Artificial Intelligence-Driven Personalized Training Systems And Their Impact On Athletic Performance, Injury Prevention, And Physical Education Outcomes: A Systematic Empirical Study

Background: Traditional athletic training and physical education frameworks frequently employ standardized methodologies that fail to accommodate individual biomechanical variations, track real-time physiological strain, or scale effectively in educational environments. Objective: This study evaluates the efficacy of an Artificial Intelligence-Driven Personalized Training System (AI-PTS) that integrates computer vision, multi-sensor wearable fusion, and predictive analytics to optimize athletic performance, reduce injury incidence, and enhance student engagement in physical education. Methods: A 24-week randomized controlled trial was conducted with 180 participants, comprising elite collegiate athletes (n = 90) and university physical education students (n = 90). Participants were randomized into an Experimental Group (AI-PTS intervention) and a Control Group (traditional training). The AI-PTS utilized Convolutional Neural Networks (CNNs) for kinematic motion capture, Long Short-Term Memory (LSTM) networks for wearable data fusion, and Extreme Gradient Boosting (XGBoost) for injury risk forecasting. Statistical analysis featured mixed-design ANOVA, multiple linear regression, and Structural Equation Modeling (SEM). Results: The experimental athletic cohort exhibited statistically significant improvements in VO2 max (+14.2%, p