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

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.