AI-Based Dance Evaluation Systems and Personalized Instruction: Possibilities and Boundaries of Dance Education in the Intelligent Era

Abstract

With the rapid advancement of intelligent technologies, AI-based dance evaluation systems have increasingly been integrated into dance education to support movement recognition, performance assessment, and personalized instruction. This study examines both the pedagogical possibilities and inherent limitations of such systems. The findings indicate that AI can enhance technical accuracy, expand learning accessibility, and provide individualized training pathways for learners with diverse skill levels. However, dance is fundamentally an embodied and expressive art form grounded in somatic awareness, emotional presence, and cultural meaning-elements that cannot be fully captured through quantifiable movement data. AI-based evaluation models may inadvertently reinforce singular aesthetic standards and overlook cultural diversity, while the role of the dance teacher in guiding artistic interpretation and providing relational support remains irreplaceable. Therefore, the integration of AI in dance education should be approached as a strategy of complementarity rather than substitution, ensuring that technological precision is balanced with human-centered artistic and cultural values.