Sports and education integration tennis player selection and personalized training path planning based on artificial intelligence technology

The integration of sports and education through artificial intelligence (AI) is redefining traditional methods of athlete selection and personalized training, particularly in skill-demanding sports like tennis. Despite advancements in training science, conventional approaches still rely heavily on subjective assessments and uniform regimens, failing to accommodate individual differences in learning styles, biomechanics, cognitive load, and psychological resilience. This study addresses this gap by investigating the role of AI technologies—specifically machine learning algorithms, neural networks, augmented reality (AR), and motion capture systems—in optimizing tennis player recruitment and training personalization. A controlled experimental design was employed, involving 30 amateur and intermediate tennis players divided into AI-assisted and traditional training groups. AI tools such as motion capture systems, wearable sensors, and predictive neural network models were used to assess performance metrics, injury risk, and psychological readiness. The study found that players in the AI-supported group demonstrated significantly greater improvements in stroke accuracy (↑14.8%), serve speed (↑13.4 km/h), and reaction time (↓29 ms), along with a 60% reduction in injury occurrence and higher satisfaction scores across multiple training dimensions. Our contributions include: (1) a hybrid framework for integrating AI technologies into tennis training and talent identification; (2) empirical validation of AI's effectiveness in enhancing performance and injury prevention; and (3) thematic insights on adaptability and psychological impact from athletes using AI tools. These findings underscore AI's transformative potential to shift tennis coaching from intuition-based to data-driven, personalized methodologies, aligning with modern educational and athletic development goals.