Generative AI and Self-Regulated Learning in Higher Education: A Narrative Review with Implications for Physics Education

Abstract

The rapid development of generative artificial intelligence (generative AI) is reshaping how students access information, complete academic tasks, and regulate learning in higher education. This narrative literature review synthesizes and critically interprets evidence on the role of generative AI in learning strategies and learning independence, with provisional implications for physics education. Searches were conducted in SINTA, Scopus, Web of Science, ScienceDirect, and SpringerLink for publications from 2020 to 2026, with Google Scholar used as a supplementary source. Twelve peer-reviewed publications were included in the main thematic synthesis and interpreted through Zimmerman’s self-regulated learning framework, Bandura’s social cognitive theory, and cognitive load theory as an additional perspective. The findings indicate that generative AI may support goal setting, feedback, motivation, strategy development, self-efficacy, and reflection. However, its benefits depend on active engagement, self-regulation capacity, output quality, and metacognitive guidance. Unguided use may encourage cognitive offloading, weaken self-monitoring, and improve task performance without equivalent conceptual understanding. Because only one study directly involved university students in physics or physics education, the implications remain provisional. Physics education programs should therefore promote independent problem solving, critical evaluation, transparent AI use, and structured metacognitive guidance.