Conceptualizing AI Governance Through Organizational Learning

A Heuristic Framework for Higher Education

Abstract

The rapid expansion of generative artificial intelligence (GenAI) in higher education has intensified concerns about ethics, accountability, academic integrity, privacy, transparency, bias, and governance. Existing approaches to AI governance emphasize compliance, procedural oversight, ethical safeguards, and risk mitigation. Although this provides an essential foundation for responsible AI use, these approaches offer limited theoretical explanation of how institutions develop and sustain governance capacity as AI technologies continue to evolve. This article draws on organizational learning theory and reconceptualizes AI governance as a system of organizational learning through which institutions generate organizational knowledge, integrate experience, revise governance practices, and adapt to technological, pedagogical, ethical, and organizational change. The article advances a conceptual framework that integrates scholarship on AI governance and organizational learning to explain how governance capacity develops through continuous organizational learning. The framework offers a theoretical foundation for strengthening institutional resilience, responsible innovation, and effective human–AI collaboration in higher education.