Artificial intelligence (AI) is increasingly embedded in school-based physical education (PE) and in elite and recreational sport, promising more individualized instruction, more objective skill assessment, and more precise performance and injury management. This paper presents a systematic narrative review synthesizing recent peer-reviewed literature (2014–2026) on AI applications across two connected domains: physical education pedagogy and sports performance analytics. Following a PRISMA-informed search and thematic synthesis of eighteen primary and review-level sources drawn from databases including Web of Science, Scopus, PubMed, IEEE Xplore, and ERIC, the review identifies four dominant application clusters: personalized and adaptive instruction, automated motor-skill assessment, wearable-sensor performance analytics, and AI-assisted injury-risk prediction. To integrate these findings, the paper proposes and explains an original conceptual framework, the AI–Physical Education Performance Enhancement Model (AI-PPEM), which links data acquisition, AI processing, pedagogical and athletic application, and measurable outcomes through a continuous feedback loop. The review also surveys documented challenges, including data privacy, algorithmic bias, teacher AI-literacy gaps, and limited external validation of predictive models, and outlines directions for future research and practice. The findings suggest that AI functions most effectively as an augmentative tool that extends, rather than replaces, the judgment of physical educators, coaches, and sports-medicine professionals.
artificial intelligence; physical education; sports performance; machine learning; wearable sensors; injury prediction; systematic review.
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