ArticleScientific reports2026
AI-enhanced virtual reality martial arts training: how technology readiness, instructional design, usefulness, and instructor competency drive learning performance through cognitive absorption.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Artificial intelligence–enhanced virtual reality (AI-VR) holds significant potential for complex psychomotor skills training, such as in martial arts. However, the psychological pathways through which its features influence learning outcomes are not well understood. This study introduces and tests the Technology-Enhanced Experiential Learning (TEEL) framework, which posits cognitive absorption as the central mediator between key factors and learning performance. A mixed-methods design was employed. Quantitatively, survey and system-usage data were collected from 847 martial artists across 23 facilities after six supervised AI-VR sessions. Structural equation modeling (SEM) and bootstrap mediation analyses were used to test the framework. Qualitatively, 45 semi-structured interviews were thematically analyzed and integrated with the quantitative findings. The model demonstrated strong fit and explanatory power (R² = 0.732 for Learning Performance). Technology Readiness, Instructional Design Quality, Instructor Competency, and Perceived Usefulness all significantly predicted Cognitive Absorption (β = 0.387 to 0.251, p < 0.001), which in turn strongly predicted Learning Performance (β = 0.791, p < 0.001). Cognitive Absorption fully mediated all antecedent relationships. The AI-enhanced model outperformed a VR-only baseline, and usage analytics confirmed significant skill improvement, with strike accuracy increasing from 64.2% to 87.6%. The findings validate the TEEL framework, establishing cognitive absorption as the core mechanism through which technological and pedagogical factors enhance learning in AI-VR martial arts training. This underscores the value of AI components and highlights design priorities for creating deeply engaging and effective training systems.
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