Generative AI-Assisted Chinese Classical Dance Choreography: Originality Definition and Subjectivity Game
Main Article Content
Abstract
Generative AI technology is reshaping the creative ecology of Chinese classical dance choreography and raising new questions concerning originality definition and creative subjectivity. Focusing on the reconstruction of originality standards and the interaction between human intention and algorithmic generation, this paper systematically analyzes the technical paths and practical status of generative AI-assisted classical dance choreography. Qualitative coding results from 12 expert interviews are integrated into the analysis, and a “three-dimensional originality evaluation model” and a “human-machine collaborative subjectivity balance framework” are constructed. The study finds that the originality of AI-assisted classical dance choreography should consider the novelty of technical generation, the uniqueness of cultural expression, and the autonomy of choreographic intention. The subjectivity game follows an evolutionary logic of technical instrumentalization, human-machine collaboration, and subject co-evolution. In the digital age, social empathy remains central to human artistic creation, while generative AI approaches this core through affective computing. A creative paradigm of humanistic guidance and technological empowerment is therefore proposed to support inheritance and innovation in classical dance choreography.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
J. Zhang, “Historical development of Chinese dance: from folklore to Hip Hop,” Research in Dance Education, pp. 1-18, 2025, doi: 10.1080/14647893.2025.2486244.
B. F. Xu, “Chinese Classical Dance,” in X. Baofeng, Ed., Contextual Dictionary of Chinese Cultural Knowledge. Singapore: Springer Nature Singapore, 2025, pp. 607-608, doi: 10.1007/978-981-99-5009-6_10857.
Y. Xia and X. Guan, “An Analysis of the Artistic Conception Beauty of Chinese Classical Dance Based on Du Fu Hainer’s Aesthetics,” Academic Journal of Humanities & Social Sciences, vol. 4, no. 10, pp. 98-102, 2021, doi: 10.25236/AJHSS.2021.041019.
H. Dong, “Discussion on Application of Dance Anatomy in Basic Training Classroom of Chinese Classical Dance,” ICEMC, pp. 1054-1058, 2017, doi: 10.2991/icemct-17.2017.231.
M. Ma and Y. Meng-jie, “Cross-cultural expression of Chinese ballet: Symbol, Identity, Context and Empathy,” Theatre, Dance and Performance Training, vol. 15, no. 4, pp. 671-685, 2024, doi: 10.1080/19443927.2023.2293097.
X. Long, “Study on Style of Chinese Classical Dance and Personalized Creation,” ICESAM, pp. 210-214, 2017, doi: 10.2991/icesame-17.2017.47.
W. J. Trost, C. Labbé, and D. Grandjean, “Rhythmic entrainment as a musical affect induction mechanism,” Neuropsychologia, vol. 96, pp. 96-110, 2017, doi: 10.1016/j.neuropsychologia.2017.01.004.
R. Gao, “Social Education of Ritual and Music Culture and Its Significance,” ICOSSED, pp. 454-462, 2024, doi: 10.2991/978-2-38476-382-5_46.
W. T. Fang, Y. J. Gao, P. H. Lin, and R. Lin, “An Exploration of Aesthetic Experience in Chinese Classical Dance–A Case Study of the Artistic Creation of ”In Praise of Golden Age”,” Journal of Arts and Humanities, vol. 7, no. 4, pp. 01-10, 2018, doi: 10.18533/JOURNAL.V7I4.1374.
Y. Xiong, “The Inheritance and Development of Ethnic Folk Dance in Dance Teaching under the Background of Informatization,” Applied Mathematics and Nonlinear Sciences, vol. 9, no. 1, 2024, doi: 10.2478/amns-2024-0568.
C. Deng, “Analysis of Aesthetic Education Elements and Values in Chinese Classical Music,” International Journal of Education and Humanities, vol. 17, pp. 86-91, 2024, doi: 10.54097/w8mt9a73.
S. Feng, “The copyrightability of AI-generated content: A doctrinal exploration of the pioneering chinese judicial practice,” Computer Law & Security Review, vol. 59, Art. no. 106236, 2025, doi: 10.1016/j.clsr.2025.106236.
W. Liu, H. Shao, J. Yang, X. Li, and G. Li, “The ‘Digital Dance Innovation Project’ Case,” Journal of Cases on Information Technology, vol. 28, no. 1, 2026, doi: 10.4018/JCIT.402406.
Z. Wang, “Artificial intelligence in dance education: Using immersive technologies for teaching dance skills,” Technology in Society, vol. 77, Art. no. 102579, 2024, doi: 10.1016/j.techsoc.2024.102579.
L. Messeri and M. J. Crockett, “Artificial intelligence and illusions of understanding in scientific research,” Nature, vol. 627, no. 8002, pp. 49-58, 2024, doi: 10.1038/s41586-024-07146-0.
M. Schirner, G. Deco, and P. Ritter, “Learning how network structure shapes decision-making for bio-inspired computing,” Nature Communications, vol. 14, pp. 2963, 2023, doi: 10.1038/s41467-023-38626-y.
S. Lei and S. Feng, “The Impact of Generative Artificial Intelligence on Social Robots and Governance Countermeasures,” Journal of Intelligence, vol. 43, no. 7, pp. 154-163, 2024, doi: 10.3969/j.issn.1002-1965.2024.07.021.
J. Wu and Y. Deng, “Reflection on Publicity under the Prospect of Generative Artificial Intelligence,” Nanjing Social Sciences, pp. (7), 2013, doi: 10.15937/j.cnki.issn1001-8263.2023.07.003.
G. Mingjie, M. Jun, and T. Huirong, “The double-edged sword effect of generative artificial intelligence usage on employee creativity: a perspective of conservation of resources theory,” Current Psychology, vol. 44, pp. 8485-8497, 2025, doi: 10.1007/s12144-025-07806-y.
F. Ofli, E. Erzin, Y. Yemez, and A. M. Tekalp, “Learn2Dance: Learning Statistical Music-to-Dance Mappings for Choreography Synthesis,” IEEE Transactions on Multimedia, vol. 14, no. 3, pp. 747-759, 2012, doi: 10.1109/TMM.2011.2181492.
E. D. Zhong, T. Bepler, B. Berger, and J. H. Davis, “CryoDRGN: Reconstruction of Heterogeneous Cryo-EM Structures Using Neural Networks,” Nature Methods, vol. 18, no. 2, pp. 176-185, 2021, doi: 10.1038/s41592-020-01049-4.