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Artificial intelligence in dance education: Using immersive technologies for teaching dance skills

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  • Wang, Zheng

Abstract

Artificial intelligence (AI) has led to a shift in modern dance education. Immersive technologies have become increasingly common worldwide, helping educators to improve the quality of dance pedagogy and increase the effectiveness of dance training. The article investigates the ways of using immersive technologies powered by artificial intelligence in dance education. The research explores the theoretical literature on dance education and the use of artificial intelligence in dance education and dance choreography. The scholars examine the impact of innovative technology solutions used in dance pedagogical practice on the development of dance skills in students. The study also discusses the functionality of interactive and multimedia dance teaching systems, including AI-powered virtual mentoring and cognitive simulations of human mind operations. The research analysed the use of virtual reality (VR), augmented reality (AR), and mixed reality (MR) in dance education. This research also focuses on mobile applications used for teaching modern dance. The proposed framework for dance education is based on digital technologies, which help to develop dance skills and improve teaching practices. The scholars conclude that the development and improvement of dance skills are possible only if a teacher combines virtual and real environments in everyday practices. The findings can be used by dance teachers, professional dancers, software developers, and researchers who examine the innovative practices and the application of artificial intelligence in dance education. The ecosystem model reframes thinking about approaches to dance education and can serve as the basis for further development of dance courses and dance style teaching modes.

Suggested Citation

  • Wang, Zheng, 2024. "Artificial intelligence in dance education: Using immersive technologies for teaching dance skills," Technology in Society, Elsevier, vol. 77(C).
    • Handle: RePEc:eee:teinso:v:77:y:2024:i:c:s0160791x24001271
    DOI: 10.1016/j.techsoc.2024.102579
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