Enhancing Student Learning Outcomes through AI-Driven Educational Interventions: A Comprehensive Study of Classroom Behavior and Machine Learning Integration
ChunHong Yuan, Nan Xiao, YuTing Pei, YuJia Bu, YuLe Cai
Abstract
This study investigates the integration of artificial intelligence (AI) in education to enhance student learning outcomes through personalized interventions. Using datasets encompassing classroom behavior, individualized learning paths, and teaching practices, the study identifies key factors influencing student engagement and performance. Employing the XGBoost classifier, it achieves a 96% accuracy in predicting student outcomes, enabling targeted support for at-risk students. A mixed-methods approach reveals the importance of engagement, effective teaching, and personalization in fostering inclusive learning environments. Despite limitations, such as controlled settings and lack of longitudinal data, this research underscores the transformative potential of AI in creating equitable educational opportunities. Future studies should explore scalability, long-term effects, and diverse AI techniques to maximize benefits.