Talk is cheap: why structural assessment changes are needed for a time of GenAI
Open accessAbstract
Generative AI (GenAI) challenges assessment validity by enabling students to complete tasks without demonstrating genuine capability. In response to this challenge, institutions have developed and implemented various approaches that aim to communicate permissible AI use to students. Familiar examples include the ‘traffic light’ approach now commonly found within institutional policy. While well-intentioned, these approaches share a common limitation: They focus primarily on communicating rules rather than redesigning assessment mechanics. To clarify why such approaches fail, and to guide more effective responses, this paper introduces a novel conceptual distinction between discursive changes to assessment (modifications relying solely on instructions students remain free to ignore) and structural changes (modifications that reshape the underlying mechanics of assessment tasks themselves). Through a critical analysis of prominent frameworks, we demonstrate that current approaches predominantly rely on discursive changes that create what we term an ‘enforcement illusion’. We find that educational frameworks frequently borrow the language of socially familiar structural systems (like vehicular traffic lights) while lacking their actual enforcement capabilities, creating an illusion of assessment security. In place of this, we argue for a shift towards structural assessment redesign that builds validity into assessment architecture rather than attempting to impose it through unen…
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Reliance on discursive measures such as traffic-light systems and GenAI-use declarations signals expectations without shaping students' approaches to assessment tasks.
“This limitation is further reflected in the reliance on discursive measures, such as traffic-light systems and GenAI-use declarations, which signal expectations without shaping students' approaches to assessment tasks (Corbin et al., 2025).”
From Beyond unstructured use: scaffolded integration of generative AI in assessment for learning (2026) · cites this paper as Corbin et al., 2025
The proposed course aligns with structural redesign recommendations in AI-assessment literature.
“This aligns with structural redesign recommendations in AI-assessment literature (Corbin et al., 2025b).”
From OTE Reconstructing the Assessment System of the Undergraduate Course “Plant Physiology” in the AI Era: A Framework for Authentic and AI-Resilient Task Design (2026) · cites this paper as Corbin et al., 2025b
Cites
Sustainable Assessment: Rethinking assessment for the learning society
Boud, D. (2000) · Studies in Continuing Education · doi:10.1080/713695728 · Cited by 1,545 · see citing works
Perkins, M., Furze, L., Roe, J., & MacVaugh, J. (2024) · Journal of University Teaching and Learning Practice · doi:10.53761/q3azde36 · Cited by 291 · cited 2× · see citing works
“However, this approach would only be viable if reliable AI detection technology existed, which it currently does not (Perkins et al. 2024b; Ardito 2024).”
Dalalah, D., & Dalalah, O. M. A. (2023) · The International Journal of Management Education · doi:10.1016/j.ijme.2023.100822 · Cited by 218 · 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-citationGonsalves, C. (2025) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2024.2415654 · Cited by 121 · cited 1× · see citing works
“…that up to 74% of students did not complete the AI declaration appropriately, with many fearing that admitting AI use might be perceived negatively (Gonsalves 2024). many universities, including Cambridge and Princeton take a stricter approach, classifying unde-clared AI use as a form of…”
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Perkins, M., Roe, J., Vu, B. H., Postma, D., Hickerson, D., McGaughran, J., & Khuat, H. Q. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00487-w · Cited by 117 · see citing works
Nikolic, S., Wentworth, I., Sheridan, L., Moss, S., Duursma, E., Jones, R. A., Ros, M., & Middleton, R. (2024) · Australasian Journal of Educational Technology · doi:10.14742/ajet.9643 · Cited by 102 · see citing works
‘Where’s the line? It’s an absurd line’: Towards a framework for acceptable uses of AI in assessment
Corbin, T., Dawson, P., Nicola-Richmond, K., & Partridge, H. (2025) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2025.2456207 · Cited by 96 · see citing works
Self-citationValidity: one word with a plurality of meanings
St‐Onge, C., Young, M. E., Eva, K. W., & Hodges, B. D. (2016) · Advances in Health Sciences Education · doi:10.1007/s10459-016-9716-3 · Cited by 91 · see citing works
How university teachers design assessments: a cross-disciplinary study
Bearman, M., Dawson, P., Bennett, S., Hall, M., Molloy, E., Boud, D. J., & Joughin, G. (2016) · Higher Education · doi:10.1007/s10734-016-0027-7 · Cited by 91 · see citing works
Self-citationGenerative AI detection in higher education assessments
Ardito, C. G. (2025) · New Directions for Teaching and Learning · doi:10.1002/tl.20624 · Cited by 86 · cited 1× · see citing works
“However, this approach would only be viable if reliable AI detection technology existed, which it currently does not (Perkins et al. 2024b; Ardito 2024).”
Evading Watermark based Detection of AI-Generated Content
Jiang, Z., Zhang, J., & Gong, N. Z. (2023) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3576915.3623189 · Cited by 67 · see citing works
Scaling Up Assessment for Learning: Progress and Prospects
Carless, D. R. (2016) · The enabling power of assessment · doi:10.1007/978-981-10-3045-1_1 · Cited by 66 · see citing works
The AI Assessment Scale Revisited: A Framework for Educational AssessmentPreprint
Perkins, M., Roe, J., & Furze, L. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2412.09029 · Cited by 7 · see citing works
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Self-citationHeads 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
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Christensen, I. F., Dohn, N. B., & Christensen, L. L. W. (2025) · Postdigital Science and Education · doi:10.1007/s42438-025-00583-8 · Cited by 19 · see citing works
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Kickbusch, S., Ashford-Rowe, K., Kemp, A., Boreland, J., & Huijser, H. (2025) · Education Sciences · doi:10.3390/educsci15111537 · Cited by 17 · see citing works
Ahangama, N. (2026) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-026-00582-0 · Cited by 21 · see citing works
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Seung, Y., & Basham, J. D. (2026) · Learning Disability Quarterly · doi:10.1177/07319487261439132 · Cited by 12 · see citing works
DeLuca, C., Volante, L., & Holden, M. (2025) · Educational Researcher · doi:10.3102/0013189x251385537 · Cited by 8 · see citing works
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Delvenne, J. (2026) · Psychology Learning & Teaching · doi:10.1177/14757257261427151 · Cited by 1 · see citing works
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Garcia, P. S., & Wandercil, M. (2026) · EccoS – Revista Científica · doi:10.5585/2026.29428
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- Authors
- Corbin, T., Dawson, P., & Liu, D.
- Affiliations
- Deakin University (AU)
- The University of Sydney (AU)
- Corresponding author
- Corbin, Thomas
- Venue
- Assessment & Evaluation in Higher Education
- Publisher
- Informa UK Limited
- Published
- 2025-05-15
- Volume
- 50
- Issue
- 7
- Pages
- 1087-1097
- ISSN
- 0260-2938 (ISSN-L)
- 1469-297X
- OpenAlex
- W4410403683
- Cited by
- 107
- References
- 14
- Citations by year
- 2026: 89 · 2025: 18
- FWCI
- 111.30
- 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
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Education › Student Assessment and Feedback
- Keywords
- generative AI, assessment validity, discursive change, AI policy in higher education, student use of AI
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 ›
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