The wicked problem of AI and assessment
Open accessAbstract
Generative artificial intelligence (GenAI) has created significant assessment challenges in higher education. Universities and teachers have responded to these in various ways, including by way of new policies, revised assessment formats, and technological safeguards. These responses are typically predicated on an assumption that AI in assessment is a problem that can be solved if only the right approach can be found. However, in this paper we argue that GenAI may not be that kind of problem at all. Drawing on interviews with 20 teachers responsible for assessment design within a large Australian University, this paper applies Rittel and Webber framework of wicked problems to analyse educators’ experiences with assessment design in the context of GenAI. Our findings demonstrate that the GenAI-assessment challenge exhibits all ten characteristics of wicked problems. For instance, it resists definitive formulation, offers only better or worse rather than correct solutions, cannot be tested without consequence, and places significant responsibility on decision-makers. In the light of this redefinition of the AI and Assessment problem, we argue that educators require certain institutional permissions – including permission to compromise, diverge, and iterate – to appropriately navigate the assessment challenges they face.
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Researchers studying genAI in higher education have used deductive coding when initial readings of data have suggested the applicability of a particular model.
“Researchers studying genAI in higher education have used deductive coding when initial readings of data have suggested the applicability of a particular model (e.g., Corbin et al., 2025).”
From Toward Partnership Ethics for Generative Artificial Intelligence in Higher Education (2026) · cites this paper as Corbin et al., 2025
In these conversations, genAI (non)use became not a problem to be solved but a condition to be navigated.
“In these conversations, genAI (non)use became not a problem to be solved but”
From Toward Partnership Ethics for Generative Artificial Intelligence in Higher Education (2026) · cites this paper as Corbin et al., 2025
Workload concerns are valid.
“while workload concerns are valid (Corbin et al., 2025a)”
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., 2025a
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- Authors
- Corbin, T., Bearman, M., Boud, D., & Dawson, P.
- Affiliations
- Deakin University (AU)
- Corresponding author
- Corbin, Thomas
- Venue
- Assessment & Evaluation in Higher Education
- Publisher
- Informa UK Limited
- Published
- 2025-09-03
- Volume
- 51
- Issue
- 4
- Pages
- 736-752
- ISSN
- 0260-2938 (ISSN-L)
- 1469-297X
- OpenAlex
- W4413999893
- Cited by
- 93
- References
- 26
- Citations by year
- 2026: 86 · 2025: 7
- FWCI
- 97.50
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Assessment & Evaluation in Higher Education (publishedVersion, cc-by-nc-nd)
- OPUS - Open Publications of UTS Scholars (University of Technology Sydney) (submittedVersion, cc-by-nc-nd)
- Figshare (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Education › Student Assessment and Feedback
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Keywords
- generative AI, AI-assisted assessment, wicked problems, assessment design, higher education assessment, teacher experiences, test format
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.
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