Assessing the Efficacy of an Artificial Intelligence-Driven Real-Time Exercise Monitoring System in Computer-Supported Collaborative Learning

Introduction: the efficacy of AI-driven real-time exercise monitoring systems in enhancing physical education programs was explored in this study. the integration of such technologies aimed to boost engagement, motivation, and safety among participants, reflecting a growing trend towards technology-enhanced learning environments. Objective: the objective was to empirically evaluate the impact of an AI-driven system on learning engagement, motivation levels, and injury prevention in a physical education context, comparing outcomes against traditional training methods. Methodology: the methodology involved a controlled experiment with eighty physical education students divided into control and experimental groups. data were collected through surveys, performance assessments, and injury reports, with statistical analyses conducted using independent samples t-tests and chi-square tests. Results: results indicated significantly higher engagement and motivation in the experimental group, which utilized the AI system. additionally, this group experienced fewer injuries, demonstrating the system’s potential to enhance safety. Discussion: other studies have similarly highlighted technology’s role in improving educational outcomes, though few have focused specifically on physical education. this study’s findings align with broader research supporting the adoption of AI in educational settings. Conclusions: the conclusions confirm that AI-driven monitoring systems significantly improve student engagement, motivation, and safety in physical education, suggesting that such technologies can be valuable additions to educational curricula to enhance learning experiences and outcomes.