Exploring university physical education teachers' artificial intelligence use intention profiles: a Q-methodology study

IntroductionArtificial intelligence is increasingly entering university teaching, research, and administrative work. This study used Q methodology to examine university physical education teachers' AI use intention profiles and to clarify how they position AI within teaching, research, and professional practice.MethodsA 42-statement Q-set was developed from theoretical models, empirical literature, policy texts, and expert interviews. Forty-five Chinese university physical education teachers completed an online Q-sorting task through Q-sortware, ranking the statements from −5 to +5 and providing explanations for their extreme choices. The Q-sorts were analyzed using Ken-Q Analysis with centroid factor extraction and varimax rotation.ResultsThe analysis revealed four distinct profiles with different understandings of AI use in physical education: (F1) AI as a practical assistant, improving teaching efficiency, (F2) AI within embodied boundaries, preserving professional judgment, (F3) AI for research support, enhancing academic productivity, and (F4) AI brings more risks than relief, resisting added burdens. These profiles indicate that teachers did not simply accept or reject AI, but located its value and risks differently across routine teaching support, embodied classroom judgment, academic production, and added technological burden.DiscussionThese findings show that university physical education teachers' AI use intention is shaped by different professional orientations toward teaching efficiency, embodied judgment, research support, and perceived risk. Consequently, AI training and institutional support should be differentiated according to teachers' work contexts, with greater attention to the embodied, safety-sensitive, and professionally accountable nature of physical education teaching.