The paradox of experience: generational analysis of AI integration in physical education using the iTPACK framework

Abstract

Introduction Artificial intelligence (AI) is gaining increasing relevance in education, offering both opportunities and challenges for teaching and learning. In physical education (PE), AI has the potential to personalize instruction, enhance teacher-student interaction, and support performance evaluation. However, its application in PE is still emerging, and there is limited knowledge about educators’ understanding of AI. Exploring this knowledge is essential for managing expectations and promoting effective integration. This study examines AI-related knowledge across three educational groups: students in Physical Activity and Sports Sciences (PASS), trainee teachers (TT), and active PE teachers.Objective The main objective is to analyse differences in AI-related knowledge among PASS students, TT participants, and active teachers. By doing so, the study seeks to identify potential enablers or barriers to adopting AI tools in PE, contributing to the broader conversation on educational innovation and digital competence.Method A cross-sectional survey based on the TPACK framework was used to assess five knowledge dimensions: Technological Knowledge (iTK), Technological Pedagogical Knowledge (iTPK), Technological Content Knowledge (iTCK), Technological Pedagogical Content Knowledge (iTPACK), and Ethics, where ‘i’ refers to AI integration. A total of 351 participants from the Valencian Community in Spain took part: 117 active PE teachers, 131 PASS students, and 103 TT participants. The Kolmogorov–Smirnov test confirmed non-normal distribution (p < 0.05), and Kruskal–Wallis tests were conducted to identify group differences. Post-hoc analyses with Mann–Whitney U tests and Bonferroni correction (p < 0.016) further explored specific contrasts.Results and Discussion Statistically significant differences were found across all dimensions. PASS students demonstrated the highest levels of AI-related knowledge and a stronger disposition to use AI in PE, particularly excelling in the iTPACK dimension. This suggests that recent academic exposure better prepares them for technological integration. In contrast, active PE teachers showed the lowest levels across most categories, likely due to limited access to professional development in AI. TT participants scored between these two groups, indicating a transitional stage in their understanding and readiness. The results highlight a generational and experiential divide, where newer educators appear more comfortable with emerging technologies than their more experienced counterparts.Conclusion The findings reveal a paradox: individuals with less teaching experience, such as PASS students, report greater confidence and enthusiasm toward AI integration than in-service and trainee teachers. This gap calls for a rethinking of training and professional development to ensure all educators, regardless of experience level, are prepared to engage with AI tools effectively. Institutions should promote access to AI-related resources and foster continuous learning opportunities to bridge this divide. Future research should explore concrete barriers and facilitators for AI use in PE to inform inclusive and effective integration strategies.

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