Artificial Intelligence dalam Pendidikan Musik: Anlisis Bibliometrik Berbasis Google Scholar 2021–2025 - Urfan Saniylabdhawega Ridhwan; Azhardi Wisnugraha (Grenek: Jurnal Seni Musik, 2026)
This study aims to map the scientific development and trends of Artificial Intelligence (AI) in Music Education during the 2021–2025 period. Utilizing a bibliometric analysis of 200 high-impact articles retrieved from Google Scholar via Publish or Perish, the findings reveal a robust scholarly impact, accumulating 1,465 citations, an average of 7.33 citations per paper, and an h-index of 17. However, cross-disciplinary collaboration remains relatively low, as indicated by an author-per-paper metric of 1.73. The temporal dynamics demonstrate a substantial 300% surge in publications during the 2023–2024 transition, marking the onset of technological democratization and a substantive shift from conceptual theories to practical field implementations. Visual analysis through VOSviewer confirms a strong integration between the musical arts and computer science. This study successfully provides a novel theoretical roadmap that bridges the humanities and algorithmic sciences for the future. Through this mapping, it is proven that contemporary AI technology functions not merely as a supplementary tool, but as a learning partner that reshapes how music theory is taught, assists students in independent instrument practice, and shifts the role of music educators into technology facilitators. These evaluative insights offer an essential empirical guide for designing AI-based music curricula and directing future research agendas in the digital era.