Artificial Intelligence and Self-Regulated Learning: A Systematic Review of Applications in Higher Education

The rapid integration of Artificial Intelligence (AI) into higher education has transformed teaching, learning, and assessment practices. Among its most promising applications is the support of Self-Regulated Learning (SRL), which refers to learners’ ability to plan, monitor, and evaluate their own learning processes. This systematic review examines the applications of AI in fostering SRL within higher education contexts. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, studies published between 2018 and 2025 were identified from Scopus, Web of Science, ERIC, and Google Scholar databases. A total of 312 records were identified, of which 42 studies met the inclusion criteria. The findings reveal that AI applications, including intelligent tutoring systems, learning analytics dashboards, machine learning algorithms, recommender systems, and conversational agents, significantly support goal setting, self-monitoring, feedback provision, and learning personalization. The review also identifies challenges related to ethics, data privacy, algorithmic transparency, and digital equity. The study concludes that AI has substantial potential to enhance SRL in higher education, provided that implementation is guided by pedagogical principles and ethical considerations. Future research should focus on explainable AI, longitudinal assessments, and inclusive educational designs.