INTELLIGENT PERFORMANCE EVALUATION IN DANCE TRAINING - Goyal, Eeshita; Likhith S R; Kalele, Girish; Sharma, Divya; Behera, Santosh Ku.; Dingankar, Shreyas (ShodhKosh: Journal of Visual and Performing Arts, 2025)
The paper discusses how intelligent systems can transform the world of dance training, with a focus on how artificial intelligence, computer vision, affective computing, and immersive technologies may be used to analyze the technical and artistic aspects of the performance. The old-style dance pedagogy is deeply based on human interpretation, which, being abundant in cultural and expressive understanding, is subjective and restricted due to its limitation to perceptual limits. The suggested smart assessment scheme will consist of pose estimation, time-related features extraction, stylistic modeling and emotional analysis to produce multi-dimensional ratings that represent accuracy, expressiveness, musicality, and style authenticity. The experimental outcomes can prove a high level of improvements in rhythm alignment, movement smoothness, and expressive clarity with high correlations between the scores obtained through AI and human experts. The results indicate the promise of AI-human pedagogy, in which computer-based intelligence systems provide real-time and data-driven feedback and the teacher provides cultural and interpretive 3. Although facing certain difficulties associated with dataset diversity, artistic subtlety and ethical implications, the analysis shows that intelligent performance assessment has the potential to increase the accessibility, accuracy, and creative exploration of dance education, which will be a significant leap forward in the future state of digitally augmented performing arts.