ORIGINAL RESEARCH article
Front. Psychol.
Sec. Educational Psychology
Exploring the Influence of AI Self-Study Rooms on K–12 Learners' Motivation, Self-Regulation, Enjoyment, and Engagement
Le Yao 1,2
Wei Li 3
Yantong Liu 4
1. College of Humanities, Sookmyung Women’s University, Seoul, Republic of Korea
2. School of Humanities, Arts and Education, Shandong Xiehe University, Jinan, China
3. Zhejiang Fashion Institute of Technology, Ningbo, China
4. Kunsan National University, Gunsan, Republic of Korea
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Abstract
Background: 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. Methods: 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. Results: 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. Conclusions: 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.
Summary
Keywords
AI education, artificial intelligence, engagement, Motivation, self-regulated learning
Received
15 December 2025
Accepted
30 January 2026
Copyright
© 2026 Yao, Li and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Yantong Liu
Disclaimer
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