Digital Readiness and Institutional Constraints: Artificial Intelligence in Hungarian Physical Education Teacher Education

The diffusion of artificial intelligence (AI) is reshaping higher education, yet its integration into practice-oriented teacher education remains uneven. This cross-sectional study examined the AI-related attitudes, knowledge, usage habits, and prospective application intentions of pre-service physical education (PE) teachers in Hungary. Data were collected during the autumn semester of 2025/2026 using the MIATT 2024_rev online questionnaire across seven Hungarian universities offering PE teacher education (N = 416). Principal component analysis (PCA; KMO = 0.741; Bartlett’s test, p < 0.001) yielded two components—communicative–interpretive (PC1) and performance–developmental (PC2)—jointly accounting for 51.7% of the variance; internal consistency was acceptable (Cronbach’s α = 0.693). Respondents rated sport performance analysis (73.3%) as the most relevant pedagogical application of AI; only 7.7% expressed career-displacement concerns. Satisfaction with institutional AI provision was moderate (M = 2.85, SD = 0.85), and knowledge gaps (27.6%) and funding constraints (25.5%) emerged as principal barriers. Part-time and master’s-level students reported significantly greater interest in AI-related coursework than full-time and undivided programme counterparts (p < 0.001), and institutional satisfaction correlated positively with course interest (ρ = 0.192, p < 0.01). Overall, student receptivity outpaces perceived institutional provision, indicating a need for system-level AI integration in PE teacher education.