Research article    |    Open Access
Base for Electronic Educational Sciences 2025, Vol. 6(2) 28-56

Artificial Intelligence (AI) Literacy in Music Education

Ayça Avcı

pp. 28 - 56

Publish Date: September 30, 2025  |   Single/Total View: 0/0   |   Single/Total Download: 0/0


Abstract

This study investigates the emerging role of artificial intelligence (AI) literacy and tools in music education through a systematic document analysis of 54 academic and institutional sources published between 2014 and 2025. The primary aim is to identify the place of AI literacy in music education, conceptualize how it can be integrated to maximize educational benefit, and propose a framework that shifts the discourse from “music with AI” to “AI for music.” Using descriptive analysis, seven main themes were identified: categories of AI tools, purposes of AI tools, AI tools in the context of AI literacy, the role of AI tools in music pedagogy, conceptual foundations of AI literacy, pedagogical–creative–ethical contributions of AI literacy, and ethical issues in AI literacy. Based on these themes, a set of “AI literacy grammar rules” is proposed to ensure purposeful, ethical, and pedagogically meaningful use of AI in music education. The findings indicate that AI literacy not only contributes to pedagogical clarity but also fosters creativity, secures ethical responsibility, and promotes cultural inclusivity in music learning environments. The study concludes that AI literacy and AI tools are complementary elements of strategic importance for shaping the future of music education, while highlighting unresolved ethical challenges.

Keywords: Artificial Intelligence, Ethical Issues, AI Literacy, Music Education, Educational Technology, Pedagogical Innovation, Creative Learning


How to Cite this Article?

APA 7th edition
Avci, A. (2025). Artificial Intelligence (AI) Literacy in Music Education. Base for Electronic Educational Sciences, 6(2), 28-56.

Harvard
Avci, A. (2025). Artificial Intelligence (AI) Literacy in Music Education. Base for Electronic Educational Sciences, 6(2), pp. 28-56.

Chicago 16th edition
Avci, Ayca (2025). "Artificial Intelligence (AI) Literacy in Music Education". Base for Electronic Educational Sciences 6 (2):28-56.

References

    Agres, K., Forth, J., & Wiggins, G. A. (2016). Evaluation of musical creativity and musical metacreation systems. Computers in Entertainment (CIE), 14(3), 1-33.

    Aljemely, Y. (2024). Challenges and best practices in training teachers to utilize artificial intelligence: A systematic review. Frontiers in Education, 9, 1470853. doi:10.3389/feduc.2024.1470853

    Almatrafi, O., Johri, A., & Lee, H. (2024). A systematic review of AI literacy conceptualization, constructs, and implementation and assessment efforts (2019–2023). Computers and Education Open, 100173.

    Berkowitz, A. E. (2024). “Gimme Some Truth”: AI Music and Implications for Copyright and Cataloging. Information Technology and Libraries, 43(3).

    Bowers, C. (2018). The false promises of the digital revolution: How computers transform education, culture, and the environment. New York, NY: Peter Lang.

    Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. doi:10.1191/1478088706qp063oa

    Briot, J.-P., Hadjeres, G., & Pachet, F.-D. (2020). Deep learning techniques for music generation. Springer.

    Cheng, L. (2025). The impact of generative AI on school music education. Arts Education Policy Review. Advance online publication. doi:10.1080/10632913.2025.2451373

    Corbetta, P. (2003). Social research: Theory, methods and techniques. London, UK: Sage.

    Crawford, K., & Joler, V. (2018). Anatomy of an AI system. Retrieved from https://anatomyof.ai

    Crawford, R. (2021). Artificial intelligence in music education: A critical review. Australian Journal of Music Education, 54(2), 23–39.

    Daley, M. (2025). Music education and artificial intelligence: A conversational editorial. Arts, Culture, and Technology (ACT).

    Eremenko, V., Morsi, A., Narang, J., & Serra, X. (2020). Performance assessment technologies for the support of musical instrument learning. In Proceedings of the 12th International Conference on Computer Supported Education (CSEDU 2020) (pp. 629–640). Setúbal, Portugal: SCITEPRESS. doi:10.5220/0009817006290640

    Farquhar, S., Kossen, J., Gal, Y., & Rainforth, T. (2024). Detecting hallucinations in large language models using semantic entropy. Nature, 630, 625–630. doi:10.1038/s41586-024-07421-0

    Fujinaga, I. (2018). Optical music recognition. In M. Müller (Ed.), Fundamentals of music processing (pp. 453–471). Cham, Switzerland: Springer.

    Fujinaga, I., Hankinson, A., & McKay, C. (2014). Optical music recognition research. In I. Fujinaga (Ed.), Music information retrieval (pp. 77–94). Cham, Switzerland: Springer.

    Glesne, C., & Peshkin, A. (1992). Becoming qualitative researchers: An introduction. White Plains, NY: Longman.

    Habib, S. (2024). How does generative artificial intelligence impact student creative thinking? Discover Education, 3(1), 100046.

    Herremans, D., & Chew, E. (2017). MorpheuS: Generating structured music with constrained patterns and tension. IEEE Transactions on Affective Computing, 7(1), 67–80. doi:10.1109/TAFFC.2015.2462832

    Herremans, D., Chuan, C. H., & Chew, E. (2017). A functional taxonomy of music generation systems. ACM Computing Surveys, 50(5), 69. doi:10.1145/3108242

    Holmes, W., Bialik, M., & Fadel, C. (2022). Artificial intelligence in education: Promises and implications for teaching and learning. Boston, MA: Center for Curriculum Redesign.

    Holster, J. D. (2024). Augmenting music education through AI: Practical applications of ChatGPT. Music Educators Journal, 110(4), 36–42. doi:10.1177/00274321241255938

    Huang, C. A., Vaswani, A., Uszkoreit, J., Simon, I., Hawthorne, C., Shazeer, N., & Eck, D. (2020). Music transformer: Generating music with long-term structure. In Proceedings of the International Conference on Learning Representations (ICLR). Retrieved from https://iclr.cc

    Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., … Liu, T. (2025). A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. ACM Computing Surveys. Advance online publication. doi:10.1145/3703155

    Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., … He, X. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), 248. doi:10.1145/3571730

    Johnson, C., Stewart, C., & Boyer, E. (2021). Augmented reality applications in instrumental music education. Journal of Music, Technology & Education, 14(2), 157–173.

    Kowald, D., Schedl, M., & Lex, E. (2020). The unfairness of popularity bias in music recommendation: A reproducibility study. In Proceedings of the 42nd European Conference on Information Retrieval (ECIR 2020) (pp. 35–47). Cham, Switzerland: Springer. doi:10.1007/978-3-030-45442-5_5

    Kuan, Y. C., Chiu, P. H., & Chen, J. L. (2022). Artificial intelligence in music education: Applications and pedagogical implications. British Journal of Music Education, 39(2), 215–231. doi:10.1017/S0265051721000281

    Li, W. (2025). AI-assisted feedback and reflection in vocal music training. Education Research International, 2025, 1–13.

    Li, W., Cui, X., Manoharan, P., Dai, L., Liu, K., & Huang, L. (2025). AI-assisted feedback and reflection in vocal music training: Effects on metacognition and singing performance. Frontiers in Psychology, 16, 1598867. doi:10.3389/fpsyg.2025.1598867

    Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Beverly Hills, CA: Sage.

    Livingstone, S., & Sefton-Green, J. (2016). The class: Living and learning in the digital age. New York, NY: NYU Press.

    Liu, Z. (2025). Exploring automated assessment of primary students’ creative works in a flow-based music programming environment. Journal of Learning Analytics, 12(1), 1–18.

    Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–16). New York, NY: ACM. doi:10.1145/3313831.3376727

    Luckin, R. (2017). Towards a machine learning pedagogy: Generative learning environments for students. Education and Information Technologies, 22(3), 957–971. doi:10.1007/s10639-016-9493-4

    Ma, H. (2025). Exploring the impact of AI tools on musicians’ creative abilities. Frontiers in Psychology, 16, 1500142.

    Mayring, P. (2014). Qualitative content analysis: Theoretical foundation, basic procedures and software solution. Klagenfurt, Austria: Beltz.

    McLeod, J. (2020). Digital notation software in music education: Collaborative and creative possibilities. Music Education Research, 22(5), 529–542. doi:10.1080/14613808.2020.1821903

    Merchán Sánchez-Jara, J. F. (2024). Artificial intelligence-assisted music education: A critical synthesis of challenges and opportunities. Education, 14(11), 1171. doi:10.3390/education14111171

    Merchán Sánchez-Jara, J. F., González Gutiérrez, S., Cruz Rodríguez, J., & Syroyid Syroyid, B. (2024). Artificial intelligence-assisted music education: A critical synthesis of challenges and opportunities. Education Sciences, 14(11), 1171. doi:10.3390/educsci14111171

    Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. Paris, France: UNESCO.

    Morreale, F. (2021). Creative AI: On the democratization and escalation of creativity. Transactions of the International Society for Music Information Retrieval, 4(1), 103–115. doi:10.5334/tismir.69

    Morris, R., & Nomikou, E. (2020). Digital platforms for music performance learning: A case study of SmartMusic. Journal of Music, Technology & Education, 13(3), 283–298. doi:10.1386/jmte_00028_1

    Moura, N., Dias, P., Veríssimo, L., Oliveira-Silva, P., & Serra, S. (2024). Solo music performance assessment criteria: A systematic review. Frontiers in Psychology, 15, 1467434. doi:10.3389/fpsyg.2024.1467434

    National Association for Music Education (NAfME). (2025). Guiding principles, frameworks, and applications for AI in music education. Reston, VA: Author.

    Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. doi:10.1016/j.caeai.2021.100041

    O’Leary, E. (2025). Considering the possibilities and problems of AI in music education: The need for critical literacies. Arts, Culture, and Technology (ACT).

    Organisation for Economic Co-operation and Development (OECD). (2021). AI and the future of skills, volume 1: Capabilities and assessments. Paris, France: OECD Publishing. doi:10.1787/5ee71fbb-en

    Organisation for Economic Co-operation and Development (OECD). (2023). Equity and inclusion in education. Paris, France: OECD Publishing.

    Organisation for Economic Co-operation and Development (OECD). (2024). OECD Digital Economy Outlook 2024 (Vol. 1): Embracing the technology frontier. Paris, France: OECD Publishing. doi:10.1787/a1689dc5-en

    Papadopoulos, G., et al. (2020). Virtual reality applications in music performance training: A review. Frontiers in Psychology, 11, 556. doi:10.3389/fpsyg.2020.00556

    Park, B. (2022). Analysis of research trends related to artificial intelligence in Korean music field. Journal of Next-Generation Convergence Technology Association, 6, 570–578.

    Park, J. R. (2019). A study on technology and artificial intelligence applied to music production. Journal of Music Theory, 33, 108–143.

    Pasquier, P. (2019). Generative music and artificial intelligence. Arts, 8(3), 92. doi:10.3390/arts8030092

    Patton, M. Q. (2015). Qualitative research & evaluation methods (4th ed.). Thousand Oaks, CA: Sage.

    RAND Corporation. (2025). Uneven adoption of artificial intelligence tools among U.S. teachers and principals (RR-A134-25). Santa Monica, CA: Author.

    Rohrmeier, M. (2022). On creativity, music’s AI-completeness and four challenges for AI in music. Transactions of the International Society for Music Information Retrieval, 5(1), 251–266. doi:10.5334/tismir.130

    Schedl, M., Yang, Y., & Herrera, P. (2016). Introduction to intelligent music systems and applications. ACM Transactions on Intelligent Systems and Technology, 8(2), 1–8. doi:10.1145/2991468

    Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Cambridge, UK: Polity Press.

    Shin, W., & Cheol, K. M. (2020). Music artificial intelligence: A case of Google Magenta. Jeju National University Tourism, Business, and Economic Research Institute, 40, 21–28.

    Smith, J., & Johnson, R. (2020). The impact of AI music software on music education. Journal of Music Education, 45(2), 78–92.

    Song, H., Liu, Y., Li, K., Ding, L., Sun, X., & Zhang, X. J. (2020). Reform and future prospects in education based on 5G technology. In Proceedings of the 3rd International Conference on Education Technology and Information System. Hangzhou, China.

    Sturm, B. L., Iglesias, M., Ben-Tal, O., & Miron, M. (2019). Artificial intelligence and music: Open questions of copyright law and engineering praxis. Arts, 8(3), 115. doi:10.3390/arts8030115

    Tuomi, I. (2018). The impact of artificial intelligence on learning, teaching, and education. Brussels, Belgium: European Commission Joint Research Centre.

    U.S. Department of Education. (2023). Artificial intelligence and the future of teaching and learning: Insights and recommendations. Washington, DC: Author. Retrieved from https://www.ed.gov/ai

    United Nations Educational, Scientific and Cultural Organization (UNESCO). (2023). Guidance for generative AI in education and research. Paris, France: Author.

    Weber, R. P. (1989). Basic content analysis. Newbury Park, CA: Sage.

    Webster, P. R. (2019). Emerging technologies for music learning. In R. Mantie & G. D. Smith (Eds.), The Oxford handbook of music making and leisure (pp. 457–476). New York, NY: Oxford University Press.

    Williamson, B., & Piattoeva, N. (2022). Education governance and datafication. Critical Studies in Education, 63(2), 141–156. doi:10.1080/17508487.2020.1861049

    Woolf, B. P., Lane, H. C., Chaudhri, V. K., & Kolodner, J. L. (2013). AI grand challenges for education. AI Magazine, 34(4), 66–84. doi:10.1609/aimag.v34i4.2484

    World Economic Forum. (2024). Shaping the future of learning: The role of AI in education. Geneva, Switzerland: Author.

    Veldhuis, A., Lo, P. Y., Kenny, S., & Antle, A. N. (2025). Critical Artificial Intelligence literacy: A scoping review and framework synthesis. International Journal of Child-Computer Interaction, 43, Article 100708. https://doi.org/10.1016/j.ijcci.2024.100708

    Yıldırım, A., & Şimşek, H. (2018). Sosyal bilimlerde nitel araştırma yöntemleri (11. baskı). Ankara: Seçkin Yayıncılık.

    Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). Thousand Oaks, CA: Sage.

    Zhou, X., & Schofield, L. (2024). Developing a conceptual framework for Artificial Intelligence (AI) literacy in higher education. Journal of Learning Development in Higher Education.

    Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(1), 1–27.