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AI-Enabled Dance Aesthetics in Higher Education: Rationale, Model, and Course Governance

Author(s)

Chen Wang

Affiliation(s)

School of Art & Design, Qingdao University of Technology, Qingdao, China

Abstract

Against the backdrop of New Humanities initiatives and national strategies for education digitalization, the objectives of dance aesthetics education in higher education are shifting from “program-oriented showcasing” to an integrated continuum of cultural understanding — embodied experience — artistic creation — public communication. Artificial intelligence (AI) introduces new methods for classroom feedback, learning analytics, and creative stimulation; however, its value depends on appropriate pedagogical positioning and robust course governance. From the perspectives of embodied learning and multimodal perception, this study delineates AI’s functions in dance aesthetics as tutor, co-creator, and evidence base, and proposes a course model comprising goal architecture, content organization, classroom process, platform support, evaluation language, and data governance. Online activities provide interpretable kinematic cues and learning trajectories; offline activities generate meaning and judgement through oral explication, ensemble rehearsal, and public presentation. The two are mutually corroborated within a materials — vocabulary — context loop. Further arguments address cultural authenticity, algorithmic bias, privacy compliance, faculty development, and cross-campus sharing, and offer implementable governance procedures and scaling paths. We conclude that, when human-centered and evidence-oriented principles are upheld, AI can enhance the granularity of feedback, the imaginative range of creation, and the auditability of course archives—without altering the educational ontology of dance aesthetics.

Keywords

Artificial Intelligence; Dance Aesthetics; Learning Analytics; Pose Estimation; Music-conditioned Generation; Course Governance

References

[1] Wang, Z. (2024). Artificial intelligence in dance education: Using immersive technologies for teaching dance skills. Technology in Society, 77, 102579. [2] Kang, J., Kang, C., Yoon, J., Ji, H., Li, T., Moon, H., Han, J. (2023). Dancing on the inside: A qualitative study on online dance learning with teacher–AI cooperation. Education and Information Technologies, 28(9), 12111–12141. [3] Söö t, A., & Viskus, E. (2013). Teaching dance in the 21st century: A literature review. The European Journal of Social & Behavioural Sciences. [4] Xu, L. J., Wu, J., Zhu, J. D., & Chen, L. (2025). Effects of AI-assisted dance skills teaching, evaluation and visual feedback on dance students’ learning performance, motivation and self-efficacy. International Journal of Human-Computer Studies, 195, 103410. [5] Wang, Y., & Zheng, G. (2020). Application of artificial intelligence in college dance teaching and its performance analysis. International Journal of Emerging Technologies in Learning (iJET), 15(16), 178–190. [6] Li, X. (2021, April). The art of dance from the perspective of artificial intelligence. Journal of Physics: Conference Series, 1852(4), 042011. [7] Xu, S., Rahim, N., & Yahaya, W. A. J. W. (2025). The impact of hybrid learning integrating mobile interaction and AI timely feedback on students’ dance performance, perceived motivation, and engagement. An-Najah University Journal for Research – B (Humanities), 39(10). [8] Wang, X. (2024). Artificial intelligence-driven training and improvement methods for college students’ line dancing. International Journal of Advanced Computer Science and Applications, 15(1). [9] Miko, H., Frizen, R., & Steinberg, C. (2025). Using AI-based feedback in dance education: A literature review. Research in Dance Education. Advance online publication. [10] Wang, F. (2025, September). Construction of street dance basic movement library based on artificial intelligence and analysis of teaching paths. In Proceedings of the International Conference on Mechatronics and Artificial Intelligence (MAI 2025) (Vol. 13795, pp. 426–433). SPIE.