Artificial Intelligence Driven Transformations in Tennis Training: Integrating Psychological and Technical Advancements

A study aims to establish a relationship between artificial intelligence-based systems by integrating the Recurrent Neural Networks (RNNs) for psychological modeling and Convolutional Neural Networks (CNNs) for error recognition. This review paper combines current developments in AI technology with recurrent neural networks (RNN) to study psychological aspects that impact tennis training strategy development. Study examined six studies from 2022 to 2024 by following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for systematic assessment. The selected findings assess AI-controlled psychological regulation alongside error action recognition and introduce improvements for online teaching and electronic line judging and automated ball transmitter path analysis and ball-passing net tracking systems. The research demonstrates that implementing RNNs optimized through AI systems produces better psychological state predictions that achieve 73% successful intervention outcomes. Big data analytics improve online tennis education while convolutional neural networks (CNNs) demonstrate 95.34% success in error action recognition. The controversy about AI taking over human line judges persists because it raises technical advantages but also ethical considerations. The review demonstrates how AI offers substantial training transformations in tennis but shows unexplored research opportunities for the future.