<![CDATA[Background. The advancement of Artificial Intelligence (AI) has transformed Physical Education (PE) learning into a more interactive, personalized, data-driven process. However, studies mapping AI utilization and its contributions to PE learning remain limited. Objectives. This study aimed to examine, analyze, and synthesize various forms of AI utilization in PE learning through a Systematic Literature Review (SLR). Method. The review followed PRISMA guidelines and employed the PICO framework to formulate research questions and select relevant studies. A total of 10 scientific articles were selected through purposive sampling based on predefined inclusion and exclusion criteria. Data were collected through document analysis from Scopus and Google Scholar using the Publish or Perish application and analyzed through thematic synthesis. Results. The findings identified five major forms of AI utilization in PE learning: movement analysis, real-time feedback, automated assessment, AI-based learning media, and physical fitness monitoring. These applications enhanced motor skills, learning motivation, student engagement, assessment objectivity, and instructional effectiveness, indicating that AI supports more effective and technology-enhanced learning environments. Conclusion. In conclusion, AI has significant potential to improve PE learning quality through interactive, personalized, and evidence-based instructional practices. This study contributes by providing a comprehensive mapping of AI utilization and insights for PE development.]]>
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