Coordination-based evolutionary game-theoretic framework of student AI use in assessment

Research Method · All research methods
Evidence · 3 claims (3 for) · 1 study (1 theoretical), q2 · 0 of 1 report an effect size · 3 claims rest on one study

Description

The framework models student generative-AI use in assessment as a two-player symmetric coordination game in a well-mixed finite population, in which the institution is not a strategic player but shapes payoffs through assessment design. Strategies span responsible use with meaningful reflection, responsible use with superficial reflection, opportunistic use, and clear misuse, with parameters for learning value, effort costs, legitimacy costs, reflection reward and effort, and misconduct penalties. Behavioural change follows the Fermi imitation rule with small mutation probability, and long-run dynamics are approximated by a Markov chain over homogeneous states. The article writes that "we model interactions at the student–student level using a two-player symmetric game in a well-mixed population".

Accounts

• Coordination-based evolutionary game-theoretic framework of student AI use in assessment: The framework models student generative-AI use in assessment as a two-player symmetric coordination game in a well-mixed finite population, in which the institution is not a strategic player but shapes payoffs through assessment design. Strategies span responsible use with meaningful reflection, responsible use with superficial reflection, opportunistic use, and clear misuse, with parameters for learning value, effort costs, legitimacy costs, reflection reward and effort, and misconduct penalties. Behavioural change follows the Fermi imitation rule with small mutation probability, and long-run dynamics are approximated by a Markov chain over homogeneous states. The article writes that "we model interactions at the student–student level using a two-player symmetric game in a well-mixed population". (Ndidi Bianca Ogbo et al. (2026))

Claims

• In simulations, increasing the reflection reward produces a sharp threshold-driven transition around r ≈ 1.5, beyond which responsible AI use with meaningful reflection dominates and opportunistic AI use collapses [+M]
• In simulations, peer sensitivity (β) moderates how rapidly responsible AI-use norms emerge: strong peer sensitivity yields a sharp tipping-point transition, while weak peer sensitivity yields only gradual, marginal increases [+M]
• In simulations, responsible AI-use norms emerge only within a constrained design space: when reflection effort is high relative to its reward, opportunistic AI use persists, and near the boundary small changes in reward or effort produce large shifts in responsible-use frequency [+M]

Related Research Methods

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Key Sources

• Ndidi Bianca Ogbo, Zhao Song, Shatha Ghareeb, and The Anh Han. (2026). Mathematical Modelling of Ethical AI Use in Higher Education: A Coordination Game Framework for Future-Facing Learning. https://arxiv.org/abs/2605.27400

Page metadata

Field	Value
type	research-method
id	coordination-based-evolutionary-game-theoretic-framework-of-student-ai-use-in-assessment
title	Coordination-based evolutionary game-theoretic framework of student AI use in assessment
description	The framework models student generative-AI use in assessment as a two-player symmetric coordination game in a well-mixed finite population, in which the institution is not a strategic player but shapes payoffs through assessment design.
status	review
generated	by process:settle-candidates on 2026-10-10
trust	unverified — no one has confirmed this page's content

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