Human–AI Co-Creation for Accessible Music Education and Inclusive Creative Experiences
Keywords:
human-AI co-creation; accessible music education; inclusive creative experiences; sociotechnical systems; algorithmic fairness; educational infrastructureAbstract
Advances in generative and interactive artificial intelligence have opened new possibilities for music education, yet translating these technical capabilities into accessible and inclusive learning remains a systems-level challenge. This article examines human-AI co-creation as a sociotechnical infrastructure for music education, focusing on structural trade-offs among pedagogical agency, algorithmic mediation, data governance, institutional readiness, and long-term equity. Drawing on scholarship in human-computer interaction, AI in education, music information retrieval, fairness, and platform governance, the article argues that accessible co-creation systems cannot be reduced to improved model performance or novel interface design. They require modular architectures that separate sensing, musical interpretation, and feedback while preserving low-latency interaction for learners with diverse sensory, cognitive, and motor profiles. They also require governance arrangements that address representational bias in training corpora, sustainable deployment pathways for under-resourced institutions, and transparent evaluation practices aligned with musical learning rather than narrow task accuracy. The discussion analyzes conceptual frameworks and system-level cases to show how co-creative platforms can shift power relations among learners, educators, institutions, and technology providers. The article concludes with policy implications for procurement, interoperability, privacy, and professional development, and outlines a research agenda centered on fairness, robustness, and inclusive musical participation. The intended audience includes system designers, educators, and policymakers concerned with building accessible creative infrastructures that remain responsive to human values.
References
1. Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Iqbal, S., Bennett, P. N., Inkpen, K., Teevan, J., Kikin-Gil, R., & Horvitz, E. (2019). Guidelines for human-AI interaction. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1-13). Association for Computing Machinery.
2. 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). Association for Computing Machinery.
3. Miranda, E. R., & Wanderley, M. M. (2006). New digital musical instruments: Control and interaction beyond the keyboard. A-R Editions.
4. Fiebrink, R., & Cook, P. R. (2010). The Wekinator: A system for real-time, interactive machine learning in music. In Proceedings of the 2010 International Computer Music Conference (pp. 366-369). International Computer Music Association.
5. Holland, S. (2000). Artificial intelligence in music education: A critical review. In E. R. Miranda (Ed.), Readings in music and artificial intelligence (pp. 239-274). Harwood Academic.
6. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education.
7. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
8. Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689-707.
9. Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (Proceedings of Machine Learning Research, Vol. 81, pp. 77-91). PMLR.
10. Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 59-68). Association for Computing Machinery.
11. Crawford, K. (2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press.
12. Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin's Press.
13. Williamson, B. (2017). Big data in education: The digital future of learning, policy and practice. SAGE Publications.
14. Wang, Z., Ma, L., Jin, Y., Feng, Y., Pan, X., Ji, S., & Zhang, K. (2025, August). AI-assisted human-pet artistic musical co-creation for wellness therapy. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (pp. 10216-10224).
15. Benetos, E., Dixon, S., Duan, Z., & Ewert, S. (2019). Automatic music transcription: An overview. IEEE Signal Processing Magazine, 36(1), 20-30.
16. Sturm, B. L., Ben-Tal, O., Monaghan, Ú., Collins, N., Herremans, D., Chew, E., Hadjeres, G., Déguernel, K., & Pachet, F. (2019). Machine learning research that matters for music creation: A case study. Journal of New Music Research, 48(1), 36-55.
17. Keller, D., & Lazzarini, V. (2017). Ecologically grounded creative practice in ubiquitous music. Organised Sound, 22(1), 61-73.
18. Susskind, R., & Susskind, D. (2015). The future of the professions: How technology will transform the work of human experts. Oxford University Press.
19. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610-623). Association for Computing Machinery.
20. Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 International Journal of Artificial Intelligence Engineering and Systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
