Integration of AI in Physical Education for Secure Student Health Monitoring and Cyber-Resilient Data Management

Abstract

Developing efficient strategies for tracking physical activity and encouraging better lives in educational settings is essential for growing concerns about the health and wellbeing of students. Traditional approaches in physical education (PE) often rely on manual tracking, which can lack accuracy and personalization. Thus, the potential of artificial intelligence (AI) in transforming PE systems into data-driven, personalized health monitoring tools for students is explored. The growing significance of preserving students' well-being requires the creation of effective systems capable of accurately monitoring and evaluating health-related data. Data is gathered from wearable sensors, self-reported questionnaires, class attendance records, and instructor observations. Predictive analytics are used to evaluate activity data and forecast students' health trends using AI methods such as deep learning (DL) and machine learning (ML) techniques. With the help of a mobile app and a cloud-based data storage security platform, AI is integrated into a physical education system to provide real-time health tracking, customized fitness plans, early health issue detection, and exercise regimen optimization to support general physical well-being. The integration of AI in PE systems improves the monitoring process and also fosters an environment conducive to better physical health, encouraging students to maintain an active lifestyle. The findings suggest that AI is pivotal in reshaping physical education into a more data-driven, individualized experience, benefiting students and teachers.