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

The rapid uptake of generative artificial intelligence (AI) in higher education is reshaping assessment
practices and intensifying concerns around academic integrity, fairness, and learning quality. While
institutional responses increasingly emphasise policy guidance and ethical principles, there remains limited
formal understanding of how collective norms of responsible or opportunistic AI use emerge and stabilise
within student cohorts. This paper reframes student AI use in assessment as a coordination problem
shaped by peer expectations and assessment design rather than individual compliance alone. We develop
a coordination-based evolutionary game-theoretic framework that captures learning value, effort, perceived
fairness, and transparency, with institutional AI governance modelled implicitly through reflective
assessment incentives.
We use analytical results and finite-population simulations to reveal threshold-driven behavioural transitions
in student AI use: small, well-calibrated changes in reflective assessment incentives can trigger rapid shifts
towards responsible, learning-oriented AI-use norms, whereas weak or misaligned incentives allow
opportunistic practices to persist. These non-linear dynamics explain why policy statements alone often fail
to change behaviour, while modest assessment redesigns can have disproportionate effects. By providing a
mechanism-level account of how assessment structures shape collective AI-use practices, this work offers
higher education institutions an analytically grounded tool for Future Facing Learning, supporting
proportionate, pedagogy-led AI governance without reliance on surveillance or punitive enforcement.