Self-Regulated Learning and Governance of AI in Higher Education.

Autor/inn/en	Misra, Debananda; Nirmal, Priya; Bhat, Mansi
Titel	Self-Regulated Learning and Governance of AI in Higher Education.
Quelle	In: Higher education quarterly, 80 (2026) 1, 16 S.Infoseite zur Zeitschrift PDF als Volltext Link als defekt meldenVerfügbarkeit
Sprache	englisch
Dokumenttyp	online; gedruckt; Zeitschriftenaufsatz
ISSN	0263-9769; 0951-5224; 1468-2273
DOI	10.1111/hequ.70079
Schlagwörter	Künstliche Intelligenz; Lehre; Studium; Wissenschaft; Hochschule; Student; Ausland; Indien
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. (HRK / Abstract übernommen).
Erfasst von	Hochschulrektorenkonferenz, Bonn
Update	2026/4