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.

翻译：生成式人工智能在高等教育中的快速应用正在重塑评估实践，并加剧了围绕学术诚信、公平性和学习质量的担忧。尽管院校层面的回应越来越强调政策指导和伦理原则，但关于学生群体中负责任或机会主义人工智能使用的集体规范如何产生并得以稳定，目前仍缺乏形式化的理解。本文将学生在评估中的人工智能使用重新概念化为一个协调问题——该问题由同伴期望和评估设计共同塑造，而非仅依赖个体合规性。我们开发了一个基于协调的演化博弈论框架，该框架捕捉学习价值、投入、感知公平性和透明度，并通过反思性评估激励对院校人工智能治理进行隐性建模。我们利用解析结果和有限种群模拟揭示了学生人工智能使用中的阈值驱动行为转变：反思性评估激励中微小且精心校准的变化可触发向负责任、以学习为导向的人工智能使用规范的快速转变，而薄弱或错位的激励则会使机会主义行为持续存在。这些非线性动力学解释了为何仅凭政策声明往往无法改变行为，而适度的评估重新设计却能产生不成比例的效果。通过提供关于评估结构如何塑造集体人工智能使用实践的机制层面解释，本研究为高等教育机构提供了一种分析驱动的工具以支持面向未来学习，从而在不依赖监控或惩罚性执法的情况下实现比例相称、以教学法为主导的人工智能治理。