REAL-TIME AI FEEDBACK FOR PERFORMANCE STUDENTS

Performance-based instruction depends on the timeliness, accuracy, and pedagogically valuable feedback in order to assist in the acquisition of embodied skills. Nevertheless, the conventional instructor-based feedback can be delayed, subjective, and not scaling and restrictive in use during live practice. This article proposes a real-time AI feedback system to performance learning which combines multimodal sensing, low-latency artificial intelligence inference and adaptive feedback control in a closed-loop instructional structure. The proposed system is based on the theories of experiential learning, deliberate practice, embodied cognition, and formative assessment and provides the learners with context-related feedback on performance implementation without interrupting their attention or creative process. The framework is a combination of the visual, auditory and haptic feedback modalities, whose dynamic control is supported by the learner models and instructor-defined pedagogical policies. An experimental set-up with a modular implementation is outlined to assess the feasibility of the system and educational effects. The exemplary analysis findings indicate that the real-time AI feedback can be used within realistic latency limits and also can be used to reduce mistakes quicker and enhance the learning curves than the standard feedback systems. The paper has placed AI as a pedagogical co-agent which enhances but does not supersede instructor expertise. The suggested framework provides a theory-based and scaleable basis of the further development of intelligent learning settings in the fields of music, dance, theatre, sports, and other performance-driven areas.