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

• URL: http://arxiv.org/abs/2605.27400v1
• Date: Thu, 23 Apr 2026 21:21:14 GMT
• Title: Mathematical Modelling of Ethical AI Use in Higher Education: A Coordination Game Framework for Future-Facing Learning
• Abstract summary: This paper reframes student AI use in assessment as a coordination problem shaped by peer expectations and assessment design.<n>We use analytical results and finite-population simulations to reveal threshold-driven behavioural transitions in student AI use.
• Score: 7.3864557762003615
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

Related papers

• Evaluation in the Age of AI: Output as Evidence of Learning [0.0]
  The rapid adoption of artificial intelligence (AI) has disrupted how learning is demonstrated and evaluated in higher education.<n>This paper examines the ethical challenges of educational evaluation in the age of AI from a university-level perspective.
  arXiv  Detail & Related papers  (2026-08-23T23:40:30Z)
• From Holistic Evaluation to Structured Criteria: Rubrics Across the Evolving LLM Landscape [79.30826980815927]
  rubrics are explicit criteria sets that transform complex quality judgments into structured and actionable standards.<n>We systematically organize existing rubric designs, examine their construction and optimization, and analyze their role across evaluation and training.
  arXiv  Detail & Related papers  (2026-06-07T13:34:55Z)
• AI Misuse in Education Is a Measurement Problem: Toward a Learning Visibility Framework [2.3412211209700997]
  This paper reframes AI misuse in education as a measurement problem rooted in the loss of visibility into the learning process.<n>Rather than promoting surveillance, the framework emphasizes transparency and shared evidence as foundations for ethical AI integration in classroom settings.
  arXiv  Detail & Related papers  (2026-03-08T22:35:09Z)
• Agentic AI, Retrieval-Augmented Generation, and the Institutional Turn: Legal Architectures and Financial Governance in the Age of Distributional AGI [0.0]
  This paper examines the intersection of agentic AI, Retrieval-Augmented Generation (RAG), and their implications for legal accountability and financial market integrity.<n>We argue that alignment must be reconceptualized as a mechanism design problem involving runtime governance graphs, sanction functions, and observable behavioral constraints rather than calibrated constitutional values.
  arXiv  Detail & Related papers  (2026-02-20T11:58:43Z)
• Mirror: A Multi-Agent System for AI-Assisted Ethics Review [104.3684024153469]
  Mirror is an agentic framework for AI-assisted ethical review.<n>It integrates ethical reasoning, structured rule interpretation, and multi-agent deliberation within a unified architecture.
  arXiv  Detail & Related papers  (2026-02-09T03:38:55Z)
• Designing AI-Resilient Assessments Using Interconnected Problems: A Theoretically Grounded and Empirically Validated Framework [0.0]
  The rapid adoption of generative AI has undermined traditional modular assessments in computing education.<n>This paper presents a theoretically grounded framework for designing AI-resilient assessments.
  arXiv  Detail & Related papers  (2025-12-11T15:53:19Z)
• AI-Educational Development Loop (AI-EDL): A Conceptual Framework to Bridge AI Capabilities with Classical Educational Theories [8.500617875591633]
  This study introduces the AI-Educational Development Loop (AI-EDL), a theory-driven framework that integrates classical learning theories with human-in-the-loop artificial intelligence (AI)<n>The framework emphasizes transparency, self-regulated learning, and pedagogical oversight.
  arXiv  Detail & Related papers  (2025-08-01T15:44:19Z)
• Artificial Intelligence in Government: Why People Feel They Lose Control [44.99833362998488]
  The use of Artificial Intelligence in public administration is expanding rapidly.<n>While AI promises greater efficiency and responsiveness, its integration into government functions raises concerns about fairness, transparency, and accountability.<n>This article applies principal-agent theory to AI adoption as a special case of delegation.
  arXiv  Detail & Related papers  (2025-05-02T07:46:41Z)
• Fairness in Reinforcement Learning with Bisimulation Metrics [45.674943127750595]
  By maximizing their reward without consideration of fairness, AI agents can introduce disparities in their treatment of groups or individuals.<n>We propose a novel approach that leverages bisimulation metrics to learn reward functions and observation dynamics.
  arXiv  Detail & Related papers  (2024-12-22T18:23:06Z)
• Rethinking Generalizability and Discriminability of Self-Supervised Learning from Evolutionary Game Theory Perspective [43.510860711231544]
  State-of-the-art self-supervised methods tend to enhance either generalizability or discriminability but not both simultaneously.<n>We propose a novel self-supervised learning method that leverages advancements in reinforcement learning to jointly benefit from the general guidance of EGT.
  arXiv  Detail & Related papers  (2024-11-30T17:20:23Z)
• Comprehensive AI Assessment Framework: Enhancing Educational Evaluation with Ethical AI Integration [0.0]
  This paper presents the Comprehensive AI Assessment Framework (CAIAF), an evolved version of the AI Assessment Scale (AIAS) by Perkins, Furze, Roe, and MacVaugh. The CAIAF incorporates stringent ethical guidelines, with clear distinctions based on educational levels, and advanced AI capabilities. The framework will ensure better learning outcomes, uphold academic integrity, and promote responsible use of AI.
  arXiv  Detail & Related papers  (2024-06-07T07:18:42Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.