Identifying the Drivers of Developing AI-Based Physical Education Teaching Methods in Primary Schools - Mardanzadeh, Zahra; Shetab Boushehri, Seyedeh Nahid; Ghaseminezhad Dehkordi , Atousa; Alam, Shahram (Assessment and Practice in Educational Sciences, 2026)
The objective of this study was to identify the key drivers influencing the development and transformation of physical education teaching methods in primary schools through the integration of artificial intelligence. This qualitative exploratory study employed a fuzzy Delphi method to gather expert consensus on factors shaping AI-based instructional development in physical education. The research population consisted of specialists in artificial intelligence, educational technology, and physical education pedagogy, including university faculty members and national-level practitioners with a minimum of ten years of relevant academic or professional experience. Using purposive and snowball sampling, 15 experts were selected based on criteria of expertise, experience diversity, and willingness to participate. Data collection involved systematic document analysis using a structured extraction form, followed by semi-structured interviews guided by a protocol focused on AI trends, uncertainties, and drivers in physical education. Qualitative data were analyzed using the three-level abstraction laddering approach of Miles and Huberman (1994), moving from descriptive coding to thematic categorization and analytical interpretation. A two-round fuzzy Delphi process screened 112 initial codes, removing low-consensus items and resulting in 86 confirmed indicators that were subsequently synthesized into final driver categories. The inferential results demonstrated that AI-driven transformation in physical education relies on a multi-dimensional set of 25 key drivers spanning technological infrastructure, teacher capacity-building, institutional and policy support, pedagogical innovation, cultural readiness, and ethical considerations. Experts emphasized that modern digital infrastructure, teacher training in AI, ministry-level strategy alignment, and smart educational content are the strongest positive predictors of successful AI adoption. However, concerns were raised regarding reduced human interaction, technological dependency, and widening educational inequality, suggesting significant systemic moderation effects that influence the feasibility and sustainability of AI-based instructional change. The study concludes that achieving effective AI integration in primary school physical education requires holistic alignment across technology, pedagogy, governance, culture, and equity, highlighting the need for coordinated national strategies, targeted teacher development, and context-sensitive implementation frameworks.