Mathematical Modelling of Ethical AI Use in Higher Education: A Coordination Game Framework for Future-Facing Learning

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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Ndidi Bianca Ogbo, Zhao Song, Shatha Ghareeb, The Anh Han (2026). Mathematical Modelling of Ethical AI Use in Higher Education: A Coordination Game Framework for Future-Facing Learning. arXiv:2605.27400. arXiv preprint.