The application of generative AI in university dance education: effects on dance skills, engagement and learning motivation

Abstract

Introduction: Generative artificial intelligence (GenAI) is rapidly reshaping higher education. However, evidence remains limited regarding its pedagogical utility and learning benefits in university dance learning environments. Methods: This study employed a quasi-experimental design with 60 university students who were randomly assigned to either an experimental group using a GenAI-based teaching tool (GEN Dance) or a control group using a conventional multimedia tool. GEN Dance supported real-time, interactive dance learning activities. Results: The GenAI-supported condition (GEN Dance) demonstrated statistically significant advantages over the conventional multimedia condition across all three assessed learning-related domains. Discussion: These findings suggest that GenAI can enhance learning outcomes in higher education dance contexts and support more interactive instructional experiences. This study extends the emerging literature on GenAI-enabled teaching and provides empirical evidence for the integration of GenAI tools in university dance learning environments.