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Exploring university physical education teachers' artificial intelligence use intention profiles: a Q-methodology
Xu Sun1, Dinghua Liu2, Xinyang Li3
1School of Physical Education, Hunan University, Changsha, China.
Frontiers in Psychology
|July 24, 2026
Summary
University physical education teachers exhibit varied intentions toward artificial intelligence (AI) integration, viewing it as a tool for efficiency, research, or a potential risk. Tailored AI training and support are crucial for effective implementation in physical education settings.
Area of Science:
- Educational Technology
- Artificial Intelligence in Education
- Sports Science Pedagogy
Background:
- Artificial intelligence (AI) is increasingly integrated into university operations, including teaching, research, and administration.
- Understanding how educators perceive and intend to use AI is crucial for successful adoption.
- University physical education (PE) teachers' unique professional contexts require specific examination regarding AI integration.
Purpose of the Study:
- To explore the diverse profiles of university physical education teachers' intentions regarding AI use.
- To clarify how these teachers position AI within their teaching, research, and professional practices.
- To identify distinct perspectives on the value and risks associated with AI in PE.
Main Methods:
- Q methodology was employed to analyze teachers' perspectives.
- A 42-statement Q-set was developed from literature, policy, and expert interviews.
- Forty-five Chinese university PE teachers participated in an online Q-sorting task, with data analyzed using Ken-Q Analysis.
Main Results:
- Four distinct AI use intention profiles emerged: (F1) AI as a practical assistant for teaching efficiency, (F2) AI within embodied boundaries preserving professional judgment, (F3) AI for research support enhancing academic productivity, and (F4) AI perceived as bringing more risks than relief.
- Teachers' AI intentions were not binary (accept/reject) but nuanced, reflecting varied value and risk perceptions across different professional domains.
- These profiles highlight differing professional orientations toward teaching efficiency, embodied judgment, research support, and perceived technological burden.
Conclusions:
- University PE teachers' AI use intentions are shaped by their professional orientations and perceptions of AI's role in efficiency, embodied judgment, research, and risk.
- AI training and institutional support strategies must be differentiated to align with teachers' specific work contexts and the nature of PE.
- Special attention should be given to the embodied, safety-sensitive, and professionally accountable aspects of physical education when implementing AI.