AI in sports-sciences education in Türkiye: A qualitative study informing the artificial intelligence sports training systems integration model (AISTSIM)

Erol Baykan; Ergun Gide; Mahmoud Elkhodr; Erol Baykan; Ergun Gide; Mahmoud Elkhodr

doi:10.3934/steme.2026027

STEM Education

2026, Volume 6, Issue 4: 659-696. doi: 10.3934/steme.2026027

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Research article

AI in sports-sciences education in Türkiye: A qualitative study informing the artificial intelligence sports training systems integration model (AISTSIM)

Erol Baykan ^{1
,},
Ergun Gide ^{2
,},
Mahmoud Elkhodr ^{2,
,
, ,}

1.
Faculty of Sports Sciences, Yozgat Bozok University, Yozgat, Türkiye; erol.baykan@bozok.edu.tr
2.
School of Engineering and Technology, Central Queensland University (CQU), Sydney, NSW, Australia; e.gide1@cqu.edu.au, m.elkhodr@cqu.edu.au

Academic Editor: William Guo

Received: 09 January 2026 Revised: 20 May 2026 Accepted: 10 June 2026 Published: 02 July 2026

This study examined the integration challenges and opportunities for artificial intelligence (AI) in sports-sciences education through the development of the artificial intelligence sports training systems integration model (AISTSIM). A qualitative case study methodology was employed, drawing on semi-structured interviews with 15 academics from Yozgat Bozok University's Faculty of Sports Sciences to investigate perceptions of AI implementation, associated challenges, and ethical considerations. Thematic analysis revealed support for AI's capacity to enhance personalized learning and evidence-based training practices, while identifying significant concerns regarding data security, faculty preparedness, infrastructure, and the need for human oversight. Inter-coder reliability was established through Cohen's kappa coefficients ranging from 0.625 to 0.867, indicating substantial to almost perfect agreement. The findings informed the AISTSIM as an empirically informed conceptual implementation framework organized around five stages: data foundation, intelligent processing, interactive applications, personalized output, and comprehensive system integration. The model incorporates human-centered decision points and ethical safeguards while addressing institutional readiness and educational platform compatibility. The study contributes a theoretically positioned framework for AI integration in sports-sciences education and identifies validation pathways for future Delphi review, pilot deployment, usability testing, and longitudinal evaluation.
- artificial intelligence,
- sports-sciences education,
- faculty perceptions,
- qualitative thematic analysis,
- human-in-the-loop oversight,
- AISTSIM, human-centered AI,
- sports pedagogy
Citation: Erol Baykan, Ergun Gide, Mahmoud Elkhodr. AI in sports-sciences education in Türkiye: A qualitative study informing the artificial intelligence sports training systems integration model (AISTSIM)[J]. STEM Education, 2026, 6(4): 659-696. doi: 10.3934/steme.2026027

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Abstract

This study examined the integration challenges and opportunities for artificial intelligence (AI) in sports-sciences education through the development of the artificial intelligence sports training systems integration model (AISTSIM). A qualitative case study methodology was employed, drawing on semi-structured interviews with 15 academics from Yozgat Bozok University's Faculty of Sports Sciences to investigate perceptions of AI implementation, associated challenges, and ethical considerations. Thematic analysis revealed support for AI's capacity to enhance personalized learning and evidence-based training practices, while identifying significant concerns regarding data security, faculty preparedness, infrastructure, and the need for human oversight. Inter-coder reliability was established through Cohen's kappa coefficients ranging from 0.625 to 0.867, indicating substantial to almost perfect agreement. The findings informed the AISTSIM as an empirically informed conceptual implementation framework organized around five stages: data foundation, intelligent processing, interactive applications, personalized output, and comprehensive system integration. The model incorporates human-centered decision points and ethical safeguards while addressing institutional readiness and educational platform compatibility. The study contributes a theoretically positioned framework for AI integration in sports-sciences education and identifies validation pathways for future Delphi review, pilot deployment, usability testing, and longitudinal evaluation.

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Author's biography Dr. Erol Baykan is a faculty member at the Faculty of Sport Sciences, Yozgat Bozok University, Türkiye. His research interests encompass sport management, artificial intelligence applications, digital transformation, sport technologies, higher education, sport policy, and qualitative research methodologies; Dr. Ergun Gide is a Professor in the School of Engineering and Technology at Central Queensland University, Australia. His research and teaching interests include AI, project management, digital transformation, higher education, and technology-enhanced learning; Dr. Mahmoud Elkhodr is a Senior Lecturer in the School of Engineering and Technology at Central Queensland University, Australia. His research focuses on cybersecurity, the Internet of Things, artificial intelligence, digital health, smart cities, and technology-enhanced education

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