EVALUATING THE IMPACT OF AI ON DANCE PEDAGOGY
DOI:
https://doi.org/10.29121/shodhkosh.v6.i5s.2025.6912Keywords:
Artificial Intelligence, Dance Pedagogy, Pose Estimation, Embodied Learning, Human–AI Interaction, Personalized FeedbackAbstract [English]
The Artificial Intelligence (AI) applied to dance pedagogy is a revolutionary innovation in the process of movement competencies instruction, rehearsal, and assessment. This paper assesses how the AI-based instructional systems affect dance education by comparing them to the existing learning theory, principles in motor learning, and the kinesthetic intelligence. The proposed framework, based on constructivism, embodied cognition, and experiential learning, will make AI an intelligent educational companion as opposed to a substitute of human instructors. To examine the movement accuracy, time coordination, and quality of expression, AI technologies that consist of computer vision-based pose estimation, machine learning-based skill evaluation, and sensor-based motion capture are used. A mixed-methodology is used which implies dance students and teachers of different styles and levels of expertise. The data is gathered by video recordings, wearable devices, questionnaires, and semi-structured interviews, which allows quantifying and qualifying it. The AI models are trained to give personalized feedback, identify the movement errors, and give adaptive learning paths, both in real-time and offline learning. Comparative analysis demonstrates that AI-assisted learning results in definitive conclusions of movement accuracy, time, and expressiveness in comparison to conventional pedagogy. Moreover, learners are found to be more engaged, they are more self-aware, and autonomous in practice sessions. The results show that AI systems that are pedagogically aligned can be used to facilitate reflective learning, promote individualized instruction, and supplement teacher-led dance learning.
References
Chen, Y., Wang, H., Yu, K., and Zhou, R. (2024). Artificial Intelligence Methods in Natural Language Processing: A Comprehensive Review. Highlights in Science, Engineering and Technology, 85, 545–550. https://doi.org/10.54097/vfwgas09 DOI: https://doi.org/10.54097/vfwgas09
Goel, P., Kaushik, N., Sivathanu, B., Pillai, R., and Vikas, J. (2022). Consumers’ Adoption of Artificial Intelligence and Robotics in Hospitality and Tourism Sector: Literature Review and Future Research Agenda. Tourism Review, 77(4), 1081–1096. https://doi.org/10.1108/TR-03-2021-0138 DOI: https://doi.org/10.1108/TR-03-2021-0138
Kim, S. S., Kim, J., Badu-Baiden, F., Giroux, M., and Choi, Y. (2021). Preference for Robot Service or Human Service in Hotels? Impacts of the COVID-19 Pandemic. International Journal of Hospitality Management, 93, 102795. https://doi.org/10.1016/j.ijhm.2020.102795 DOI: https://doi.org/10.1016/j.ijhm.2020.102795
Lauriola, I., Lavelli, A., and Aiolli, F. (2022). An Introduction to Deep Learning in Natural Language Processing: Models, Techniques, and Tools. Neurocomputing, 470, 443–456. https://doi.org/10.1016/j.neucom.2021.05.103 DOI: https://doi.org/10.1016/j.neucom.2021.05.103
Lund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., and Wang, Z. (2023). ChatGPT and a New Academic Reality: Artificial Intelligence-Written Research Papers and the Ethics of Large Language Models in Scholarly Publishing. Journal of the Association for Information Science and Technology, 74(5), 570–581. https://doi.org/10.1002/asi.24750 DOI: https://doi.org/10.1002/asi.24750
Pattnaik, P., Sharma, A., Choudhary, M., Singh, V., Agarwal, P., and Kukshal, V. (2021). Role of Machine Learning in the Field of Fiber Reinforced Polymer Composites: A Preliminary Discussion. Materials Today: Proceedings, 44, 4703–4708. https://doi.org/10.1016/j.matpr.2020.11.026 DOI: https://doi.org/10.1016/j.matpr.2020.11.026
Sumi, M. (2025). Simulation of Artificial Intelligence Robots in Dance Action Recognition and Interaction Process Based on Machine Vision. Entertainment Computing, 52, 100773. https://doi.org/10.1016/j.entcom.2024.100773 DOI: https://doi.org/10.1016/j.entcom.2024.100773
Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., and Trichina, E. (2022). Artificial Intelligence, Robotics, Advanced Technologies and Human Resource Management: A Systematic Review. The International Journal of Human Resource Management, 33(6), 1237–1266. https://doi.org/10.1080/09585192.2020.1871398 DOI: https://doi.org/10.1080/09585192.2020.1871398
Wallace, B., Nymoen, K., Torresen, J., and Martin, C. P. (2024). Breaking from Realism: Exploring the Potential of Glitch in AI-Generated Dance. Digital Creativity, 35(2), 125–142. https://doi.org/10.1080/14626268.2024.2327006 DOI: https://doi.org/10.1080/14626268.2024.2327006
Wang, Z. (2024). Artificial Intelligence in Dance Education: Using Immersive Technologies for Teaching Dance Skills. Technology in Society, 77, 102579. https://doi.org/10.1016/j.techsoc.2024.102579 DOI: https://doi.org/10.1016/j.techsoc.2024.102579
Wang, Z., Deng, Y., Zhou, S., and Wu, Z. (2023). Achieving Sustainable Development Goal 9: A Study of Enterprise Resource Optimization Based on Artificial Intelligence Algorithms. Resources Policy, 80, 103212. https://doi.org/10.1016/j.resourpol.2022.103212 DOI: https://doi.org/10.1016/j.resourpol.2022.103212
Yang, L. (2022). Influence of Human–Computer Interaction-Based Intelligent Dancing Robot and Psychological Construct on Choreography. Frontiers in Neurorobotics, 16, 819550. https://doi.org/10.3389/fnbot.2022.819550 DOI: https://doi.org/10.3389/fnbot.2022.819550
Zeng, D. (2025). AI-Powered Choreography Using a Multilayer Perceptron Model for Music-Driven Dance Generation. Informatica, 49(1), 137–148. https://doi.org/10.31449/inf.v49i20.8103 DOI: https://doi.org/10.31449/inf.v49i20.8103
Zhang, L., and Zhang, L. (2022). Artificial Intelligence for Remote Sensing Data Analysis: A Review of Challenges and Opportunities. IEEE Geoscience and Remote Sensing Magazine, 10(4), 270–294. https://doi.org/10.1109/MGRS.2022.3145854 DOI: https://doi.org/10.1109/MGRS.2022.3145854
Zhou, G., Zhang, C., Li, Z., Ding, K., and Wang, C. (2020). Knowledge-Driven Digital Twin Manufacturing Cell Towards Intelligent Manufacturing. International Journal of Production Research, 58(4), 1034–1051. https://doi.org/10.1080/00207543.2019.1607978 DOI: https://doi.org/10.1080/00207543.2019.1607978
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Copyright (c) 2025 Mr. Manas Kumar Swain, S. Simonthomas, Mona Sharma, Preetjot Singh, Nishant Kulkarni, Dr. Rajesh Dev

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