Beyond Automation: Cultivating Self-Regulated Learning through AI and Reflective Practice

This mixed-methods study examines the effects of AI utilization style (ChatGPT as information provider vs. writing partner) and reflection type (group vs. individual) on students’ academic self-regulation (SRL). In a five-week, 2 × 2 factorial design intervention with Vietnamese undergraduates (final matched analytic sample: n = 288), quantitative data from the Self-Regulation Questionnaire–Academic (SRQ-A) were analyzed using ANCOVA. The analysis revealed that reflection type significantly influenced identified regulation, with group reflection fostering greater internalization of academic goals. AI utilization style significantly impacted all four regulation types (external, introjected, identified, and intrinsic), indicating that ChatGPT as a writing partner can both heighten performance pressure and foster deeper engagement. No significant between‐group interaction effects emerged, although qualitative themes suggested possible complementarities between reflection and AI use. Thematic analysis of reflective journals and interviews enriched these findings. Individual reflection was found to promote deeper introspection, while group reflection fostered motivation and accountability. Students’ engagement evolved from an initial over-reliance on ChatGPT to more strategic use over time, a development linked to improved prompt literacy and the regulatory role of reflection. While students valued ChatGPT for its speed, they expressed distrust in its accuracy and a strong demand for structured AI literacy education. These findings highlight the need for teacher guidance and metacognitive scaffolds in AI-supported tasks, demonstrating how emerging technologies interact with motivation and self-regulation in complex educational settings.