Research on Identifying Educational Psychological Influencing Factors of Student Learning Burnout from the Perspective of Artificial Intelligence Learning Behavior Data Analysis
DOI:
https://doi.org/10.70088/me8qcz83Keywords:
learning burnout, educational psychology, student engagement, data analysis, artificial intelligenceAbstract
This study investigates educational psychological factors influencing student learning burnout through artificial intelligence learning behavior data analysis. As intelligent tutoring systems and adaptive platforms integrate into classrooms, they generate behavioral traces revealing students' cognitive and emotional states. This research identifies which AI-derived indicators predict learning burnout and clarifies the mediating and moderating mechanisms among them. A quantitative design was employed with 320 middle school students using various AI learning platforms over sixteen weeks. Behavioral logs, including task completion, response latency, interaction frequency, and sentiment polarity, were extracted and aligned with a standardized burnout scale. Descriptive statistics, correlation analysis, multiple regression, and structural equation modeling tested the relationships. Results indicate that lower task completion rates and negative sentiment polarity significantly associate with higher emotional exhaustion and depersonalization. Longer response latency predicts reduced personal accomplishment, while higher interaction frequency exerts a modest protective effect. Mediation analysis shows academic self-efficacy partially mediates the link between behavioral engagement and burnout. Moderation analysis reveals perceived teacher support buffers the negative impact of cognitive load. These findings suggest AI learning behavior data can serve as an early warning system for learning burnout. The study recommends educators and designers incorporate real-time monitoring, adaptive feedback, and emotional support to reduce burnout and promote academic well-being. The conclusions provide a theoretical foundation and practical guidance for integrating artificial intelligence into educational psychological assessment and application.References
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Copyright (c) 2026 Cong Wan (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
