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Core Competency Development of Physical Education Teachers in Artificial Intelligence-Driven STEAM Education
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DOI：10.1123/jtpe.2025-0055.png)
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Abstract
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Objective: This study systematically examined how an artificial intelligence-driven STEAM teaching model influences the professional development of physical education teachers across the core domains of teaching practice, interdisciplinary instruction, and educational technology application. Methods: A 16-week, mixed-method intervention was conducted with 40 physical education teachers, who were randomly assigned to an experimental group using an artificial intelligence-assisted STEAM teaching system or a control group using conventional methods. Results: The study revealed that teacher competency development followed a nonlinear, four-stage trajectory (adaptation, development, integration, and stabilization), with a notable percentage of participants experiencing temporary regression, termed integration fatigue. Structural equation modeling confirmed that educational technology competency significantly enhanced teaching practice, both directly and indirectly through interdisciplinary teaching, with novice and experienced teachers demonstrating distinct developmental patterns. Conclusion: These findings highlight the necessity for stage-sensitive and differentiated support systems to help teachers, particularly novices, effectively navigate the cognitive demands of integrating advanced technology and interdisciplinary instructional models.
Keywords:
AI-assisted instruction
teacher training
integration fatigue
competency retention
physical education innovation
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