Beyond the ban: a theoretical framework for integrating Generative AI in assessment

Universities can neither ban nor ignore generative AI (GenAI); they must govern and design for it. This paper argues that the core challenge is not “cheating” per se but the misalignment between legacy assessment designs and AI-mediated learning. We contribute (1) a practical, tiered governance model that aligns policy, pedagogy, and assessment operations; (2) an assessment redesign heuristic that integrates authenticity, cognitive demand, and evidence provenance; and (3) a risk-mitigation view adapted from the Swiss-cheese model that places student learning, rather than surveillance, at the centre of integrity work. Building on recent philosophical critiques of instrumental responses to GenAI in education (Peters et al., 2024), we position assessment as a socio-technical system where teacher judgement, student agency, and tool affordances co-evolve. We illustrate the approach with ready-to-adopt patterns (e.g., oral defence with artefact trail; cohort-specific data briefs; constrained-tools practicals) and specify implementable governance levers (role clarity, template language, moderation workflows, analytics). The result is a coherent pathway “beyond bans” toward trustworthy assessment that is educative, fair, and feasible at scale.