The Impact of Artificial Intelligence on Animated Dance Creation: A Study of Audience Engagement and Creative Transformation

As a result of advancements in technology and software, the artificial intelligence (AI) has integrated into creative industries. AI has introduced novel approaches to various aspects of dance production, including costume design, choreographic composition, and animated dance creation. As a result, dance has extended beyond the boundaries of live stage performance and has increasingly become integrated into virtual and digital platforms. The creation of dance animation is a complex and multi-layered process that involves the anthropomorphizing of non-living or virtual characters into lifelike forms. It requires the integration of full-body movement, expressive gestures, costume design, in to rhythmic cultural and traditional way to achieve a coherent and believable performance. The primary problem of this research is to determine whether human-created or AI-generated animated dance is more effective and realistic from the audience’s perspective. In addition, the study seeks to investigate the extent to which artificial intelligence influences dance animation in terms of visual quality, emotional expression, and conceptualization. This study adopts a mixed-method research design to examine audience perceptions of AI-generated and human-created animated dance. A purposive sample of fifty participants with prior knowledge of dance and media will be selected. Quantitative data collected through questionnaires and qualitative data collected from audience observation and discussions. The findings of this research AI-generated dance content enhance visual appeal and accessibility, particularly in digital and social media contexts and it may lack the depth of emotional expression typically associated with human performance. The study concludes that AI does not replace traditional dance practices but rather expands the possibilities of dance as a hybrid digital art form.

Artificial Intelligence, Animation, Dance Art, Audience Engagement, Digital Creativity

1. Adshead-Lansdale, J., & Layson, J. (2006). Dance history: An introduction (2nd ed.). Routledge. [Google Scholar] [Crossref]

2. AI Aesthetics. (2018). Strelka Press, December 21, 2018. [Google Scholar] [Crossref]

3. Correia, N. N., & Primett, W. (2025). Understanding the Use of AI in Contemporary Dance: Perspectives from Practitioners. ARTECH 2025, 1–4. https://doi.org/10.1145/3773699.3773723 [Google Scholar] [Crossref]

4. Dance History (2nd Edition). (1994). Routledge, 11 New Fetter Lane, London. [Google Scholar] [Crossref]

5. Foster, S. L. (2011). Choreographing empathy: Kinesthesia in performance. Routledge. [Google Scholar] [Crossref]

6. Generative AI for Character animation: A comprehensive survey of techniques, applications, and future directions [Online forum post]. (2025, April 27). arXiv.org. https://github.com/llm-lab-org/Generative-AI-for-Character-Animation-Survey [Google Scholar] [Crossref]

7. Niu, M., Cao, M., Zhan, Y., Zhu, Q., Ma, M., Zhao, J., Zeng, Y., Zhong, Z., Sun, X., & Zheng, Y. (2025). AniCrafter: Customizing realistic Human-Centric animation via Avatar-Background conditioning in video diffusion models. ArXiv.org. https://doi.org/10.48550/arxiv.2505.20255 [Google Scholar] [Crossref]

8. Generative AI for Character animation: A comprehensive survey of techniques, applications, and future directions [Online forum post]. (2025, April 27). arXiv.org. https://github.com/llm-lab-org/Generative-AI-for-Character-Animation-Survey [Google Scholar] [Crossref]

9. Manovich, L. (2019). AI aesthetics. Strelka Press. [Google Scholar] [Crossref]

10. Wallace, B., Martin, C. P., Tørresen, J., & Nymoen, K. (2021). Learning Embodied Sound-Motion Mappings: Evaluating AI-Generated Dance Improvisation. Creativity and Cognition, 1–9. https://doi.org/10.1145/3450741.3465245 [Google Scholar] [Crossref]

11. Wang, B., Zhou, J., Bai, J., Yang, Y., Chen, W., Wang, F., & Lei, Z. (2024). RealisHuman: a Two-Stage approach for refining malformed human parts in generated images. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2409.03644 [Google Scholar] [Crossref]

12. Xing, Y., Yin, J., & Liu, X. (2025). DANCER: Dance ANimation via Condition Enhancement and Rendering with diffusion model. ArXiv.org. https://doi.org/10.48550/arxiv.2510.27169 [Google Scholar] [Crossref]

• Block-Based Programming for Education: A Comprehensive Analysis of Visual Programming Environments in K-12 Learning
• Management of Academic Libraries and Client Satisfaction Towards Digital Utilization: Basis for Monitoring Library Operations in SOCCSKSARGEN Region.
• Revenue Leakages in TPA Insurance Claims and Corporate Claims: An Institutional Overview of Aster Prime Hospital, Hyderabad
• Technology and Innovation in Hospitality and Tourism: A Management Perspective
• Geospatial Distribution of Tarok Sacred Grove of Langtang North and Langtang South Local Government Areas