A Study on Innovative Paths and Practical Effectiveness of AI Technology in Enabling Dance Teaching in Higher Education Institutions
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Abstract
Artificial intelligence (AI) technologies are increasingly transforming higher education by enabling data-driven and personalized teaching paradigms. To overcome limitations in conventional dance instruction related to subjective assessment, insufficient individualized guidance, and restricted interactive feedback, this study establishes an AI-enabled teaching framework integrating motion capture, computer vision, adaptive learning recommendation, and intelligent virtual assistants. A semester-long teaching experiment was conducted in which multidimensional indicators, including movement accuracy, choreography complexity, classroom interaction, and learning motivation, were quantitatively analyzed. Experimental results demonstrate that students in the AI-supported group achieved significantly greater improvements in standardized skill assessments and exhibited superior originality and structural coherence in dance improvisation tasks. The proposed framework enhances teaching precision and learner engagement while providing a methodological reference for multimodal motion analysis, intelligent sensing, and human–machine interaction. The findings also offer insights into data-driven signal acquisition and real-time perception technologies applicable to intelligent monitoring and advanced sensing systems.
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