EMOTION-AWARE TUTORING SYSTEMS FOR PERFORMING ARTS

Abstract

Digital performing arts education Emotion-aware tutoring systems are a new development in the field of digital performing arts, which combines the concept of affective computing with multimodal learning analytics to improve the growth of expressive skills. Conventional online training systems have been emphasizing more on technical accuracy pitch accuracy, movement accuracy or dialogue accuracy but have failed to consider the emotional shades required in artistic excellence. The current research introduces and analyzes an AI-based framework that helps to capture emotional stimuli based on face expression analysis, voice prosody analysis, gesture monitoring, and movement patterns. The system is capable of detecting emotional deviations, feedback provision, and helping the learner understand themselves by three examples in dance, theatre, and vocal music. The quantitative data reveals that there are large improvements in the emotional accuracy (32-40%), expressive clarity, and performance coherence in all domains. Adaptive feedback mechanism in the system leads to increased engagement and elevation of emotional literacy where learners can tune the emotions and the expressive behavior. The results reveal the significance of incorporating emotion-sensitive technologies into the arts pedagogy and emphasize the importance of the hybrid human-AI co-learning models in the process of balancing between technical instructions and creative autonomy. The ethical concerns, such as emotional privacy, cultural difference, and algorithmic prejudice are addressed to make sure that there is responsible execution. In general, the paper shows that emotion-sensitive tutoring systems can provide a revolutionary method of performing arts education, enhancing more expressive growth, and overcome the traditional disadvantages of teaching digital arts in a digital setting.