Smart training systems in sports vocational education: Applications of artificial intelligence and data analytics
Sports vocational education is becoming increasingly data-driven, but evidence on integrated artificial intelligence (AI)-enabled training systems remains limited. This work aims to explore the potential of AI and data analytics (DA) to strengthen smart training systems (STS) and boost performance in sports vocational education. The study offers an alternative to traditional technology adoption frameworks and proposes a skill-based model encompassing technological capability, user acceptance, institutional context, and training performance. This is a cross-sectional study of 212 students and trainers from sports vocational institutions in China. Partial least squares structural equation modeling (PLS-SEM), hierarchical regression, common method bias diagnostics, role-based group comparisons, and machine learning-based predictive analysis were used. The results indicate that AI (β = 0.42, p < 0.001) and DA (β = 0.36, p < 0.001) significantly improved STS, while STS positively influenced self-reported performance outcomes (β = 0.48, p < 0.001). AI and DA demonstrated strong direct effects on performance, with STS as an additional partial mediator (accounting for >50% of the variance). The Technology Acceptance Model (TAM) further reinforced the effects of AI and DA on STS and, consequently, on its performance, while the vocational education context further strengthened the STS–performance relationship. These findings were robust, as random forest achieved the highest predictive accuracy (test R2 = 0.75). In general, the study enriches the TAM, the unified theory of acceptance and use of technology, and learning analytics theory in explaining the phenomenon of technology-enabled skill development in sports vocational education.
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