A systematic literature review on designing self-regulated learning using generative artificial intelligence and its future research directions

A growing body of research suggests that generative AI (GenAI) can significantly enhance self-regulated learning (SRL) through its immediacy and interactivity. Nevertheless, challenges remain, including the lack of clarity regarding the mechanisms by which generative AI influences SRL, as well as the difficulties teachers encounter when trying to incorporate it into their classrooms. This systematic review study investigates how to design SRL activities using GenAI. We examined 73 articles published over the past five years, drawn from four databases: Web of Science, ProQuest, ERIC, and Scopus. This study has three major empirical findings. First, six pedagogical affordances from GenAI across the three phases of SRL—forethought, Performance, and self-reflection—are (i) creating personalized learning objectives; (ii) searching for, analyzing, and integrating resources; (iii) monitoring and evaluating progress; (iv) recommending learning strategies; (v) recording progress and providing feedback; and (vi) generating new ideas and examples. Second, popular student learning activities using GenAI in each phase are information searching in the forethought; strategies for problem-solving in the performance, and obtaining feedback and conducting self-assessments in the self-reflection. Third, two major dimensions influencing student engagement in SRL using GenAI are individual and environmental. Finally, we visualize how the three empirical findings relate to each other. Our findings help us understand how AI as a human-machine collaborative tool affects the SRL process. This study provides practical recommendations for facilitating SRL in GenAI-enhanced environments and offers guidance for the design and development of personalized GenAI learning tools.