From Deep Learning to Digital Pedagogy: A Bibliometric Study of Artificial Intelligence in Music Education
Abstract
Artificial intelligence is finding more and more applications in the field of music education in the form of automated performance feedback, adaptive practice systems, and online learning platforms. Since the sphere of this multidisciplinary approach that includes education, music, and computing is growing at a rapid pace, a bibliometric snapshot can be used to explain the trend of the literature development and reveal the areas of its focus. This paper presents a literature review on AI and music education and maps it with a Scopus-export dataset of 231 articles on that topic that were published between 2002 and 2025 in English. The analysis unites the increase in publications, leading journals, countries in which published papers have most of the authors, keywords primarily used, and the high-citation publications. The analysis indicates that there is a strong recent trend: 223 out of 231 publications have been released since 2021, and the trend is higher in 2024 (71 articles) and 2025 (52). Applied Mathematics and Nonlinear Sciences (23 articles), Computational Intelligence and Neuroscience (13), and Wireless Communications and Mobile Computing (11) are the most referred sources, meaning that there is a very strong representation of computational and engineering sources. The pattern of affiliation reveals that China is the leading contributor (183 papers) then South Korea (13) is followed by Malaysia (8). The topic of keyword frequency focuses on the music, music education, artificial intelligence, and deep learning, which indicates the field that is inclined towards AI-driven systems and modelling. The article with the highest number of citations in the data set is the article titled development and applications of artificial intelligence in music education (2023) which has 95 citations. In general, the literature is growing at a high rate and it is geographically and methodologically focused which means that it needs to be widely collaborated and well education-based assessment of learning outcomes in real classroom environments.
References
Li, C. (n.d.). System Design and Platform Implementation for AI-Based Metaverse Music. https://doi.org/10.4108/eai.19-5-2023.2334381
Gioti, A.-M. (2021). Artificial Intelligence for Music Composition (pp. 53–73). Springer, Cham. https://doi.org/10.1007/978-3-030-72116-9_3
Biernoff, L., & Blom, D. (2002). Non-Western Ensembles: Crossing Boundaries and Creating Interstices in Cross-Cultural Educational Contexts. Research Studies in Music Education, 19(1), 22–31. https://doi.org/10.1177/1321103X020190010401
Ferm, C., & Johansen, G. (2008). Professors’ and trainees’ perceptions of educational quality as related to preconditions of deep learning in musikdidaktik. British Journal of Music Education, 25(2), 177–191. https://doi.org/10.1017/s0265051708007912
Vass, E. (2018). Musical co-creativity and learning—the fluid body language of Receptive-Responsive Dialogue. Human Arenas, 1(1), 56–78. https://doi.org/10.1007/s42087-018-0009-7
Pati, K., Gururani, S., & Lerch, A. (2018). Assessment of student music performances using deep neural networks. Applied Sciences, 8(4), 507. https://doi.org/10.3390/app8040507
Carey, G., Coutts, L., Grant, C., Harrison, S. D., & Dwyer, R. (2018). Enhancing learning and teaching in the tertiary music studio through reflection and collaboration. Music Education Research, 20(4), 399–411. https://doi.org/10.1080/14613808.2017.1409204
Cheng, L. (2025). The impact of generative AI on school music education: Challenges and recommendations. Arts Education Policy Review, 1–8.
Li, H., Chen, Z., Sun, M., Tong, J., & Liu, M. (2025). Educational Exploration of the Integration of Artificial Intelligence and Music Education. Deleted Journal, 4(2), 23. https://doi.org/10.26789/ijest.v4i2.2098
Dan. (n.d.). Artificial Intelligence Technology Assisted Music Teaching Design. https://doi.org/10.1155/2021/9141339
Zheng, H., & Dai, D. (2022). Construction and Optimization of Artificial Intelligence-Assisted Interactive College Music Performance Teaching System. Scientific Programming, 2022, 1–9. https://doi.org/10.1155/2022/3199860
Zhang, C., & Li, H. (2022). Adoption of Artificial Intelligence Along with Gesture Interactive Robot in Musical Perception Education Based on Deep Learning Method. International Journal of Humanoid Robotics, 19(03), 2240008:1-2240008:18. https://doi.org/10.1142/s0219843622400084
Xing, W., Slowik, A., & Peter, J. D. (2024). Edge-cloud computing oriented large-scale online music education mechanism driven by neural networks. 13, 55. https://doi.org/10.1186/s13677-023-00555-y
Yu, X., Ma, N., Zheng, L., Wang, L., & Wang, K. (2023). Developments and applications of artificial intelligence in music education. Technologies, 11(2), 42. https://doi.org/10.3390/technologies11020042
Yu, H., & Zou, Z. (2023). The music education and teaching innovation using blockchain technology supported by artificial intelligence. International Journal of Grid and Utility Computing, 14(2–3), 278–296. https://doi.org/10.1504/IJGUC.2023.131020
Zhao, R. (2025). AI-Driven Music Composition and Its Impact on Musical Styles: A Comprehensive Review. Education Insights., 2(9), 239–249. https://doi.org/10.70088/eagegp03
Zhang, Y., Fen, B. W., Zhang, C., & Pi, S. (2024). Transforming Music Education Through Artificial Intelligence: A Systematic Literature Review on Enhancing Music Teaching and Learning. International Journal of Interactive Mobile Technologies, 18(18), 76–93. https://doi.org/10.3991/ijim.v18i18.50545
Liu, S., Yang, Q., & Gong, T. (2023). Combined Application of Video Semantic Understanding Technology for music video information learning. Computer-Aided Design and Applications, 34–44. https://doi.org/10.14733/cadaps.2023.s10.34-44
Wang, X. J. (2022). The Influence of Artificial Intelligence Technology on Smart Music Teaching Driven by Mobile Internet. Wireless Communications and Mobile Computing, 2022, 1–13. https://doi.org/10.1155/2022/6766651
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright for this article is retained by the author(s), with first publication rights granted to the journal. This is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (https://creativecommons.org/licenses/by-nc-nd/4.0/).
