Generative Artificial Intelligence in Didactics of Sports Sciences and Physical Education: A Comprehensive Review of Pedagogical Applications, Teaching Innovations, and Research Implications
International Journal of Sport Studies for Health, cilt.9, sa.3, 2026 (Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 9 Sayı: 3
- Basım Tarihi: 2026
- Doi Numarası: 10.61838/kman.intjssh.4891
- Dergi Adı: International Journal of Sport Studies for Health
- Derginin Tarandığı İndeksler: Scopus
- Anahtar Kelimeler: Academic Integrity, Artificial Intelligence, Chatbots, Curriculum, Education, Motor Skills, Science of Education, Teaching
- Atatürk Üniversitesi Adresli: Evet
Özet
Objective: Physical inactivity contributes to 5.3 million deaths annually, yet physical education (PE) faces persistent challenges, including inadequate instructional time, insufficient teacher preparation, and limited capacity for differentiated instruction. Generative Artificial Intelligence (Gen AI), particularly Large Language Models (LLMs) like ChatGPT, presents unprecedented opportunities for transforming pedagogical practices in sports sciences and PE while introducing critical challenges regarding academic integrity, cognitive development, and preservation of embodied learning central to movement education. This comprehensive review examined: (i) current applications and pedagogical affordances of Gen AI in sports sciences and PE didactics; (ii) alignment with established pedagogical principles; (iii) potential benefits and risks from a didactics perspective; and (iv) evidence-informed frameworks for pedagogically sound integration emphasizing human-AI collaboration. Methods and Materials: An integrative narrative review was conducted across seven databases covering January 2022 to November 2025. From 1,247 initial records, 78 studies meeting the inclusion criteria underwent thematic analysis organized into four domains: teaching support, learning enhancement, assessment innovation, and didactic research. Results: Analysis revealed significant applications, including lesson planning time reductions (35-45%), improved conceptual understanding through AI tutoring, and research efficiency gains. However, critical challenges emerged, including academic integrity violations (23-43% of student work), factual inaccuracies in specialized content, cognitive atrophy concerns (AI-Chatbot Induced Cognitive Atrophy, AICICA), reduced emphasis on embodied learning, and equity issues. Conclusion: Gen AI represents a transformative tool when implemented within robust pedagogical frameworks, preserving essential embodied, social, and affective dimensions of movement education. A hybrid model is recommended, integrating Gen AI for cognitive content delivery and administrative tasks while rigorously preserving face-to-face instruction, kinesthetic learning, and human mentorship. Successful integration requires comprehensive teacher professional development, explicit policies balancing academic integrity with beneficial use, a critical AI literacy curriculum, and ongoing empirical evaluation of long-term outcomes.