Hanyuan Qu¹ and Ivan Revilla², ¹USA, ²California State Polytechnic University, USA

This research focuses on the development of an intelligent mobile application to address challenges in selecting suitable sports and predicting athletic performance. The problem of early dropout in youth sports and performance stagnation due to mismatched expectations underscores the need for solutions. The proposed application combines OpenAI and machine learning algorithms to evaluate user inputs, including physical attributes, psychological traits, and environmental factors, to recommend appropriate sports and predict future performance. Key technologies include generative AI for personalized sports recommendations and machine learning algorithms for performance prediction in golf, trained on professional player data. Challenges such as data accuracy, generalization of algorithms, and prompt optimization were tackled through user feedback and rigorous performance testing. The use of accuracy measurement validated the system's reliability and adaptability, demonstrating its usefulness. By offering accurate and scalable solutions, the application has the potential to create sustained athletic engagement and enhance decision-making for users across.

Open AI, Machine Learning, Flutter, Sports Recommendation