Mathematical Modelling of Ethical AI Use in Higher Education: A Coordination Game Framework for Future-Facing Learning
arXiv:2605.27400v1 Announce Type: cross Abstract: 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
The rapid and widespread adoption of generative AI in higher education necessitates formal frameworks to understand and manage its ethical implications, especially regarding assessment and academic integrity.
This paper provides a mathematical framework to understand how ethical norms for AI use emerge in educational settings, which is crucial for institutions to develop effective policies and preserve learning quality.
The understanding of student AI use shifts from individual compliance to a coordination problem influenced by peer expectations, requiring new approaches to policy and intervention design.
- · Higher Education Institutions (proactive)
- · AI ethics researchers
- · Students (with clear ethical guidelines)
- · Higher Education Institutions (reactive)
- · Traditional assessment methods
- · Students (if ethical use is not managed)
Universities will increasingly adopt game theory and behavioral economics in designing AI usage policies.
New educational technologies may emerge that incorporate coordination mechanisms to foster ethical AI use.
The definition of academic integrity may fundamentally shift, impacting curriculum design and the value of credentials.
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Read at arXiv cs.AI