Generative AI in higher education psychology programs: a scoping review exploring the opportunities for its use in assessment methods
Bibliographic record
Abstract
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. 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. 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. 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. What is already known about this topic:Generative Artificial Intelligence (GenAI) has disrupted the education sector, with educators re-evaluating their teaching practices and tertiary institutions revising their policies.Both educators and students use and appreciate the benefits of GenAI for administrative tasks and reducing cognitive load; however, there are ongoing challenges and concerns about its role in student assessment tasks.In Australian psychology programs, where students must meet specific standards under accreditation, educators have implemented bans or adopted alternative assessment methods that minimise or completely remove its capability. Generative Artificial Intelligence (GenAI) has disrupted the education sector, with educators re-evaluating their teaching practices and tertiary institutions revising their policies. Both educators and students use and appreciate the benefits of GenAI for administrative tasks and reducing cognitive load; however, there are ongoing challenges and concerns about its role in student assessment tasks. In Australian psychology programs, where students must meet specific standards under accreditation, educators have implemented bans or adopted alternative assessment methods that minimise or completely remove its capability. What this topic adds:This scoping review is the first to report the findings of a comprehensive search that focus on the opportunities of integrating GenAI into tertiary psychology student assessment methods.The findings offer examples and suggestions for using GenAI as a personal tutor, incorporating and critically evaluating GenAI to develop students’ artificial intelligence literacy, and illustrating how it can be drawn on to develop authentic assessments.We highlight opportunities for future research, given the dearth of work to date, focusing on the need to increase the evidence-base and for designing adaptable assessment methods that evolve with GenAI. This scoping review is the first to report the findings of a comprehensive search that focus on the opportunities of integrating GenAI into tertiary psychology student assessment methods. The findings offer examples and suggestions for using GenAI as a personal tutor, incorporating and critically evaluating GenAI to develop students’ artificial intelligence literacy, and illustrating how it can be drawn on to develop authentic assessments. We highlight opportunities for future research, given the dearth of work to date, focusing on the need to increase the evidence-base and for designing adaptable assessment methods that evolve with GenAI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".