AI-Empowered Innovation in Reba Dance Inheritance: A Media Convergence Approach to Digital Cultural Preservation

Abstract

The inheritance of intangible cultural heritage dance is increasingly challenged by aging inheritor communities, limited transmission efficiency, and restricted dissemination channels. Under the context of media convergence, emerging digital and artificial intelligence technologies provide new opportunities for transforming traditional inheritance models. This study proposes an AI-empowered innovation framework for Reba dance inheritance from a media convergence perspective. By integrating motion capture technology, artificial intelligence modeling, and immersive media environments, a standardized digital movement database of Reba dance was constructed, and AI-assisted inheritance and choreography systems were developed. Quantitative learning experiments were conducted to compare AI-assisted instruction with traditional teaching methods, and audience evaluations were employed to assess the effectiveness of immersive performance in cultural communication. The results suggest that AI-assisted learning may improve movement accuracy, action similarity, rhythm synchronization, and learning efficiency. Moreover, immersive performance achieved high levels of audience immersion, aesthetic engagement, and cultural cognition, indicating its effectiveness in enhancing public understanding and cultural identity. From a media and societal perspective, this research highlights how artificial intelligence can function as a mediating tool within convergent media environments to support the sustainable inheritance and dissemination of intangible cultural heritage. The proposed framework offers both theoretical insights into digital cultural transmission and practical implications for heritage preservation under contemporary media conditions.