Analysis of the Mode Exploration and Practical Effect of AIGC Empowering Dancesport Education

Jing Liu and Penghao Gao

Pinnacle Academic Press Proceedings Series, 2026, vol. 11, 102-108

Abstract: With the rapid development of artificial intelligence, generative artificial intelligence (AIGC) is gradually penetrating all aspects of education, bringing unprecedented possibilities for educational innovation and pedagogical transformation. As an important educational form that intricately combines art and sports, traditional teaching models in dance sports education frequently suffer from systemic issues such as insufficient personalization, limited instructional resources, and delayed performance feedback. This paper focuses on the paradigm shift brought about by AIGC-enabled dance sports education. First, it comprehensively explains the current status of dance sports education and the unique opportunities presented by AIGC, thereby clarifying the research significance and primary purpose. By systematically analyzing AIGC technology alongside dance sports education theory, and combining literature research, empirical case analysis, and in-depth stakeholder interviews, this study explores the multifaceted integration of AIGC. Specifically, it investigates personalized and interactive teaching models, dynamic resource integration, and intelligent push notification systems tailored for dance sports. Furthermore, the study rigorously analyzes practical implementation results from the critical perspectives of teaching effectiveness, educational resource allocation, and overall teaching efficiency. It also identifies the primary technical and pedagogical challenges faced in the practical application of AIGC, proposing corresponding optimization strategies to mitigate these barriers. Finally, the research findings are synthesized to provide a robust theoretical and practical reference for the further application and sustainable development of AIGC technologies within dance sports education and related disciplines.

Keywords: generative ai; educational innovation; dance sports; personalized teaching; educational technology (search for similar items in EconPapers)
Date: 2026
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