Advanced Artificial Intelligence in Sports Science A Guide to Models, Applications, and Ethical Challenges

Part 1: Theoretical Foundations of Artificial Intelligence in Sports Science 1. Introduction to the Concept of AI in Sports Science 2. Fundamentals of Natural Language Processing and Large Language Models 3. Explainable Artificial Intelligence in Sport Scientists 4. Big Data in Sports Science 5. AI agent in Sport Science Part 2: Practical AI Modeling for Sports Science 6. Modeling Environments and Their Relevance for Sports Science 7. Cross-Platform AI Modeling in Sports Science 8. Cross-Platform XAI for Time-Series Modeling in Sports Science 9. Cross-Platform Unsupervised Learning in Sport Science 10. Cross-Platform Bayesian Approach 11.Recommended Reporting Elements Part 3: Future Trends, Ethics, and Challenges in AI and Sports Science 12. Ethical, Legal, and Societal Foundations of AI in Sports Sciences 13. Emerging AI Technologies and Future Trends in Sports Sciences 14. Methodological, Data-Related, and Governance Challenges 15. Governing Artificial Intelligence in Sports Science Academia 16. Implementing Trustworthy AI in Sport Part 4: Conclusion

Biography

Michal Bozděch is an Assistant Professor at the Faculty of Sports Studies, Masaryk University (Czech Republic), specializing in artificial intelligence, data analysis, and research methodology in sports science. His research focuses on the application of machine learning, Bayesian approaches, and neural networks to performance analysis, talent identification, and athlete development, with a particular emphasis on the junior-to-senior transition in sport.

He is the author of more than 40 scientific publications and has published in leading journals. He serves as a principal investigator and collaborator on multiple research projects and is actively involved in international academic cooperation.

In addition to his research, he teaches courses in methodology, data analysis, and artificial intelligence, and contributes to the development of innovative educational approaches integrating AI into higher education. His work bridges statistical reasoning, domain expertise, and applied artificial intelligence in sports science