AI-Empowered Innovation in the Management Model of “Balancing Study and Training” for College Student-Athletes with Special Talents

Abstract

The rapid development of artificial intelligence (AI) technology offers new pathways to address the contradiction between academic study and athletic training in the cultivation of college student-athletes with special talents. Based on an objective analysis of the causes of the “study-training contradiction,” this paper proposes a transformation in governance philosophy centered on “data governance,” “personalized customization,” and “proactive early warning.” By constructing an “intelligent sensing” data infrastructure, designing a “dual-track parallel” algorithmic model, and establishing a “human-machine collaboration” closed-loop feedback mechanism, the study elaborates the implementation pathways for AI-enabled “study-training balance.” This approach aims to advance the cultivation of student-athletes from traditional extensive and reactive management towards precise, personalized, and forward-looking intelligent governance, thereby providing theoretical and practical insights for deepening the national strategy of “integrating sports with education.”