Digital transformation of university sports management through platform solutions for data analytics and artificial intelligence to personalize the training process and rational use of resources

Abstract

The article is devoted to the digital transformation of university sports management based on the integration of platform solutions for data analytics and artificial intelligence systems aimed at personalizing the training process and the rational use of sports infrastructure resources. The purpose of the study is to justify and experimentally test a model of a digital platform that, based on comprehensive data about a student-athlete (biomedical indicators, academic workload, training history, behavioral metrics), enables the formation of individual training programs and management decisions at the department and university levels. The study employs methods of functional modeling of business processes, data mining, machine learning, as well as a pedagogical experiment involving student teams from three universities; the information basis consists of anonymized data from university learning management and sports activity monitoring systems over three academic years. The developed platform provides automated calculation of training loads, prediction of athletic results and overload risks, scheduling of gym and equipment use, as well as dashboard panels for coaches and university administrators. Pilot implementation demonstrated improved accuracy in training process planning, increased student engagement, and more balanced utilization of sports facilities, along with a reduction in health related absences; comparison with control groups confirms a statistically significant advantage of the digital model. The discussion substantiates methodological and organizational conditions for the successful digital transformation of university sports management, identifies the study’s limitations (data quality, the need to update coaching competencies), and outlines directions for further developments related to the integration of the platform into the smart campus ecosystem and the expansion of artificial intelligence modules. The scientific novelty of the study lies in the comprehensive integration of predictive analytics tools, human capital management, and infrastructure resource management within a single university sports platform, while the practical significance is confirmed by the potential scalability of the proposed model to a network of universities, adaptability to various sports, and the inclusion of analytical indicators into the strategic management system for the development of physical education and mass sports in a digital educational environment. The prospects for further research are associated with the expansion of datasets through wearable sensors, the development of more transparent artificial intelligence models, and the evaluation of ethical aspects of automating coach and management decisions in the field of university sports.