Closing the AI Governance Gap in Tertiary Education: A Global Living Governance Framework for Institutional Readiness

Artificial intelligence (AI), particularly generative artificial intelligence (GenAI), has moved rapidly from emerging technology to embedded infrastructure across global tertiary education. Universities, community colleges, polytechnics, technical and vocational institutions, and professional education providers are increasingly encountering AI across teaching, assessment, research, administration, student support, and workforce preparation. Yet institutional governance has developed unevenly. AI adoption frequently advances faster than policy development, faculty capability, assessment redesign, technology oversight, student guidance, and mechanisms for institutional learning. This paper conceptualizes this mismatch as the AI governance gap: the distance between the speed, scope, and embeddedness of AI adoption and an institution’s capacity to guide, support, oversee, evaluate, and continuously adapt that use responsibly. Drawing on organizational readiness theory, dynamic capabilities, adaptive governance, responsible AI, and scholarship on AI in education, the paper develops a Global Living Governance Framework for Institutional AI Readiness. The framework identifies eight interconnected dimensions of readiness: leadership, strategy, and governance; faculty and staff capacity; teaching, learning, and assessment; student AI literacy and agency; technology and vendor governance; data, privacy, and security; accessibility, ethics, and equity; and workforce and ecosystem alignment. These dimensions operate through an iterative governance cycle of sensing, assessment, guidance, implementation, observation, evaluation, adaptation, and reassessment. The paper argues that the governance gap is globally relevant but contextually variable, shaped by institutional mission, regulatory environment, technological infrastructure, resource capacity, governance structure, culture, and workforce conditions. Eight propositions are offered for future empirical testing. The framework reframes AI governance from policy production to adaptive institutional capability and provides a foundation for comparative research and strategic action across diverse tertiary education systems.