DATA-DRIVEN INSIGHTS FOR PERFORMING ARTS TEACHING - Shah, Jaimeel; Patra, MrChandan; Shalini K, Shantha; Srivastava, Swati; Kalra, Simran; Bhanuse, Vijaykumar (ShodhKosh: Journal of Visual and Performing Arts, 2025)
The convergence of artificial intelligence and education of performing arts has introduced possibilities of improving creativity, assessment and pedagogy. This work is a proposal to develop a Data-Driven Pedagogical Model of Performing Arts (DDPMPA) that combines multimodal analytics such as motion, audio, emotional and textual data to create holistic information about the performance of learners. The framework uses the technique of feature extraction, fusion, and machine learning algorithms to measure both expressive and technical aspects of performance on an adaptive feedback provided to instructors and learners. The experimental findings in the fields of music and dance as well as theatre indicate the presence of quantifiable changes in the learner engagement, expressiveness, and reflective self-regulation. The system does not only make the evaluation more transparent and precise but also retains the subjective and affective nature of artistic learning. The study reveals the prospects of the data-driven feedback mechanism to supplement conventional training, allowing the creation of the ecosystem that is co-creative, where AI serves as a complement, not as a substitute, to human intuition. The study extends the developing sphere of AI-based performing arts education, encouraging the customized, evidence-based, and ethically-based learning settings.