Ogbo et al. (2026) — Teesside University / Adobe Research. arXiv preprint.
Ethical AI Use in Higher Education: A Coordination Game Framework provides a formal mechanism-level account of why policy statements alone fail to change student AI-use behavior. Reframing student AI use in assessments as a coordination problem — where individual choices depend on peer expectations and assessment design — the authors develop an evolutionary game-theoretic model capturing learning value, effort, perceived fairness, and transparency. The key finding is non-linear threshold dynamics: small, well-calibrated changes to reflective assessment incentives can trigger rapid cohort-wide shifts toward responsible AI use, while weak or misaligned incentives allow over-reliance and opportunistic practices to persist. This explains the common observation that institutional regulation and policy pronouncements have limited impact while modest assessment redesign — such as requiring students to reflect on their AI interactions — can have disproportionate effects. The framework complements genai-assessment-governance by providing the mathematical underpinning for why Restrict/Scaffold/Require stances have differential behavioral effects, and connects to academic-integrity research on how social norms shape generative-ai use among higher-ed students. The model supports institutional-change-framework-ai approaches that emphasize pedagogy-led governance over surveillance.
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- intelligent-tutoring — AI tutoring systems and architectures
- ai-literacy — Frameworks for understanding and using AI
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- generative-ai — Generative AI applications and implications
- higher-ed — AI in higher education contexts