Leveraging Explainable Artificial Intelligence (XAI) to Enhance Academic Integrity: A Predictive Risk Mitigation Framework

Abstract

Academic institutions face numerous studentcentric risks; the most critical one being academic misconduct through plagiarism, unauthorized collaboration, and cheating. Academic institutions have been traditionally managing such risks through academic integrity policies, manual plagiarism detection, employing strict exam monitoring, and even using automated plagiarism detection tools. However, such traditional methods are reactive, slow, human-dependent, and lack early intervention capabilities. To overcome these inherent limitations, most institutions have taken support of Artificial Intelligence (AI) tools; however, most of these AI tools focus on making predictions and decisions autonomously, often without revealing the underlying reasoning. In contrast, Explainable AI (XAI) enhances AI by providing clear, interpretable explanations that increase transparency, accountability, and trust in automated outcomes. This research aims to propose a comprehensive framework that integrates predictive analytics and Explainable AI (XAI) to proactively identify and mitigate academic misconduct risks in higher education. The framework will emphasize transparency, faculty trust, and ethical decisionmaking to support early interventions and promote academic integrity.

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