Analysis of the Mode Exploration and Practical Effect of AIGC Empowering Dancesport Education
DOI:
https://doi.org/10.71222/h5w51q65Keywords:
generative ai, educational innovation, dance sports, personalized teaching, educational technologyAbstract
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.References
1. S. Park and Y. Kim, "Leveraging educational technology in liberal arts dance sports: Exploring effectiveness and sustainable application," Sustainability, vol. 16, no. 19, p. 8491, 2024.
2. Z. Hu, "Research on the application of virtual reality technology in the teaching of sports dance in colleges and universities," in *2021 2nd International Conference on Artificial Intelligence and Education (ICAIE)*, pp. 414–418, June 2021.
3. X. Liu, "Research on college sports dance lesson teaching," in *Proceedings of the 2nd International Conference on Green Communications and Networks 2012 (GCN 2012): Volume 3*, Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 239–245, Jan. 2013.
4. I. M. Soronovich, E. V. Chaikovsky, and V. Pilevskaya, "Features of functional support of competitive activity in sports dance given the differences prepared by partners," Physical Education of Students, vol. 17, no. 6, pp. 78–87, 2013.
5. J. Sun and H. Tang, "Research on sports dance video recommendation method based on style," Scientific Programming, vol. 2022, no. 1, p. 7089057, 2022.
6. M. Guettala, S. Bourekkache, O. Kazar, and S. Harous, "Generative artificial intelligence in education: Advancing adaptive and personalized learning," Acta Informatica Pragensia, vol. 13, no. 3, pp. 460–489, 2024.
7. J. Batista, A. Mesquita, and G. Carnaz, "Generative AI and higher education: Trends, challenges, and future directions from a systematic literature review," Information, vol. 15, no. 11, p. 676, 2024.
8. Y. Dai, A. Liu, and C. P. Lim, "Reconceptualizing ChatGPT and generative AI as a student-driven innovation in higher education," Procedia CIRP, vol. 119, pp. 84–90, 2023.
9. P. Nedungadi, K. Y. Tang, and R. Raman, "The transformative power of generative artificial intelligence for achieving the sustainable development goal of quality education," Sustainability, vol. 16, no. 22, p. 9779, 2024.
10. M. Alier Forment, F. J. García Peñalvo, and J. D. Camba, "Generative artificial intelligence in education: From deceptive to disruptive," International Journal of Interactive Multimedia and Artificial Intelligence, vol. 8, no. 5, pp. 5–14, 2024.
11. Y. Jin, L. Yan, V. Echeverria, D. Gašević, and R. Martinez-Maldonado, "Generative AI in higher education: A global perspective of institutional adoption policies and guidelines," Computers and Education: Artificial Intelligence, vol. 8, p. 100348, 2025.
12. N. J. Francis, S. Jones, and D. P. Smith, "Generative AI in higher education: Balancing innovation and integrity," British Journal of Biomedical Science, vol. 81, p. 14048, 2025.
13. W. M. Lim, A. Gunasekara, J. L. Pallant, J. I. Pallant, and E. Pechenkina, "Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators," The International Journal of Management Education, vol. 21, no. 2, p. 100790, 2023.
14. U. Mittal, S. Sai, V. Chamola, and D. Sangwan, "A comprehensive review on generative AI for education," IEEE Access, vol. 12, pp. 142733–142759, 2024.
15. I. Pesovski, R. Santos, R. Henriques, and V. Trajkovik, "Generative AI for customizable learning experiences," Sustainability, vol. 16, no. 7, p. 3034, 2024.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Jing Liu, Penghao Gao (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.

