The rapid integration of generative AI in education has fundamentally disrupted traditional approaches to academic integrity. Historically, institutions have relied heavily on punitive detection models – a ‘police and punish’ paradigm seeking to retroactively identify misconduct. However, in an AI-ubiquitous environment, this reactive strategy is increasingly unreliable, contributing to heightened student anxiety and unmanageable administrative burdens for educators.

This video explores a necessary conceptual shift toward a ‘support and validate’ framework. Rather than solely policing final outputs, this proactive concept focuses on embedding formative feedback and students self-validating their authentic learning journey throughout the assessment lifecycle. By emphasising student support and process transparency, institutions can foster a positive culture of academic integrity built on trust and equitable skill development rather than surveillance.

Crucially, this poster will present exclusive, early insights from a series of global university pilots launching on July 1. These preliminary findings will illuminate the practical efficacy of the ‘support and validate’ model in real-world academic settings, offering a scalable, evidence-based alternative to punitive AI detection. Attendees will learn actionable strategies for rethinking institutional academic integrity frameworks to better support both students and educators.