Generative AI in higher education psychology programs: a scoping review exploring the opportunities for its use in assessment methods
Open accessAbstract
Objective The current literature on Generative Artificial Intelligence (GenAI) in tertiary settings primarily focuses on the risk it poses to academic integrity, and ways to reduce or remove GenAI use in assessments. As psychology graduates enter a workforce where GenAI is present, educators need to prepare students to use GenAI responsibly. This scoping review aims to assess the current state of knowledge in tertiary psychology regarding opportunities for integrating GenAI into assessment methods.Method A comprehensive literature search identified four studies for inclusion. These were published in Australia, Canada, Switzerland, and the United States, and included two quantitative case studies, a mixed-method case study, and a pedagogical case study.Results Three themes were generated: 1) GenAI can be used as an effective psychology tutor, 2) GenAI can be used for authentic assessment in undergraduate psychology, and 3) Critiquing GenAI as a form of assessment can enhance student learning and AI literacy.Conclusions Only four studies were identified, but all indicate that GenAI can be meaningfully incorporated into psychology assessments. However, this is an underdeveloped area and ongoing research with a particular focus on developing evidence-based assessment methods which adapt to the evolving GenAI landscape is needed.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Insufficiently clear demonstrations, explanations, or alternative pathways in courses may cause students to perceive evidence requirements as additional burdens, thereby increasing stress to some extent.
“insufficiently clear demonstrations, explanations, or alternative pathways in courses may cause students to perceive evidence requirements as additional burdens, thereby increasing stress to some extent (Bittle and El-Gayar, 2025; Halliday et al., 2025).”
From Multidimensional responses to generative AI in higher education: an integrative framework linking institutional governance, teaching practice, and student psychological adaptation (2026) · cites this paper as Halliday et al., 2025
Cites
The PRISMA 2020 statement: An updated guideline for reporting systematic reviews
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., et al. (2021) · BMJ · doi:10.1136/bmj.n71 · Cited by 108,283 · see citing works
Technological Pedagogical Content Knowledge: A Framework for Teacher Knowledge
Mishra, P., & Koehler, M. J. (2006) · Teachers College Record: The Voice of Scholarship in Education · doi:10.1111/j.1467-9620.2006.00684.x · Cited by 12,604 · see citing works
Munn, Z., Peters, M. D. J., Stern, C., Tufanaru, C., McArthur, A., & Aromataris, E. (2018) · BMC Medical Research Methodology · doi:10.1186/s12874-018-0611-x · Cited by 11,385 · cited 1× · see citing works
“Scoping reviews are an ideal tool for determining the scope of a body of literature on a given topic, identifying underlying key concepts, and the exploratory nature informs areas for subsequent research (Munn et al., 2018).”
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., et al. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2023.102642 · Cited by 4,584 · see citing works
Chatting and cheating: Ensuring academic integrity in the era of ChatGPT
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024) · Innovations in Education and Teaching International · doi:10.1080/14703297.2023.2190148 · Cited by 3,087 · see citing works
Students’ voices on generative AI: perceptions, benefits, and challenges in higher education
Chan, C. K. Y., & Hu, W. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00411-8 · Cited by 2,301 · cited 1× · see citing works
“These findings mirror those from other disciplines, and recognise the benefits of immediate and tailored feed-back, improved academic performance, and the ligh-tened administrative load for educators (Al-Abri, 2025; Celik et al., 2022; C. Chan & Hu, 2023).”
Popay, J., Roberts, H., Sowden, A., Petticrew, M., Arai, L., Rodgers, M., Britten, N., Roen, K., & Duffy, S. (2006) · Lancaster University · doi:10.13140/2.1.1018.4643 · Cited by 2,070 · see citing works
A comprehensive AI policy education framework for university teaching and learning
Chan, C. K. Y. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00408-3 · Cited by 1,577 · cited 1× · see citing works
“…universities where the use of GenAI for assignments is classified as academic misconduct, some Australian universities have reverted to paper-based exams to eliminate GenAI use entirely, and others have revised plagiarism policies as GenAI raises academic integrity concerns (C. K. Y. Chan, 2023).”
Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100118 · Cited by 1,181 · cited 1× · see citing works
“…focus from risks to opportunities of GenAI use in psychology assessments, as risks had been comprehensively addressed for tertiary education (please see Chiu et al. 2023) for a detailed review), the limited number of findings indicates that this field is presently lacking a strong knowledge base.”
The promises and challenges of artificial intelligence for teachers: A systematic review of research
Celik, I., Dindar, M., Muukkonen, H., & Järvelä, S. (2022) · TechTrends · doi:10.1007/s11528-022-00715-y · Cited by 984 · see citing works
Focus Article: On the Structure of Educational Assessments
Mislevy, R. J., Steinberg, L. S., & Almond, R. G. (2003) · Measurement Interdisciplinary Research and Perspectives · doi:10.1207/s15366359mea0101_02 · Cited by 841 · see citing works
Crawford, J., Cowling, M., & Allen, K.-A. (2023) · Journal of University Teaching and Learning Practice · doi:10.53761/1.20.3.02 · Cited by 547 · cited 4× · see citing works
“A key concern for educators regarding the use of GenAI in higher education is its impact on student learning, particularly in the context of student assessments (Crawford et al., 2023).”
InfluentialA Scoping Review on How Generative Artificial Intelligence Transforms Assessment in Higher Education
Xia, Q., Weng, X., Ouyang, F., Lin, T. J., & Chiu, T. K. F. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00468-z · Cited by 493 · see citing works
Yu, H. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1181712 · Cited by 409 · cited 2× · see citing works
“As graduates enter workplaces where GenAI is readily available, educational settings should equip students with an understanding of the strengths, limitations, and the biases of these tools (Johnston et al., 2024; Yu, 2023).”
Lee, D., Arnold, M., Srivastava, A., Plastow, K., Strelan, P., Ploeckl, F., Lekkas, D., & Palmer, E. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100221 · Cited by 402 · cited 2× · see citing works
“It has the capacity to perform a wide range of language tasks including question answering, text generation, translation, and summarisation to a sophisticated standard (Brown et al., 2020; Lee et al., 2024).”
Johnston, H., Wells, R. F., Shanks, E. M., Boey, T., & Parsons, B. N. (2024) · International Journal for Educational Integrity · doi:10.1007/s40979-024-00149-4 · Cited by 363 · see citing works
High Rates of Fabricated and Inaccurate References in ChatGPT-Generated Medical Content
Bhattacharyya, M., Miller, V. M., Bhattacharyya, D., & Miller, L. E. (2023) · Cureus · doi:10.7759/cureus.39238 · Cited by 294 · see citing works
Sotiriadou, P., Logan, D., Daly, A., & Guest, R. (2020) · Studies in Higher Education · doi:10.1080/03075079.2019.1582015 · Cited by 265 · see citing works
Kizilcec, R. F., Huber, E., Papanastasiou, E. C., Cram, A., Makridis, C. A., Smolansky, A., Zeivots, S., & Raduescu, C. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100269 · Cited by 150 · see citing works
Effective learning with a personal AI tutor: A case study
Baillifard, A., Gabella, M., Lavenex, P. B., & Martarelli, C. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12888-5 · Cited by 90 · see citing works
Newton, P., & Xiromeriti, M. (2024) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2023.2299059 · Cited by 86 · see citing works
Leveraging Large Language Models to Power Chatbots for Collecting User Self-Reported Data
Wei, J., Kim, S., Jung, H., & Kim, Y.-H. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3637364 · Cited by 84 · see citing works
Editorial: Generative artificial intelligence as a plagiarism problem
Dien, J. (2023) · Biological Psychology · doi:10.1016/j.biopsycho.2023.108621 · Cited by 53 · see citing works
Salah, M., Abdelfattah, F., & Halbusi, H. A. (2024) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2024.101872 · Cited by 47 · see citing works
Al-Abri, A. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13484-x · Cited by 44 · see citing works
Kohnke, L., & Zou, D. (2025) · TESOL Quarterly · doi:10.1002/tesq.3396 · Cited by 44 · see citing works
Students' perception of the use of artificial intelligence (AI) in pharmacy school
Knobloch, J., Cozart, K., Halford, Z., Hilaire, M. L., Richter, L. M., & Arnoldi, J. D. (2024) · Currents in Pharmacy Teaching and Learning · doi:10.1016/j.cptl.2024.102181 · Cited by 34 · see citing works
Psychological literacy: proceed with caution, construction ahead
Murdoch, D. D. (2016) · Psychology Research and Behavior Management · doi:10.2147/prbm.s88646 · Cited by 31 · see citing works
Roche, R. A. P., Mullally, S. L., McNulty, J. P., Hayden, J., Brennan, P., Doherty, C. P., Fitzsimons, M. B., McMackin, D., Prendergast, J., Sukumaran, S., et al. (2009) · BMC Neuroscience · doi:10.1186/1471-2202-10-136 · Cited by 24 · see citing works
Richmond, J. L., & Nicholls, K. (2024) · Teaching of Psychology · doi:10.1177/00986283241287203 · Cited by 18 · see citing works
Authentic and Creative Assessment in a World with AI
Miserandino, M. (2025) · Teaching of Psychology · doi:10.1177/00986283241260370 · Cited by 15 · see citing works
Employing the interactive oral to mitigate threats to academic integrity from ChatGPT.
Newell, S. J. (2023) · Scholarship of Teaching and Learning in Psychology · doi:10.1037/stl0000371 · Cited by 13 · see citing works
The Efficacy of GenAI Tools in Postsecondary Education
Chambers, L., & Owen, W. J. (2024) · Brock Education Journal · doi:10.26522/brocked.v33i3.1178 · Cited by 10 · see citing works
Student perspectives on AI in Higher Education: Student Survey
Chung, J., Henderson, M., Pepperell, N., Slade, C., & Liang, Y. (2024) · Monash University · doi:10.26180/27915930 · Cited by 7 · see citing works
Comparing ChatGPT With Experts’ Responses to Scenarios that Assess Psychological Literacy
Machin, M. A., Machin, T. M., & Gasson, N. (2024) · Psychology Learning & Teaching · doi:10.1177/14757257241241592 · Cited by 6 · see citing works
Doing Assessment Well: Advances for Undergraduate Psychology Programs and Psychology Educators
Dunn, D. S., Coffman, K., Bhalla, M., Boysen, G. A., Diaz‐Granados, J. L., McGregor, L. N., Morgan, B. L., & Smith, P. A. (2020) · Teaching of Psychology · doi:10.1177/0098628320945097 · Cited by 6 · see citing works
Undergraduate Psychological Writing
Ishak, S., & Salter, N. P. (2016) · Teaching of Psychology · doi:10.1177/0098628316677491 · Cited by 6 · see citing works
Leading the Future of Learning. Proceedings of the Innovating Higher Education Conference 2024.
European Association of Distance Teaching Universities (2024) · University of Southern Denmark Research Portal (University of Southern Denmark) · doi:10.5281/zenodo.14215069 · Cited by 2 · see citing works
Stevens, A., & Bernier, R. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1698-3_750 · Cited by 2 · see citing works
Halliday, S., Callaghan, P., Lavis, T., & Chur‐Hansen, A. (2024) · Australian Journal of Psychology · doi:10.1080/00049530.2024.2395521 · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Kuang, W., & Gao, X. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1789506 · Cited by 1 · see citing works
“…to develop more standardized usage habits, insufficiently clear demonstrations, explanations, or alternative pathways in courses may cause students to perceive evidence requirements as additional burdens, thereby increasing stress to some extent (Bittle and El-Gayar, 2025; Halliday et al., 2025).”
Rachiotis, T., Tassi, A., Papanikolaou, I., & Adamakis, M. (2026) · Encyclopedia · doi:10.3390/encyclopedia6100224
- Authors
- Halliday, S., Lavis, T., Callaghan, P., & Chur-Hansen, A.
- Affiliations
- The University of Adelaide (AU)
- The University of Adelaide
- Corresponding author
- Halliday, Sarah
- Venue
- Australian Psychologist
- Publisher
- Informa UK Limited
- Published
- 2025-11-02
- Volume
- 61
- Issue
- 2
- Pages
- 115-126
- ISSN
- 0005-0067 (ISSN-L)
- 1742-9544
- OpenAlex
- W4415776941
- Cited by
- 2
- References
- 40
- Citations by year
- 2026: 2
- FWCI
- 2.10
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Australian Psychologist (publishedVersion, cc-by-nc-nd)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Keywords
- generative AI, higher education, psychology education, assessment methods, authentic assessment, AI tutors, AI literacy, academic integrity, responsible AI use
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
