‘Where’s the line? It’s an absurd line’: Towards a framework for acceptable uses of AI in assessment
Open accessAbstract
As higher education grapples with ensuring assessment validity in an increasingly AI-populated time, institutions and educators are working to establish appropriate boundaries for AI use. However, little is known about how students and teachers conceptualize and experience these boundaries in practice. This study investigates how students and teachers navigate the line between acceptable and unacceptable AI use in assessment, drawing on a thematic analysis of qualitative interviews with 19 students and 12 staff at a large Australian university informed by social boundary theory. The titular metaphor of ‘drawing a line’ emerged organically from both students and staff in our interviews, revealing ongoing struggles to understand and articulate what counts as appropriate. We found that students frequently construct their own individually unique and often complex ethical frameworks for AI use. Teachers, meanwhile, report significant emotional burden and professional uncertainty as they attempt to understand and communicate what is appropriate to their students. Our analysis suggests that assessment policies for AI ought to move beyond simple prohibitions or permissions and begin to address three critical dimensions: the feasibility of enforcement, the preservation of authentic learning, and the emotional wellbeing of teachers and students.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The current body of knowledge on interventions to support ethical and effective generative AI integration in education is fragmented.
“Although some studies have proposed interventions to support ethical and effective generative AI integration in education (e.g., An et al., 2025; Barus et al., 2025; Corbin et al., 2025), the current body of knowledge is fragmented.”
From Six Institutional Intervention Areas to Support Ethical and Effective Student Use of Generative AI in Higher Education: A Narrative Review (2026) · cites this paper as Corbin et al., 2025
Assessment policy should attend to enforcement feasibility and authentic learning rather than simple prohibition.
“Corbin et al. (2025) similarly argue that assessment policy should attend to enforcement feasibility and authentic learning rather than simple prohibition”
From From AI Literacy to AI Fluency: A Developmental Framework for Higher Education (2026) · cites this paper as Corbin et al., 2025
Responsible AI use modules are being developed to encourage reflective rather than passive engagement, often incorporating real-world tasks.
“responsible AI use modules are being developed to encourage reflective rather than passive engagement, often incorporating real-world tasks to guide appropriate usage (Acosta-Enriquez et al., 2024a; Alharbi, 2024; Corbin et al., 2025).”
From Six Institutional Intervention Areas to Support Ethical and Effective Student Use of Generative AI in Higher Education: A Narrative Review (2026) · cites this paper as Corbin et al., 2025
The Discussion proposes embedding clear AI-use expectations in assessments to support meaningful learning, but it does not describe responsible-AI modules being developed. Searches for modules, reflective engagement, and real-world tasks found no passage establishing those features. (LLM, conf=0.74)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Using thematic analysis in psychology
Braun, V., & Clarke, V. (2006) · Qualitative Research in Psychology · doi:10.1191/1478088706qp063oa · Cited by 194,414 · see citing works
The Study of Boundaries in the Social Sciences
Lamont, M., & Molnár, V. (2002) · Annual Review of Sociology · doi:10.1146/annurev.soc.28.110601.141107 · Cited by 5,186 · see citing works
Gieryn, T. F. (1983) · American Sociological Review · doi:10.2307/2095325 · Cited by 4,838 · see citing works
Cultural boundaries of science: Credibility on the line
(1999) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(99)90325-5 · Cited by 1,794 · see citing works
Boundary Work among Groups, Occupations, and Organizations: From Cartography to Process
Langley, A., Lindberg, K., Mørk, B. E., Nicolini, D., Raviola, E., & Walter, L. (2019) · Academy of Management Annals · doi:10.5465/annals.2017.0089 · Cited by 430 · see citing works
Luo (Jess), J. (2024) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2024.2309963 · Cited by 366 · cited 1× · see citing works
“…validity, such as essay mills or contract cheating services, involved clear substitution of student work, GenAI blurs the line between acceptable assistance and inappropriate delegation at every stage of the assessment process – from ideation through to final editing (Luo 2024; Corbin et al. 2024).”
Perkins, M., Furze, L., Roe, J., & MacVaugh, J. (2024) · Journal of University Teaching and Learning Practice · doi:10.53761/q3azde36 · Cited by 291 · cited 1× · see citing works
“Numerous frameworks have emerged to help educators adapt to this new AI populated reality, frameworks which aim to articulate boundaries between acceptable and unacceptable uses of AI (see for example, Perkins et al. 2024; Davis 2024).”
Boundary-work between work and life in the high-speed university
Ylijoki, O.-H. (2011) · Studies in Higher Education · doi:10.1080/03075079.2011.577524 · Cited by 192 · see citing works
Validity matters more than cheating
Dawson, P., Bearman, M., Dollinger, M., & Boud, D. J. (2024) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2024.2386662 · Cited by 177 · see citing works
Self-citationGenerative AI and higher education: a review of claims from the first months of ChatGPT
Jensen, L. X., Buhl, A., Sharma, A., & Bearman, M. (2025) · Higher Education · doi:10.1007/s10734-024-01265-3 · Cited by 144 · see citing works
Academic Integrity and Artificial Intelligence: An Overview
Kumar, R., Eaton, S. E., Mindzak, M., & Morrison, R. (2024) · Springer international handbooks of education · doi:10.1007/978-3-031-54144-5_153 · Cited by 69 · see citing works
Lombi, L., & Rossero, E. (2023) · Sociology of Health & Illness · doi:10.1111/1467-9566.13702 · Cited by 55 · see citing works
Risan, M. (2021) · Teaching and Teacher Education · doi:10.1016/j.tate.2021.103559 · Cited by 32 · see citing works
It Takes Work: How University Students Manage Role Boundaries when the Future is Calling
Eastgate, L., Creed, P. A., Hood, M., & Bialocerkowski, A. E. (2023) · Research in Higher Education · doi:10.1007/s11162-023-09736-9 · Cited by 12 · see citing works
Inclusion, Cheating, and Academic Integrity
Dawson, P. (2022) · doi:10.4324/9781003293101-13 · Cited by 9 · see citing works
Self-citationReading at university in the time of GenAI
Corbin, T., Liang, Y., Bearman, M., et al. (2024) · Learning Letters · doi:10.59453/ll.v3.35 · Cited by 9 · see citing works
Self-citationThe Politics of Policing Hate: Boundary Work, Social Inequalities, and Legitimacy
Solhjell, R., & Klatran, H. K. (2023) · Social & Legal Studies · doi:10.1177/09646639231201912 · Cited by 8 · see citing works
Digital boundary work and work-to-family spillover in Europe: examining the role of digital skills
Christensen, M. A., & Treas, J. (2024) · Community Work & Family · doi:10.1080/13668803.2024.2412700 · Cited by 8 · see citing works
Supporting inclusion in academic integrity in the age of GenAI
Davis, M. (2024) · Routledge eBooks · doi:10.4324/9781003482918-4 · Cited by 2 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Talk is cheap: why structural assessment changes are needed for a time of GenAI
Corbin, T., Dawson, P., & Liu, D. (2025) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2025.2503964 · Cited by 107 · see citing works
Self-citationThe wicked problem of AI and assessment
Corbin, T., Bearman, M., Boud, D., & Dawson, P. (2026) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2025.2553340 · Cited by 93 · see citing works
Self-citationUnderstanding the place and value of GenAI feedback: a recognition-based framework
Corbin, T., Tai, J., & Flenady, G. (2025) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2025.2459641 · Cited by 73 · see citing works
Self-citationElshall, A. S., & Badir, A. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1596462 · Cited by 28 · see citing works
Walton, J., Bearman, M., Crawford, N., Tai, J., & Boud, D. (2026) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2025.2570328 · Cited by 28 · see citing works
“For example, they must judge, in the current absence of clear guidance, when it is appropriate to use GenAI outputs within their assessments (Corbin et al. 2025).”
InfluentialHeads we win, tails you lose: AI detectors in education
Bassett, M. A., Bradshaw, W., Bornsztejn, H., Hogg, A., Murdoch, K., Pearce, B., & Webber, C. (2026) · Journal of Higher Education Policy and Management · doi:10.1080/1360080x.2026.2622146 · Cited by 24 · see citing works
Kangwa, D., Msafiri, M. M., & Fute, A. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70109 · Cited by 22 · see citing works
Jayasinghe, S., Gamage, K. A. A., Yang, D., Cheng, C., Disanayake, C., & Apeji, U. D. (2026) · Education Sciences · doi:10.3390/educsci16010137 · Cited by 23 · see citing works
Luo, J. (., & Dawson, P. (2026) · Studies in Higher Education · doi:10.1080/03075079.2025.2552825 · Cited by 17 · see citing works
Self-citationGenerative AI use and misuse call for assessment reform in higher education
Chirikov, I., Smirnov, I., & Kizilcec, R. F. (2026) · Science · doi:10.1126/science.aec5115 · Cited by 16 · see citing works
Thinking to learn: managing the risks of outsourcing to GenAI
Chase, A.-M., & Galvin, K. (2026) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2026.2620055 · Cited by 8 · see citing works
Encheva, A., van Es, K., & Nguyen, D. (2025) · Learning, Media and Technology · doi:10.1080/17439884.2025.2578625 · Cited by 1 · see citing works
From AI Literacy to AI Fluency: A Developmental Framework for Higher Education
Zentner, A. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7014818
Lo, S., Lea, T., Mavromoustakos, A., Joseph, B., Yii, M., Exintaris, B., Karunaratne, N., Pearson, D. L., Ritchie, T., Yuriev, E., et al. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1818781
- Authors
- Corbin, T., Dawson, P., Nicola-Richmond, K., & Partridge, H.
- Affiliations
- Deakin University (AU)
- Deakin University
- Corresponding author
- Corbin, Thomas
- Venue
- Assessment & Evaluation in Higher Education
- Publisher
- Informa UK Limited
- Published
- 2025-01-24
- Volume
- 50
- Issue
- 5
- Pages
- 705-717
- ISSN
- 0260-2938 (ISSN-L)
- 1469-297X
- OpenAlex
- W4406796099
- Cited by
- 96
- References
- 19
- Citations by year
- 2026: 70 · 2025: 26
- FWCI
- 99.82
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Assessment & Evaluation in Higher Education (publishedVersion, cc-by-nc-nd)
- Figshare (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Social Sciences › Education › Student Assessment and Feedback
- Keywords
- AI-assisted assessment, assessment validity, student perceptions of AI, teacher perceptions of AI, AI ethics, assessment policy, authentic learning, professional uncertainty, higher education
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
