implications of this study's results. Higher education programs should take into account the need to teach students how to effectively use ER techniques based on contextual and psychological variables. A wide variety of tactics should be the focus of such training programs, with an emphasis on demonstrating when and how each one works. Training should also place more emphasis on how university teachers' personal qualities, as well as preferences, impact the efficacy of their ER tactics. This data also encourages academics to change their negative ER methods to more positive ones, which should help their self-efficacy and second language grit.

Professionals in the field of EFL education would do well to assist their fellow educators by providing them with guidance and training in the identification and management of stress, as well as in the development of healthy pedagogical attitudes and emotional regulation skills. Managers may do their part to create a warm and inviting classroom environment where EFL instructors feel comfortable sharing their true sentiments and where they can get the information, they need to accurately assess different teaching situations. If teachers have a more positive outlook on their work environment, they may be more motivated to find solutions to the emotional challenges they face in the classroom. The findings of this research highlight the need to support EFL professionals in developing their self-worth and knowledge since these factors are known to play a significant role in ensuring their psychological health while working in the EFL field. One way to make this happen is to provide teachers with more opportunities to acquire the knowledge they need to address any gaps in their pedagogical repertoire. Furthermore, to foster a more favorable perception of EFL instructors' ability as educators, supervisors or administrators should provide them additional power.

Limitations and suggestions for future researchers

Some limitations are included in the results of this study: To begin, the research used a quantitative approach. Future research can utilize mixed-method approaches to examine the connections between various teacher-related concepts, such as ER, self-efficacy, L2 grit, work engagement, autonomy, critical thinking, job satisfaction, reflective teaching, self-regulation, and immunity, to gain a better grasp of the causal interactions among the variables in question. Secondly, as a point for future study, we did not investigate how participants' demographic factors impacted OW, TE, WP, and ER. Finally, individuals were selected via convenience sampling owing to practical limitations. Which is not accurately reflective. Consequently, findings from this research should be carefully understood and used broadly with caution. This research will concentrate on the ER methods used by EFL university teachers in the workplace. A trait method was used to evaluate EFL university teachers' ER. Moreover, ER methods in reaction to emotional events in the workplace were evaluated retrospectively, focusing on their frequency and intensity. Additionally, additional studies should be conducted to see whether ER affects their students' ER. Another idea is for academics to look at different types of educational settings, such as public and private language schools, to see how OW, TE, WP, and ER interact.

Availability of data and materials

The dataset of the present study is available upon request from the corresponding author.

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Yu, X., Ying, T. Under the surface of teacher occupational wellness and effectiveness in higher education: a look into the mediator roles of work passion and emotion regulation via SEM analysis. BMC Psychol 12 , 166 (2024). https://doi.org/10.1186/s40359-024-01656-2

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The Wrong Way to Study AI in College

Computer-science students are being shielded from the liberal arts. That may be a problem.

This is Atlantic Intelligence, a limited-run series in which our writers help you wrap your mind around artificial intelligence and a new machine age. Sign up here.

Earlier this week, my colleague Ian Bogost published a provocative article about a trend in higher education: the opening of distinct colleges of computing, akin to law schools. New programs at MIT, Cornell, and soon UC Berkeley follow an uptick in the number of students graduating with computer-science majors. They are serving a growing market.

“When they elevate computing to the status of a college, with departments and a budget, they are declaring it a higher-order domain of knowledge and practice,” Ian writes. “That decision will inform a fundamental question: whether computing ought to be seen as a superfield that lords over all others, or just a servant of other domains, subordinated to their interests and control. This is, by no happenstance, also the basic question about computing in our society writ large.”

This talk of subordination naturally turned my mind toward AI, a technology that some believe threatens to upend the world as we know it. If students pursue majors in AI within the isolated confines of a college of computing—without the grounding of a broader arts-and-science education—how can we expect them to make wise decisions about how that technology is applied? I asked Ian for his thoughts.

“We generally don’t use computers just for computing—the computing does something,” he told me. “ AI is a term without real meaning; in some ways, it’s just a nickname for a certain intensification of automation that’s been ongoing for years. But one of the promises, or threats, of AI is that it can apply computing problem-solving to many more domains, and very effectively. So if computer people do, in fact, know less about everything in the world beyond computing while simultaneously building or applying AI to many more problem spaces, and faster, too, then that seems like it could be quite concerning indeed.”

But the risk, such as it is, extends in two directions. The “computer people” might be missing out on the liberal arts, but so, too, are the liberal-arts people missing out on computer science. “If people with domain expertise insist on simply resisting AI as an unwelcome incursion, one that can be fought, they also risk ceding their opportunity to influence how computing might colonize their efforts,” Ian said. “It seems naive to think that it just won’t, that the ‘enemy’ can be held off at the gates forever—or even that it’s an enemy you’d want to hold off.”

— Damon Beres , senior editor

Universities Have a Computer-Science Problem

By Ian Bogost

Last year, 18 percent of Stanford University seniors graduated with a degree in computer science, more than double the proportion of just a decade earlier. Over the same period at MIT, that rate went up from 23 percent to 42 percent. These increases are common everywhere: The average number of undergraduate CS majors at universities in the U.S. and Canada tripled in the decade after 2005, and it keeps growing. Students’ interest in CS is intellectual—culture moves through computation these days—but it is also professional. Young people hope to access the wealth, power, and influence of the technology sector. That ambition has created both enormous administrative strain and a competition for prestige. At Washington University in St. Louis, where I serve on the faculty of the Computer Science & Engineering department, each semester brings another set of waitlists for enrollment in CS classes. On many campuses, students may choose to study computer science at any of several different academic outposts, strewn throughout various departments. At MIT, for example, they might get a degree in “Urban Studies and Planning With Computer Science” from the School of Architecture, or one in “Mathematics With Computer Science” from the School of Science, or they might choose from among four CS-related fields within the School of Engineering. This seepage of computing throughout the university has helped address students’ booming interest, but it also serves to bolster their demand.

What to Read Next

• Elon Musk just added a wrinkle to the AI race : “Transparency, or the appearance of it, is the technology’s new norm,” Matteo Wong writes.
• The AI industry is stuck on one very specific way to use a chatbot : “OpenAI, Google, and Microsoft are dying to help plan your next trip,” Caroline Mimbs Nyce writes.
• Whatever happened to all those care robots? : “So far, companion robots haven’t lived up to the hype—and might even exacerbate the problems they’re meant to solve,” Stephanie H. Murray writes.

I spotted a new Coca-Cola flavor at the grocery store earlier this week—“raspberry spiced”—and had a flashback to September, when the company unveiled a formula that was purportedly made with the help of artificial intelligence. “It smells like circus-peanut candies and tastes mostly like Coke,” my colleague Kaitlyn Tiffany wrote at the time . Bottoms up.

• Open access
• Published: 19 March 2024

Perception of the operation theater learning environment and related factors among anesthesia students in Ethiopian higher education teaching hospitals: a multicenter cross-sectional study

• Habtemariam Wubshet 2 ,
• Abatneh Feleke Agegnehu 1 ,
• Misganaw Mengie Workie 1 &
• Yonas Addisu 1  

BMC Medical Education volume  24 , Article number:  303 ( 2024 ) Cite this article

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Introduction

Operation theater learning involves three key elements: clinical work, learning, and the environment. There is little evidence regarding the operating theatre learning environment for anesthesia trainees. Identifying the overall perception of the operation theater learning environment helps to establish an efficient operation theater learning environment and produce competent anesthesia professionals.

The aim of this study was to assess the perceptions of the operating theater learning environment and associated factors among undergraduate anesthesia students in Ethiopian higher education teaching hospitals from April to May 2023.

A multicenter cross-sectional study was conducted on 313 undergraduate anesthesia students who began operation room clinical practice at 13 higher education teaching hospitals. The data were entered into EpiData version 4.6. A generalized ordered logistic regression model was used to analyze and identify factors associated with the operating theater learning environment using STATA software version MP17.

The findings of this study revealed that 45.05%,26.52%), 23% and 5.43% of the participants reported having desirable, moderately desirable, very desirable and undesirable perceptions of the operating theater learning environment, respectively, from highest to lowest. Preoperative discussion (AOR = 4.98 CI = 1.3–18.8), lack of teaching facilities (AOR = 0.16 CI = 0.03–0.75), noise from played music (AOR = 0.22 CI = 0.07–0.63), absence of tutors (AOR = 0.03 CI = 0.01–0.22), respect for students (AOR = 3.44 CI = 1.6–7.2), roll modeling for students (AOR = 3.23 CI = 1.5–6.8) and strict supervision of students (AOR = 0.24 CI = 0.07–0.88) were significantly associated with perceptions of the operation theater learning environment, with 95% CIs.

No study participant agreed that the operation theater learning environment in operation theatres was very undesirable. A lack of teaching facilities at the OR, a lack of tutors from the OR, noise from played music in the OR, a tutor respecting their student, a tutor role model for their student, a preoperative discussion with a tutor and strict supervision of the student are strongly associated with the operation theatre learning environment.

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Modern medicine would be unthinkable without the profession of anesthesia. On October 16, 1846, the surgeon John Warren and dentist William T.G. Morton performed the first operation under a primitive form of anesthesia at Massachusetts General Hospital in Boston. Among medical specialties, anesthesia is unique in that it requires extensive knowledge in three major areas of learning: cognitive (knowledge), psychomotor (procedural skills), and affective (nontechnical skills) [ 1 ].

Anesthesiologists have been among the pioneers of patient safety [ 2 ]. Anesthesiology is a branch of medicine that addresses the treatment of surgical patients before, during and after surgery, as well as pain management, and is involved in the treatment of patients who require intensive care [ 3 ]. Students should be able to practice their theoretical knowledge at clinical practicum sites, develop their clinical thinking and clinical skills, and adapt to professional situations through clinical practice [ 4 ].

In operation theater learning environments, theory is translated into practice, and it is a clinical classroom that includes three key elements: clinical work, learning, and environment [ 5 ]. The majority of anesthesia educators believe that the most typical form of anesthesia education is teaching in the operating room (OR) [ 6 , 7 ]. The OR is a complex environment in which a variety of factors, such as advanced equipment and the sensitivity of clinical situations, create a unique and engaging learning experience; however, if not done properly, teaching can occasionally lead to cognitive overload for both the instructor and the learner [ 6 , 8 ].

There are Lyon’s three key categories of obstacles for operation theater learning (OTL): physical setting, the learning task in that setting, and the difficulty of managing social relationships in theaters [ 9 ]. The operating room is a special setting with its own culture, procedures, and interdisciplinary team. Many of whom have been working together for a very long time [ 10 ]. Quality has a special meaning in clinical education, so clinical education can be viewed as a learning activity in a clinical setting involving the clinical instructor and the student [ 11 ]. The Accreditation Council for Graduate Medical Education (ACGME) developed 6 core competencies that can be taught effectively while teaching in the OR [ 12 ]. These include patient care, clinical knowledge, practice-based learning improvement, role models, communication skills, professionalism and system-based practice [ 13 ].

The operating theater learning environment is thought to influence learner behavior and predict learner competency in clinical practice. An assessment of the operating theater learning environment is a technique for evaluating the quality of educational programs [ 14 ]. The operation theater learning environment (OTLE) is not always optimal for learning because of clinical productivity expectations and a lack of support from supervisors.

Operation room (OR)-based student learning has traditionally been suboptimal. Learning in operating rooms and similar settings can be difficult. Even in teaching hospitals, patient safety must remain the top priority for all staff members, and in this respect, learning plays a secondary role for instructors [ 15 ].

The effectiveness of clinical teaching and learning can be influenced by several factors. A faculty member conducts clinical instruction within the framework of a curriculum designed and delivered in response to professional, societal, environmental, and educational expectations and demands using the available human, intellectual, material, and financial resources of the curriculum [ 16 ]. The provision of the instructor, proper management, and availability of opportunities for learners are just some of the aspects that determine the success or failure of theory-practice integration [ 17 ].

Effective supervision, a sufficient number of tutors, and clinical instructors are facilitating factors, and a lack of self-confidence, absenteeism, inadequate supervision, and a lack of resources are obstacles to effective clinical learning [ 18 ]. Effective teaching efforts in the OR may be hindered by considerable problems that may be present at the same time, such as medical-legal issues and production pressure, these difficulties, teaching anesthesiology in the operating room will likely continue to be the primary educational setting for their specialty [ 6 ]. Some studies have outlined a few of the advantages that students look for in an excellent instructor, including excellence in clinical practice, demonstration of clinical excellence, active engagement of learners, positive attitude toward teaching, ability to create a positive overall environment, role models of professionalism and overall concern for the learner [ 19 ]. Before their OR rotation, students who receive formal training in virtual environments do better than those who do not, and undergraduates can practice in a safe environment using simulations before their theatre placement [ 20 ]. The OR experience can be greatly improved with the use of didactic lectures, web conferences, online seminars, pretheatre workshops, virtual training, and simulated operation suites (SOS) [ 21 ]. Although every anesthesiologist learns his or her craft in the operating room, the majority of anesthesia educators are not trained in how to teach effectively in this environment [ 6 ]. The physical environment may make it difficult to teach clinical skills due to loudness, a lack of privacy for giving feedback, or a lack of space for training [ 22 ].

In the operating room, learning interactions between the clinical instructor and the student are often brief, unplanned, spontaneous and opportunistic. Although most operating room teachers have extensive teaching experience in their setting, only a few are able to enhance and maximize opportunities and learning obstacles [ 23 ]. Clinical instructors must become proficient in the techniques and strategies that will enhance teaching and learning in operating rooms. Understanding learning methodologies and principles improves instructors’ capacity to provide successful instruction and fosters gratifying faculty-learner engagement [ 24 , 25 ]. Learning settings are among the crucial aspects of learning, along with the skills and traits of clinical teachers [ 26 ].

Operating room learning is an important phase in anesthesia students’ training; however, it remains unstructured [ 27 ]. students have reported inadequate organizational support for OR-based learning [ 24 ]. When learning objectives are futile, they hinder learning and cause dissatisfaction and confusion [ 22 ].

Raindrop and colleagues reported that 47% of the students were uncertain learning objectives when asked about them [ 22 ]. To achieve this goal, the importance of a comprehensive OR orientation session is pivotal. It is imperative to emphasize senior faculty members’ active involvement in mentoring and supervising students if students are to comprehend the significance of their theatre placement. Teamwork and a sense of feeling socially included are powerful determinants that positively influence learning. The friendliness and approachability of staff are the most important factors, and they are reported to affect learning [ 28 ]. A lack of orientation about the OR environment induces stress and confusion [ 27 , 29 ].

Numerous studies have investigated the factors influencing the operating theater learning environment outside of the operating room; however, there is little evidence regarding the operating theatre learning environment for anesthesia trainees [ 30 ]. It is essential to determine what the operating room learning environment should look like to increase students’ enthusiasm, strength and empowerment through the use of proper educational strategies and teaching methods to boost students’ interest in anesthesia clinical practice.

To create standardized OTLEs in the future, at least a baseline study is needed, and exploration of Ethiopian anesthesia students’ operation theater learning environment will continue to be an important research focus. The aim of this study was to evaluate the learning environment in operating theaters and its associated factors among undergraduate anesthesia students in Ethiopian higher education teaching hospitals. Specifically, our objectives are to assess the learning environment in operating theatres among undergraduate anesthesia students in Ethiopian higher education teaching hospitals and to identify the factors influencing the operating theatre learning environment. The focus of the study is on evaluating the learning environment in operating theaters for undergraduate anesthesia students, with the aim of enhancing educational quality in this field. By identifying factors influencing the learning environment, such as teaching methods and resources, this study seeks to inform improvements in teaching practices and student experiences. This can lead to increased satisfaction, motivation, and engagement among students undergoing anesthesia training. Insights from the study can also inform targeted interventions to optimize learning outcomes and competency development. Furthermore, the study serves as a baseline for ongoing evaluation and improvement of the learning environment, facilitating continuous enhancement of educational quality. Additionally, this research contributes to the existing knowledge on anesthesia education and learning environments, particularly in Ethiopian higher education teaching hospitals, benefiting future research and facilitating cross-cultural comparisons.

Methods and materials

A multicenter cross-sectional study design was employed to evaluate the perceptions of the operation theater learning environment and related factors among undergraduate anesthesia students in Ethiopian higher education teaching hospitals. The study was conducted from April to May 2023. The study included all undergraduate anesthesia students in Ethiopian higher education teaching hospitals who commenced clinical practice in the operating theatre. Data collection utilized a survey method, with a self-administered questionnaire distributed to each university. A representative from each university facilitated the distribution of the questionnaire to every student, ensuring the inclusion of all students from each university. The convenience sampling method was employed in this study. To perform the study, the University of Gondar School of Medicine provided ethical approval. Participants had the freedom to fill or ignore participation in this study. The names of participants were removed from questionnaires to ensure confidentiality. Due to its practicality, all students were incorporated into this study. However, upon completion, data from seven participants were found to be incomplete and were consequently excluded from the study. To ensure data quality, we conducted pretesting of the data collection tool.

Sample size determination

In Ethiopia, 27 universities offer undergraduate anesthesia training, but only 13 of them have teaching hospitals. Our research focused solely on universities with teaching hospitals. Initially, 328 students started the anesthesia module course and clinical practice in the operating room, and we were able to include all of them in our study. However, data from seven participants were incomplete, leading to their exclusion. As a result, the final sample size comprised 313 students.

The 13 universities with their corresponding numbers of students are as follows: 57 students at Addis Ababa University, 10 students at the University of Gondar, 20 students at Jimma University, 23 students at Hawassa University, and 30 students at Bahir Dar University. Dilla University − 18 students, Wolaita Sodo University − 19 students, Menelik II College − 39 students, Debre Birhan University − 22 students, Adama Health Science College − 13 students, Wachemo University − 33 students, Arsi University − 32 students, Bule hora University − 12 students.

Dependent variables

Perception to Operation Theater learning environment.

The dependent variables included sex, clinical instructor factor, emotional factors, socio environmental factors and organizational factors.

Operational definition

The perception of the operating theater learning environment is categorized based on the recommendations of an expert statistician and an expert in clinical education. A “very undesirable” environment indicates a very poor educational setting, where negative aspects outweigh positive aspects. An “undesirable” operation theater learning environment signifies that negative aspects are predominant over positive ones. A “moderately desirable” operation theater learning environment suggests numerous problems with the educational setting. In a “desirable” operation theater learning environment, positive aspects outweigh negative aspects. A “Very Desirable” environment denotes an excellent educational setting. The score is 160 [ 14 ]. The questionnaire was adapted from the Persian version of the Anesthetic Trainee Theater Educational Environment Measure (ATEEM) tool. It measures the learning environment for anesthesia students in the OR. The ATEEM tool consists of 40 items with a maximum possible score of 160. These materials are very undesirable (0–31), undesirable (32–63), moderately desirable (64–95), desirable (96–127) and very desirable (128–160).

Data collection tool, methods and procedures

The data were collected using an English version of a self-administered structured questionnaire adapted from the Persian version of the Anesthetic Trainee Theater Educational Environment Measure (ATEEM) tool, which was designed to evaluate the learning environment of anesthesia students in the operating room (OR). The ATEEM tool comprises 40 items across five domains: autonomy, perceptions of the operation theater learning environment, workload support, perceptions of teachers and teaching-learning opportunities, and orientation to learning. The tool demonstrated strong internal consistency (Cronbach’s alpha of 0.95) and was validated for face and content validity by the developer. The questionnaire consists of two sections, with the first gathering demographic data such as sex, university name, and academic year. The second section included 40 items scored on a 5-point Likert scale (ranging from 0 to 4), with negatively worded questions reverse-coded. The maximum score for each domain is determined by multiplying the number of questions by the highest possible score for each item [ 14 ].

Data quality management

To ensure data quality, we conducted pretesting of the data collection tool (the questionnaire) on a sample representing 5% of the student population. Completion of the questionnaire was promptly verified shortly after it was completed. Furthermore, before analysis, the obtained data were coded, cleaned, and investigated. Each data point was checked for completeness before being placed into the electronic data. The analysis did not include incomplete data.

Data management and analysis

The data were entered into EpiData 4.6 and then exported to Statistical Software for Data Science (STATA) Version MP17 for cleaning and analysis. Descriptive statistics were used to describe participants’ sociodemographic status, while inferential analyses were performed to identify factors influencing the operation theater learning environment. The results are presented in the form of text, figures, tables, graphs, and charts. To determine the association between predictor variables and the outcome variable, the chi-square test was employed, and significant variables (with a p  value less than 0.05 and a 95% confidence interval) were fitted for bivariable logistic analysis. Responses associated with the operating theater learning environment (with a p  value less than or equal to 0.2) from the bivariable analysis were entered into generalized ordered logistic regression analyses to identify factors associated with the learning environment. A p  value less than 0.05 in the 95% confidence interval was considered to indicate statistical significance. To address concerns about multicollinearity, the Spearman test, variance inflation factor (VIF), and tolerance test were used. All tolerance values greater than 0.1 and all VIF values less than 10 indicated that any significant relationships found were not inflated by correlations between the predictor variables. Adjusted odds ratios (AORs) with corresponding 95% confidence intervals (CIs) were calculated to determine the associations of the independent factors with the outcome variables. Box plots were used to check for outliers.

Sociodemographic characteristics of the study participants

In this study, 313 undergraduate anesthesia students from 13 higher education teaching hospitals participated, for a response rate of 95.7%, as shown in Table  1 .

Anesthesia student’s perception of operation theater learning environments

Nearly half of the participants 141(45.05%) rated the operation theater learning environment as desirable, while 83 (26.52%) rated it as moderately desirable. Approximately 72 (23%) of the participants rated it as very desirable, and only 17 (5.43%) considered it undesirable. Interestingly, none of the participants rated the environment as very undesirable in Ethiopian higher education teaching hospitals, as shown in Figs.  1 and 2 .

Bar graph of the outcomes of perception of the operating theater learning environment percentage in 2023

Bar graph of the outcomes of perceptions of the operating theater learning environment at each university in 2023

Factors affecting the perception of the operation theater learning environment and student responses

The study revealed that many students felt that operation theater placement did not adequately support learning 83(26.52%). A significant portion did not receive clinical demonstration 121(38.66%) or simulation 124(39.62%) before practicing on real patients. A greater number of students 236(75.4%) discussed elective surgical cases with their tutors preoperatively. Nearly half of the patients experienced fear during patient management 156(49.84%). Common issues included a lack of competent instructors 70(22.36%), a lack of teaching facilities (164, 52.4%), and insufficient number of theater sessions 85(22.16%) per week. Additionally, some students felt that their tutors lacked respect 96(30.67%) and were not effective role models 159(50.8%) (Table  2 ).

Factors associated with perceptions of the anesthesia operation theater learning environment

The study identified significant factors influencing the operation theatre (OR) learning environment through bivariable and multivariable analyses. A lack of teaching facilities reduced the odds of a very desirable learning environment by 84%, while the absence of tutors in clinical areas reduced the odds by 96.5%. Noise disturbance decreased odds by 80%, while students in generic anesthesia programs had 4.59 times greater odds of a very desirable environment. Tutors respecting students increased odds by 3.5 times, and being a role model increased odds by 3.2 times. Preoperative discussion of surgical cases with tutors increased the odds by 4.98 times, but strict supervision during patient management decreased the likelihood of a very desirable operation theatre learning environment by 75% (Table  3 ).

The operating room is a unique and dynamic learning environment that caters to a wide range of learning styles, encompassing spatial, aural, verbal, physical, logical, interpersonal, and intrapersonal aspects [ 7 ]. In this study, 23% and 45.05% of participants perceived operation theater learning environments as very desirable and desirable, respectively. However, these figures are lower than those of similar studies conducted at the University of Witwatersrand (30.6%, 67.1%) and in Thailand (48.4% and 51.6%), respectively [ 3 , 31 ].

Among all the participants, 83 (26.52%) and 17 (5.43%) stated that the operation theater learning environment was moderately desirable and undesirable, respectively. This finding contrasts with a study conducted in Thailand, which reported that only 2.4% and 0% of respondents reported a moderately desirable and undesirable learning environment, respectively [ 31 ]. The difference may be attributed to their use of a different cutoff point for the tool’s category levels. Their total score was 164, while this study scored out of 160 based on tool recommendations. Additionally, the use of more advanced medical equipment and technology in Thailand’s anesthesia operation theaters compared to the lack of teaching facilities in our country may have influenced these results, leading to no reported instances of an undesirable operation theater learning environment in Thailand.

This study highlights how the absence of teaching facilities in the operating room (OR) detrimentally impacts the creation of an optimal learning environment. This finding correlates with findings from the study “Educational Resources for Anesthesia Training Programs,” which concludes that teaching facilities in anesthesia education environments often fall short of meeting national standards [ 32 ]. The expansion of professional anesthesia training programs in low- and middle-income nations underscores the urgent need for investment in teaching materials. A sufficient supply of teaching instruments and resources is indispensable for fostering an efficient learning environment in the operating theatre. This study sheds light on a critical aspect of medical education: the importance of adequate teaching facilities in the operating room (OR) for creating an optimal learning environment. The finding that the absence of such facilities can detrimentally impact learning underscores the need for attention and investment in this area. It not only affects the quality of education for current students but also has broader implications for the future of healthcare.

The likelihood of experiencing a highly desirable learning environment in the operation theater decreased significantly by 96.5% when tutors were absent from the clinical learning area. This conclusion is reinforced by a survey conducted in the United States of America on the supervision of anesthesia trainees, which revealed that inadequate supervision can have a detrimental impact, reducing the learning environment by 90%. This deficiency not only affects student education but also compromises patient care and safety [ 33 ]. Students who reported higher levels of supervision performed procedures more frequently than did those with lower supervision scores. Insufficient supervision has been linked to a greater frequency of patient deaths under the care of junior students, thereby affecting the desirability of the operation theater learning environment. It is plausible that anesthesia training programs burdened with heavy clinical workloads struggle to provide trainees with the necessary supervision. This was further evidenced by the inverse correlation between low supervision and the assertion that excessive clinical workload poses a significant obstacle to effective supervision. Our findings suggest that academic institutions encounter greater challenges in delivering adequate supervision to trainees when faced with a greater demand for clinical services. the study’s findings highlight the multifaceted implications of inadequate supervision in anesthesia training. They underscore the importance of prioritizing supervision in medical education and call for concerted efforts to address the underlying factors contributing to this issue. By investing in supervision, institutions can create a more supportive learning environment, enhance procedural proficiency among trainees, and ultimately improve patient care and safety.

The likelihood of achieving a very desirable learning environment was significantly reduced by 80% when noise from playing music disrupted the operating room (OR). This discovery is supported by a systematic review conducted on noise and music in the operating room, which emphasizes the need for caution regarding music played in such settings. Questions arise regarding its safety concerning communication and distraction due to the elevated decibel levels it introduces [ 34 ]. Another study further illustrated the challenges posed by music-induced noise in effectively hearing, understanding, and communicating, thus impacting the desirability of the operation theater learning environment. Given that poor communication is a significant contributor to adverse events, addressing such distractions becomes imperative. The Environmental Protection Agency (EPA) recommends a maximum background noise level of 45 decibels (dB) in hospitals, yet even this level can prove distracting [ 35 ]. Monitoring noise levels during trauma procedures in operating rooms revealed an average noise level of 85 dB, ranging from 40 to 130 dB [ 36 ]. Notably, orthopedic and neurosurgical procedures exhibit some of the highest continuous background noise levels, with intermittent peak levels exceeding 100 dB occurring more than 40% of the time [ 35 ]. In a laboratory experiment simulating OR background noise, anesthesia students’ ability to accurately identify changes in saturation on a pulse oximeter decreased by 17% [ 37 ].

The discussion of elective surgical cases with a tutor before an operation significantly increased the likelihood of achieving a very desirable learning environment in the operation theater by 4.98 times. This finding is supported by a study conducted by Zhou Y, which concluded that preoperative discussions for students, facilitated through standardized training in the Department of Anesthesiology, were highly effective, practical, and tailored to individual needs [ 38 ]. During these discussions, the benefits of four learning approaches—problem-based, patient-focused, disease-centered, and learner-focused—are seamlessly integrated into interactive teaching sessions. This approach not only fosters active student engagement but also enhances clinical reasoning skills by encouraging participation in the analysis and resolution of clinical questions [ 38 ]. Topics covered in preoperative discussions may encompass various aspects, including preoperative evaluation, anesthetic considerations, anesthetic techniques, and conditions requiring heightened vigilance [ 6 ].

Being a role model for students increases the likelihood of creating a very desirable learning environment by 3.2 times. The findings of this study highlight the crucial role of clinical tutors in facilitating students’ socialization and the establishment of their professional identities as they embark on a career in anesthesia, consistent with the literature [ 39 , 40 , 41 , 42 ]. Students learn not only from formal instruction but also through the observation, imitation, and emulation of their teachers. Clinicians who serve as role models contribute significantly to students’ development of professional skills, values, and attitudes. Three key attributes define an effective role model: clinical skills, teaching abilities, and personal qualities [ 43 ]. The responsibility of the teacher is to exemplify and demonstrate various components of anesthesia practice, such as the psychomotor skills involved in endotracheal intubation or the judgment required for devising an anesthesia induction plan. Subsequently, the learner is encouraged to understand, practice, and apply these essential components repeatedly [ 6 ]. Teachers are responsible for exemplifying and demonstrating various components of anesthesia practice, such as psychomotor skills and judgment required for anesthesia induction plans. By consistently demonstrating these essential components, teachers encourage learners to understand, practice, and apply them repeatedly. This approach facilitates comprehensive skill acquisition and professional growth among students, ultimately enhancing the overall learning experience in anesthesia education.

The likelihood of achieving a very desirable learning environment increases by 3.5 times when all tutors respect anesthesia students. Within the operating room (OR), it is imperative for clinical educators to regard students with respect as individuals to facilitate effective learning. A study assessing various factors influencing learning experience revealed that the level of respect shown by clinical instructors toward anesthesia students had the strongest correlation with the creation of a very desirable learning environment, as ranked by students among 27 different factors [ 44 ]. Respecting anesthesia students not only fosters greater interaction between tutors and students but also plays a pivotal role in shaping a supportive and conducive atmosphere for learning within the OR. This culture of respect not only enhances the educational experience but also nurtures professional growth and development among students.

“Strict supervision of students during patient management in the operating room (OR) reduces the likelihood of creating a very desirable learning environment by 75%. This finding is supported by a study conducted at the Mayo Clinic, which indicated that strict supervision can significantly impede students’ desire to learn in the OR by impacting their decision-making abilities and confidence [ 45 ]. Anesthesia students may independently write orders for diagnostic studies and therapeutic interventions. The decision to adjust the instructor-to-student ratio from 1:1 to 1:2 or to allow instructors to leave the OR for periods of time depends on several factors: the student’s knowledge and ability, the patient’s physical status, the complexity of the anesthesia and/or surgical procedure, and the instructor’s experience [ 46 ]. The term “supervision” refers to a variety of activities, including being physically present during critical moments of a case, participating in anesthesia planning, providing clinical and educational guidance throughout the anesthetic, and granting autonomy to supervised individuals with feedback [ 45 ]. Future research in anesthesia education and training should focus on several key areas to enhance the learning environment and promote patient safety. This includes investigating the impact of specific teaching facilities and resources on student learning outcomes, optimal supervision practices in terms of instructor-to-student ratios and feedback methods, strategies for managing noise levels in the operating room, and the refinement of preoperative discussions between tutors and students.

Strengths and limitations of the study

Comprehensive data collection from primary sources across all Ethiopian higher education institution teaching hospitals contributes to the generalizability of the findings to the national population of anesthesia students in Ethiopian teaching hospitals. The potential for future research to utilize this study as a benchmark for assessing the learning environment of anesthetic operation theaters underscores its significance. The inherent limitations of cross-sectional studies include the inability to establish cause-and-effect relationships. There are possible biases in the data collection methods. Potential for confounding variables not accounted for in the study.

In conclusion, the findings of this study revealed that the highest frequency of perception regarding the operation theater learning environment in Ethiopia was deemed desirable by 141 participants (45.05%), with no study participants indicating that OTLEs in operation theaters were very undesirable. Factors strongly associated with the OTLE in the OR for anesthesia students include the lack of teaching facilities in the OR, the absence of tutors from the OR, noise from music played in the OR, tutors’ respect for their students, tutors serving as role models for their students, preoperative discussions with tutors, and the strict supervision of students.

Recommendation

The strict supervision of anesthesia students significantly affects the desirability of the operation theater environment. Therefore, instructor supervision should be tailored based on the student’s knowledge and ability, the patient’s physical status, the complexity of the anesthetic and/or surgical procedure, and the instructor’s experience. It is essential for all tutors to serve as role models for their students whenever possible. The three defining characteristics of a good role model are clinical skills, teaching abilities, and personal qualities. Therefore, efforts should be made to ensure that tutors exhibit these qualities in their interactions with students. Universities and anesthesia departments need to acknowledge the demanding nature of anesthesia operation theater practices. Consequently, they should collaborate to provide the necessary resources and teaching facilities to support effective teaching and learning in this environment. It may be beneficial to conduct additional studies that include managers and instructors as study participants. This would provide valuable insights into their perspectives on the operation theater learning environment and potential strategies for improvement.

Data availability

The data sets used and analyzed during the study are available from the corresponding author upon reasonable request.

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Abatneh Feleke Agegnehu, Misganaw Mengie Workie & Yonas Addisu

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Habtemariam Wubshet and M.M Workie contributed to the conception of the review and interpreted the kinds of literature based on the level of evidence and revised the manuscript. A.F Agegnehu and Yonas Addisu, participate in reviewing the preparation of the manuscript. Both authors participated in the preparation and critical review of the manuscripts. In addition, all authors read and approved the manuscript.

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Wubshet, H., Agegnehu, A.F., Workie, M.M. et al. Perception of the operation theater learning environment and related factors among anesthesia students in Ethiopian higher education teaching hospitals: a multicenter cross-sectional study. BMC Med Educ 24 , 303 (2024). https://doi.org/10.1186/s12909-024-05320-6

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In One Key A.I. Metric, China Pulls Ahead of the U.S.: Talent

China has produced a huge number of top A.I. engineers in recent years. New research shows that, by some measures, it has already eclipsed the United States.

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Paul Mozur reported from Taipei, Taiwan, and Cade Metz from San Francisco.

When it comes to the artificial intelligence that powers chatbots like ChatGPT, China lags behind the United States . But when it comes to producing the scientists behind a new generation of humanoid technologies, China is pulling ahead.

New research shows that China has by some metrics eclipsed the United States as the biggest producer of A.I. talent, with the country generating almost half the world’s top A.I. researchers. By contrast, about 18 percent come from U.S. undergraduate institutions, according to the study , from MacroPolo, a think tank run by the Paulson Institute, which promotes constructive ties between the United States and China.

The findings show a jump for China, which produced about one-third of the world’s top talent three years earlier. The United States, by contrast, remained mostly the same. The research is based on the backgrounds of researchers whose papers were published at 2022’s Conference on Neural Information Processing Systems. NeurIPS, as it is known, is focused on advances in neural networks , which have anchored recent developments in generative A.I.

The talent imbalance has been building for the better part of a decade. During much of the 2010s, the United States benefited as large numbers of China’s top minds moved to American universities to complete doctoral degrees. A majority of them stayed in the United States. But the research shows that trend has also begun to turn, with growing numbers of Chinese researchers staying in China.

What happens in the next few years could be critical as China and the United States jockey for primacy in A.I. — a technology that can potentially increase productivity, strengthen industries and drive innovation — turning the researchers into one of the most geopolitically important groups in the world.

Generative A.I. has captured the tech industry in Silicon Valley and in China, causing a frenzy in funding and investment. The boom has been led by U.S. tech giants such as Google and start-ups like OpenAI. That could attract China’s researchers, though rising tensions between Beijing and Washington could also deter some, experts said.

(The New York Times has sued OpenAI and Microsoft for copyright infringement of news content related to A.I. systems.)

China has nurtured so much A.I. talent partly because it invested heavily in A.I. education. Since 2018, the country has added more than 2,000 undergraduate A.I. programs, with more than 300 at its most elite universities, said Damien Ma, the managing director of MacroPolo, though he noted the programs were not heavily focused on the technology that had driven breakthroughs by chatbots like ChatGPT.

“A lot of the programs are about A.I. applications in industry and manufacturing, not so much the generative A.I. stuff that’s come to dominate the American A.I. industry at the moment,” he said.

While the United States has pioneered breakthroughs in A.I., most recently with the uncanny humanlike abilities of chatbots , a significant portion of that work was done by researchers educated in China.

Researchers originally from China now make up 38 percent of the top A.I. researchers working in the United States, with Americans making up 37 percent, according to the research. Three years earlier, those from China made up 27 percent of top talent working in the United States, compared with 31 percent from the United States.

“The data shows just how critical Chinese-born researchers are to the United States for A.I. competitiveness,” said Matt Sheehan, a fellow at the Carnegie Endowment for International Peace who studies Chinese A.I.

He added that the data seemed to show the United States was still attractive. “We’re the world leader in A.I. because we continue to attract and retain talent from all over the world, but especially China,” he said.

Pieter Abbeel, a professor at the University of California, Berkeley, and a founder of Covariant , an A.I. and robotics start-up, said working alongside large numbers of Chinese researchers was taken for granted inside the leading American companies and universities.

“It’s just a natural state of affairs,” he said.

In the past, U.S. defense officials were not too concerned about A.I. talent flows from China, partly because many of the biggest A.I. projects did not deal with classified data and partly because they reasoned that it was better to have the best minds available. That so much of the leading research in A.I. is published openly also held back worries.

Despite bans introduced by the Trump administration that prohibit entry to the United States for students from some military-linked universities in China and a relative slowdown in the flow of Chinese students into the country during Covid, the research showed large numbers of the most promising A.I. minds continued coming to the United States to study.

But this month, a Chinese citizen who was an engineer at Google was charged with trying to transfer A.I. technology — including critical microchip architecture — to a Beijing-based company that paid him in secret , according to a federal indictment.

The substantial numbers of Chinese A.I. researchers working in the United States now present a conundrum for policymakers, who want to counter Chinese espionage while not discouraging the continued flow of top Chinese computer engineers into the United States, according to experts focused on American competitiveness.

“Chinese scholars are almost leading the way in the A.I. field,” said Subbarao Kambhampati, a professor and researcher of A.I. at Arizona State University. If policymakers try to bar Chinese nationals from research in the United States, he said, they are “shooting themselves in the foot.”

The track record of U.S. policymakers is mixed. A policy by the Trump administration aimed at curbing Chinese industrial espionage and intellectual property theft has since been criticized for errantly prosecuting a number of professors. Such programs, Chinese immigrants said, have encouraged some to stay in China.

For now, the research showed, most Chinese who complete doctorates in the United States stay in the country, helping to make it the global center of the A.I. world. Even so, the U.S. lead has begun to slip, to hosting about 42 percent of the world’s top talent, down from about 59 percent three years ago, according to the research.

Paul Mozur is the global technology correspondent for The Times, based in Taipei. Previously he wrote about technology and politics in Asia from Hong Kong, Shanghai and Seoul. More about Paul Mozur

Cade Metz writes about artificial intelligence, driverless cars, robotics, virtual reality and other emerging areas of technology. More about Cade Metz

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Gov. Bill Lee of Tennessee signed a bill  to prevent the use of A.I. to copy a performer’s voice. It is the first such measure in the United States.

French regulators said Google failed to notify news publishers  that it was using their articles to train its A.I. algorithms, part of a wider ruling against the company for its negotiating practices with media outlets.

Apple is in discussions with Google  about using Google’s generative A.I. model called Gemini for its next iPhone.

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Victoria, Western Australia, Queensland, South Australia and Tasmania to get new study hubs to lift university enrolment in regions

Growing up in poverty in Morwell, in Victoria's Latrobe Valley, Jade Smith had big dreams of becoming not just the first person in her family to go university, but the first to get a doctorate.

"The idea of university was something that was really unattainable and quite fantastical … I didn't know anyone who had been to university," Ms Smith said.

Ms Smith always loved learning but at school noticed she missed out on things her peers took for granted, like new clothes, books or even school excursions.

"I had a single parent who wasn't able to work due to a lot of her own chronic health issues. Finances were, to put it bluntly, quite tight for most of my upbringing. It was difficult to even pay for food and bills let alone school supplies," she said.

Inspired though by her high-school history teacher, Ms Smith was accepted into one of Australia's most prestigious schools — the University of Melbourne.

Like many students from under-served groups, she faced new challenges amid the sandstone pillars.

It could be lonely and initially overwhelming as she learned the routines of university life on her own without the advice many students receive at home. As an undergrad, Jade worked four part-time jobs to pay her costs. She also received a scholarship from educational charity The Smith Family.

"We live in a country where access to educations remains a privilege. While affording textbooks might represent loose change for some, this isn't the case for the 700,000 Australian children that live in poverty," Ms Smith said.

Ms Smith is pursuing a masters degree and, while her dream of a doctorate is on track, she'd like to see more students with backgrounds like hers enrolled in higher education.

"I'm supportive of anything that can increase equity on campus and make university more inclusive," she said.

Education Minister Jason Clare will today announce $16 million to fund 10 new study hubs in Victoria, Western Australia, Queensland, South Australia and Tasmania to better support these students.

"I want more people to get a crack at going to university and that's what these hubs are all about," Mr Clare said.

"At the moment, almost half of young Australians in their 20s and 30s have a uni degree but that's not the case everywhere. It's certainly not the case in regional Australia."

The new sites, which will open in the next 12 months will be in East Arnhem Land (NT), Victor Harbor (SA), Warwick (Qld) Chinchilla (Qld), Innisfail (Qld), King Island (Tas), Katanning (WA), The Pilbara, Longreach (Qld) and East Gippsland (Vic).

Study hubs are designed to assist students who live a long way from campus, either in regional areas or the outer suburbs, and are currently assisting 4,000 students to get a degree.

They allow regional students to enrol in higher education from their home towns without moving away. The study hubs also attempt to replicate services available on campus that remote students would otherwise miss out on.

As well as computers, high-speed internet, video conferencing and small classrooms, students can get pastoral care and advice as they transition to higher education.

"These hubs are places where people can go to do their degree but also talk to people who've been there and done that and can help people to put their assignments together, but also help them get through the sometimes long and lonely process of getting a university degree," Mr Clare said.

Thirty-four existing study hubs were established by previous Coalition governments, and towns and outer suburban areas will soon be able to apply to host one of another 24 planned study hubs.

Study hubs are also popular overseas and studies have shown they can improve the high first-year attrition rates for new students trying to find their way on campus.

"Where we've got a hub like this in a small country town … the percentage of people doing a university degree goes up and the percentage of people who finish that degree goes up. So they work, they really have a big impact," Mr Clare said.

The announcement comes as the government considers its response to the final report of its Australian Universities Accord expert panel, which aims to more than double the number of new students from 860,000 to 1.8 million.

It recommended equity targets to dramatically boost the number of students from First Nations, low SES backgrounds and regional and remote areas.

The report also called on universities to provide more support for these students to improve their completion rates.

Ms Smith hopes things will be more inclusive for the next generation to blaze a trail in the nation's universities.

"It can be so challenging to be the first in your family to go to university and anything we can do to make it less overwhelming is welcome," Ms Smith said.

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How to Thrive as You Age

U.s. drops in new global happiness ranking. one age group bucks the trend.

Allison Aubrey

The U.S. ranks higher in the world happiness report when it comes to people aged 60 and older. Thomas Barwick/Getty Images hide caption

The U.S. ranks higher in the world happiness report when it comes to people aged 60 and older.

How happy are you? The Gallup World Poll has a simple way to gauge well-being around the globe.

Imagine a ladder, and think about your current life. The top rung, 10, represents the best possible life and the bottom rung, 0, represents the worst. Pick your number.

Researchers use the responses to rank happiness in countries around the globe, and the 2024 results have just been released.

This year, Finland is at the top of the list. Researchers point to factors including high levels of social support and healthy life expectancy, to explain the top perch of several Scandinavian countries.

Goats and Soda

Can a picture make you happy we asked photographers and here's what they sent us.

North America does not fare as well overall. As a nation, the United States dropped in the global ranking from 15th to 23rd. But researchers point to striking generational divides.

People aged 60 and older in the U.S. reported high levels of well-being compared to younger people. In fact, the United States ranks in the top 10 countries for happiness in this age group.

Conversely, there's a decline in happiness among younger adolescents and young adults in the U.S. "The report finds there's a dramatic decrease in the self-reported well-being of people aged 30 and below," says editor Jan-Emmanuel De Neve , a professor of economics and behavioral science, and the director of the Wellbeing Research Centre at Oxford University.

This drop among young adults is also evident in Canada, Australia and, to a lesser extent in parts of western Europe and Britain, too. "We knew that a relationship existed between age and happiness, but the biggest surprise is that it is more nuanced than we previously thought, and it is changing," says Ilana Ron-Levey , managing director at Gallup.

"In North America, youth happiness has dropped below that of older adults," Ron-Levey says. The rankings are based on responses from a representative sample of about 1,000 respondents in each country.

There are a range of factors that likely explain these shifts.

De Neve and his collaborators say the relatively high level of well-being among older adults is not too surprising. Researchers have long seen a U-shaped curve to happiness.

Children are typically happy, and people tend to hit the bottom (of the U) of well-being in middle age. By 60, life can feel more secure, especially for people with good health, financial stability and strong social connections. Living in a country with a strong social safety net can also help.

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Can little actions bring big joy researchers find 'micro-acts' can boost well-being.

"The big pressures in life, [such as] having small children, a mortgage to pay, and work, have likely tapered off a bit," De Neve says. But what's so unexpected he says is the extent to which well-being has fallen among young adults.

"We would expect youth to actually start out at a higher level of well-being than middle-age individuals," De Neve says.

"People are hearing that the world is going to hell in a handbasket and the young especially are feeling more threatened by it," says John Helliwell , Professor Emeritus at the University of British Columbia, and a co-author of the study.

He says many younger people may feel the weight of climate change, social inequities, and political polarization which can all be amplified on social media.

But hope is not lost, Helliwell says.

He points to countries in eastern Europe where levels of well-being are on the rise among young people.

He says the older generations in the countries that make up the former Yugoslavia, tend to be less happy. "They are bearing the scars of genocide and conflict," he says.

But he says the younger people are looking beyond this history. "A new generation can put it in the past and think of building a better future and feel that they can be part of that," Helliwell says.

Stuck In A Rut? Sometimes Joy Takes A Little Practice

This story was edited by Jane Greenhalgh

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