Enhancing School Students’ Self-Regulated Learning through Generative AI Support: A Randomized Controlled Trial

Abstract Self-regulated learning (SRL) is a critical competency for academic success and lifelong learning, yet providing personalized SRL support in real-time classroom settings remains challenging. In this randomized controlled trial, we investigated whether generative-AI-(GenAI)-based interventions can effectively support SRL in secondary school students. Drawing on an integrative model of self-regulation in education, the study contrasted two types of GenAI-supported interventions—targeting either motivational (utility value) or strategic (cognitive-learning-strategy-based) aspects of SRL—with a control condition using standard ChatGPT. A total of 371 students (Grades 7–9) were randomly assigned to one of the three conditions and participated in six 45-min sessions during regular physics or English lessons. Outcome measures included self-reported utility value, strategy use (self-reported and tested performance), interest, effort, and tested domain-specific knowledge, assessed at pre- and posttest. The results showed that students in the utility value condition reported more favorable development of perceived utility value than those in the cognitive strategy condition. However, no statistically significant advantages of either intervention over the control condition were found for effort, domain-specific knowledge, or elaboration-based strategy use. Exploratory analyses indicated that students who engaged more meaningfully with the GenAI tended to have more sustained interest than those in the control group. No subject-specific (physics or English) differences in intervention effects were observed. Findings suggest GenAI-based interventions may help preserve motivational aspects of SRL under certain conditions, but further development is needed to effectively support cognitive strategies and improve learning outcomes in secondary school settings.