Beyond the ban
A theoretical framework for integrating Generative AI in assessment
Bibliographic Data
| ID | 21728059 |
|---|---|
| Authors | Beth L Chapman (0000-0001-5413-0159, University of Canberra, corresponding author), Barney Dalgarno (0000-0002-6935-6844, University of Canberra) |
| Year | 2025 |
| Publication date | 2025-12-23 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Policy Futures in Education (JOURNAL) |
| Journal identifiers | ISSN: 1478-2103 • E-ISSN: 1478-2103 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/14782103251411725 |
| OpenAlex | W7116937402 |
| Language | EN |
| References cited | 40 |
Universities can neither ban nor ignore generative AI (GenAI); they must govern and design for it. This paper argues that the core challenge is not ‘cheating’ per se but the misalignment between legacy assessment designs and AI-mediated learning. We contribute (1) a practical, tiered governance model that aligns policy, pedagogy, and assessment operations; (2) an assessment redesign heuristic that integrates authenticity, cognitive demand, and evidence provenance; and (3) a risk-mitigation view adapted from the Swiss-cheese model that places student learning, rather than surveillance, at the centre of integrity work. Building on recent philosophical critiques of instrumental responses to GenAI in education, we position assessment as a socio-technical system where teacher judgement, student agency, and tool affordances co-evolve. We illustrate the approach with ready-to-adopt patterns (e.g. oral defence with artefact trail; cohort-specific data briefs; constrained-tools practicals) and specify implementable governance levers (role clarity, template language, moderation workflows, analytics). The result is a coherent pathway ‘beyond bans’ toward trustworthy assessment that is educative, fair, and feasible at scale
Accountability · Affordance · Cognition · Corporate governance · Generative grammar · Generative model · Heuristic · Trustworthiness · Ethics and Social Impacts of AI · Explainable Artificial Intelligence (XAI · Online Learning and Analytics
Cheating in Academic Institutions
GPT detectors are biased against non-native English writers
Human error
Examining Science Education in ChatGPT
Generative AI tools and assessment
Exploring students’ perspectives on Generative AI-assisted academic writing
It's Wrong, But Everybody Does It
A five-dimensional framework for authentic assessment
Chatting and cheating
Leveraging AI to enhance learning
Student assessment in the ubiquitously connected world
Student and Teacher Views on Cheating in High School
The Next Word
AI and the future of humanity
School climate, school identification and student outcomes
| Highly cited | No |
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