Explainable AI Modeling of Academic Burnout in High School Students Using Cognitive Flexibility, School Climate, and Online Learning Engagement - Mahfouz, Yara; Minyi, Zhang (Journal of Adolescent and Youth Psychological Studies (JAYPS), 2026)
Objective: The objective of this study was to develop and validate an explainable artificial intelligence model for predicting academic burnout among Chinese high school students based on cognitive flexibility, perceived school climate, and online learning engagement. Methods and Materials: This quantitative cross-sectional study was conducted with 1,042 high school students from public schools in three major urban regions of eastern China using multi-stage cluster sampling. Participants completed standardized measures of academic burnout, cognitive flexibility, school climate, and online learning engagement. Data were analyzed using an ensemble machine learning framework combining Random Forest, Gradient Boosting, and XGBoost algorithms. Model performance was evaluated via nested cross-validation. Explainable AI techniques including SHapley Additive exPlanations and Local Interpretable Model-agnostic Explanations were applied to ensure transparency and interpretability of predictions. Findings: The ensemble model demonstrated strong predictive performance (RMSE = 0.32, MAE = 0.24) and explained 81% of the variance in academic burnout. Cognitive flexibility emerged as the most influential predictor (38.7% relative importance), followed by school climate (31.2%) and online learning engagement (22.5%). The model exhibited high stability across gender and grade-level subgroups, with explained variance ranging from 78% to 83%. Conclusion: Academic burnout among Chinese high school students is best explained through a dynamic interaction of cognitive, environmental, and behavioral factors, and the proposed explainable AI framework provides a powerful and transparent tool for early identification and targeted prevention of burnout risk in educational settings.