INJURY PREDICTION: THE POTENTIAL OF ARTIFICIAL INTELLIGENCE IN PREVENTING INJURIES AMONG STUDENT-ATHLETES

Abstract

The article examines the application of predictive models based on artificial intelligence (AI) as a modern technological approach to injury prevention among student-athletes in higher education institutions. The study aims to explain the theoretical foundations of injury risk prediction and to provide practical guidelines for coaches and instructors on implementing this methodology in the training process. The author identifies three key stages of AI application in injury prediction: multifactor data collection, risk model development, and the design of preventive measures. The paper analyzes the main predictors of injuries, including imbalance between training load and recovery, biomechanical changes, and physiological stress.

An algorithm of actions for coaches is proposed, including receiving alerts, verifying data, adjusting training programs, and monitoring outcomes. Ethical aspects of AI use in sports training are also considered.

The results demonstrate that AI enables a transition from a reactive (treatment-based) approach to a preventive one, which is particularly important in conditions of limited resources in higher education institutions.