From choreography to computation: a bibliometric analysis of artificial intelligence in dance research
DOI:
https://doi.org/10.55040/7gg8ka88Keywords:
artificial intelligence, dance, bibliometric, motion capture, generative chereographyAbstract
Artificial intelligence has increasingly influenced dance research through developments in motion imaging, computer vision, pose estimation, virtual reality, generative choreography, and human–AI interaction. Despite the growing literature on dance and artificial intelligence, the scientific structure of this research field has not yet been comprehensively mapped. This article presents a bibliometric analysis of artificial intelligence in dance research based on documents indexed in Scopus from 2000 to 2025. After screening and cleaning, 2,445 English-language articles, reviews, and conference papers were retained from the 7,300 initially exported records. Bibliometrix/Biblioshiny was used for descriptive bibliometric analysis, whereas VOSviewer was used to visualize co-authorship, keyword co-occurrence, bibliographic coupling, and co-citation networks. Annual scientific production increased substantially, with the strongest acceleration occurring after 2020. The literature was concentrated in computer science, human–computer interaction, engineering, multimedia, and digital-performance venues, reflecting the interdisciplinary and technology-oriented development of the field. Collaboration patterns revealed an internationally active but unevenly connected research network, with author-level collaboration concentrated in relatively small clusters. Keyword co-occurrence analysis identified 13 conceptual clusters related to movement recognition, motion capture, virtual reality, machine learning, generative choreography, immersive pedagogy, cultural preservation, rehabilitation, wearable sensing, and human–AI interaction. Bibliographic coupling identified seven source communities, whereas co-citation analysis showed that the intellectual base was concentrated in music-to-dance generation, motion synthesis, virtual dance training, deep learning, human-motion modeling, and generative AI. Overall, AI-related dance research has evolved from isolated technology-assisted applications into an expanding interdisciplinary field. Future research should evaluate the pedagogical, artistic, ethical, cultural, and practical effectiveness of AI across dance education, choreography, performance, heritage preservation, and rehabilitation.
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