Self‐Regulated Learning and Governance of <scp>AI</scp> in Higher Education

ABSTRACT We examine how students in higher education respond to governance and self‐regulate AI use for learning within evolving institutional norms. Using a qualitative design and interviews with 25 students from three leading STEM universities in India, we analyse students' engagement with AI tools across regulatory—self‐regulation, co‐regulation, socially shared regulation, and top‐down regulation—and governance dimensions. Our findings indicate that student agency is shaped by both pedagogical and governance aspects of AI. Students actively interpret AI policies, seek legitimacy for AI use, and negotiate institutional expectations through compliance, adaptation, and peer standardisation. We propose a conceptual framework combining the regulation dimension with AI governance, suggesting four student‐centred scenarios for the use of AI: student‐AI as mutually reinforcing, AI as a peer, AI as a co‐creator, and AI as a rule‐based tool. Our study challenges binary perspectives of AI governance as either strict prohibition or unrestricted autonomy. Instead, we argue for a balanced approach where institutions provide clear guidelines while fostering student‐driven, ethical AI use.