Exploring the influence of AI self-study rooms on K-12 learners' motivation, self-regulation, enjoyment, and...

Advances in artificial intelligence (AI) have enabled the development of AI-based self-study rooms that may influence K-12 learners' motivation, engagement, enjoyment, and self-regulation. This study was guided by Zimmerman's self-regulated learning framework to explore whether AI-equipped self-study environments can foster more autonomous and effective learning compared to traditional self-study settings.

A quasi-experimental design was conducted with 383 primary-school students in mainland China, randomly assigned to either an experimental group (AI self-study rooms) or a control group (traditional self-study). Over a 10-week period, participants completed standardized pre- and post-tests, as well as validated scales measuring motivation, engagement, enjoyment, and self-regulation. Data were analyzed using ANCOVA to control for baseline equivalences.

Findings revealed that students exposed to AI self-study rooms recorded notably higher post-intervention scores on all measured constructs than those in traditional self-study settings. ANCOVA results showed that group membership significantly affected motivation (p < 0.001, η² = 0.171), self-regulation (p < 0.001, η² = 0.238), enjoyment (p < 0.001, η² = 0.201), and engagement (p < 0.001, η² = 0.220). These outcomes suggest that AI-enhanced environments can better support self-regulated learning processes through personalized feedback, adaptive content recommendations, and data-driven scaffolding.

This study suggests that AI study rooms may be able to provide K-12 students with a more customized, responsive, and engaging learning experience that improves key elements of their learning. Future inquiries could employ longitudinal designs, diversify educational contexts, and integrate broader psychological variables to enrich understanding of how AI-driven tools might shape learners' trajectories over time.