AI governance in higher education

A lifecycle assurance model

Abstract

Generative artificial intelligence (GenAI) is reshaping higher education across teaching, research, and administration; however, institutional governance has not kept pace with its rapid adoption. Prior scholarship emphasizes principles such as transparency, fairness, and accountability, but provides limited guidance for how institutions can operationalize these commitments in practice. This study addresses this gap by examining how higher education institutions can implement effective, sustained governance of AI systems. Drawing on a systematic and integrative review of AI governance literature and institutional theory, the article develops an assurance-based lifecycle framework that conceptualizes governance as a continuous organizational capability. The framework spans five stages: design and development, pre-deployment validation, institutional deployment, continuous monitoring, and adaptation. Within each stage, microethical practices are embedded as actionable checkpoints to translate ethical principles into operational processes. The framework defines the characteristics of higher education, including distributed governance, academic freedom, and multi-stakeholder decision-making, while addressing challenges associated with probabilistic systems and algorithmic drift. The study contributes a structured model with defined indicators, roles, and sociotechnical conditions that support effective implementation. These contributions provide practical guidance for institutional leaders seeking to align AI adoption with educational mission, equity, and student success.