IJIET 2026 Vol.16(5): 1153-1160
doi: 10.18178/ijiet.2026.16.5.2584
doi: 10.18178/ijiet.2026.16.5.2584
Generative Artificial Intelligence in University Sports Training: A Mixed-Methods Study
Zhifang Xiao1 and Wentao Guo2,*
1. School of Public Courses, Hunan Mechanical ＆ Electrical Polytechnic, Changsha, China
2. School of Electrical Engineering, Hunan Mechanical ＆ Electrical Polytechnic, Changsha, China
Email: hnjsgwt@163.com (Z.X.); hncsgwt@163.com (W.G.)
*Corresponding author
2. School of Electrical Engineering, Hunan Mechanical ＆ Electrical Polytechnic, Changsha, China
Email: hnjsgwt@163.com (Z.X.); hncsgwt@163.com (W.G.)
*Corresponding author
Manuscript received October 10, 2025; revised November 21, 2025; accepted December 4, 2025; published May 12, 2026
Abstract—This study investigates the application of Generative Artificial Intelligence (GenAI) in university sports training programs. A mixed-methods approach was employed, combining a quasi-experimental design with qualitative interviews. A total of 120 student-athletes from track and field and basketball programs were divided into experimental (GenAI-supported training) and control (traditional training) groups. Quantitative data included pre- and post-intervention performance metrics (e.g., speed, accuracy, endurance), while qualitative data were gathered through semi-structured interviews with 10 coaches and 15 athletes. Results indicated that the experimental group showed statistically significant improvements in performance outcomes compared to the control group (p < 0.05). Thematic analysis revealed that GenAI was perceived as highly effective for personalized training plan generation, real-time technique feedback, and motivational support. However, challenges included data privacy concerns, over-reliance on technology, and the need for specialized trainer upskilling. The findings suggest that GenAI has substantial potential to enhance sports training in higher education settings, but its integration must be pedagogically sound and ethically guided. This study contributes to the growing body of literature on AI in physical education and provides practical implications for sports educators and policymakers.
Keywords—athletic performance, generative artificial intelligence, higher education, mixed-methods research, personalized learning, sports training
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
Keywords—athletic performance, generative artificial intelligence, higher education, mixed-methods research, personalized learning, sports training
Cite: Zhifang Xiao and Wentao Guo, "Generative Artificial Intelligence in University Sports Training: A Mixed-Methods Study," International Journal of Information and Education Technology, vol. 16, no. 5, pp. 1153-1160, 2026.
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).