AI-GENERATED COMPOSITIONS IN MUSIC EDUCATION
DOI:
https://doi.org/10.29121/shodhkosh.v6.i3s.2025.6794Keywords:
Artificial Intelligence, Music Education, AI-Generated Composition, Creativity, Educational TechnologyAbstract [English]
With the introduction of the creative process into the sphere of music learning, the introduction of the Artificial Intelligence (AI) has changed the way of composing, analyzing, and learning. The compositions created by AI give educators and learners new tools that could be applied to generate creativity and composing, and their interpretation of the musical form. The paper explains how AI has been used more and more as a potent application in the field of music education because it has evolved into a neural network-based network, such as AIVA, Amper Music, and MuseNet. The research question examined in the paper with the assistance of a mixed-methods research incorporating surveys, interviews, and classroom activities is how the AI-assisted tools impact the engagement, creativity, and compositional abilities of students. The findings indicate that AI-generated music can help to enhance the appreciation of the compositional processes and experimentation and collaboration. Nevertheless, the study also finds obstacles, such as the presence of ethical issues relating to authorship, the possible excessive dependence on technology, and the necessity to establish a balance between intuition and algorithms. Case studies demonstrate effective educational applications, focusing on the use of teacher facilitation and curriculum integration to make AI pedagogical potential the most effective way to utilize it.
References
Afchar, D., Melchiorre, A., Schedl, M., Hennequin, R., Epure, E., and Moussallam, M. (2022). Explainability in Music Recommender Systems. AI Magazine, 43(2), 190–208. https://doi.org/10.1002/aaai.12056 DOI: https://doi.org/10.1002/aaai.12056
Briot, J.-P., and Pachet, F. (2020). Music Generation by Deep Learning: Challenges and Directions. Neural Computing and Applications, 32(4), 981–993. https://doi.org/10.1007/s00521-018-3813-6 DOI: https://doi.org/10.1007/s00521-018-3813-6
Briot, J.-P., Hadjeres, G., and Pachet, F.-D. (2019). Deep Learning Techniques for Music Generation: A Survey (arXiv:1709.01620). arXiv. DOI: https://doi.org/10.1007/978-3-319-70163-9
Civit, M., Civit-Masot, J., Cuadrado, F., and Escalona, M. J. (2022). A Systematic Review of Artificial Intelligence-Based Music Generation: Scope, Applications, and Future Trends. Expert Systems with Applications, 209, Article 118190. https://doi.org/10.1016/j.eswa.2022.118190 DOI: https://doi.org/10.1016/j.eswa.2022.118190
Guo, Z., Dimos, M., and Dorien, H. (2021). Hierarchical Recurrent Neural Networks for Conditional Melody Generation with Long-Term Structure (arXiv:2102.09794). arXiv.
Hernandez-Olivan, C., and Beltran, J. R. (2021). Music Composition with Deep Learning: A Review. Springer. DOI: https://doi.org/10.1007/978-3-031-18444-4_2
Herremans, D., Chuan, C.-H., and Chew, E. (2017). A Functional Taxonomy of Music Generation Systems. ACM Computing Surveys, 50(5), Article 69. https://doi.org/10.1145/3108242 DOI: https://doi.org/10.1145/3108242
Ji, S., Yang, X., and Luo, J. (2023). A Survey on Deep Learning for Symbolic Music Generation: Representations, Algorithms, Evaluations, and Challenges. ACM Computing Surveys, 56(1), Article 39. https://doi.org/10.1145/3597493 DOI: https://doi.org/10.1145/3597493
Ma, Y., Øland, A., Ragni, A., Sette, B. M. D., Saitis, C., Donahue, C., Lin, C., Plachouras, C., Benetos, E., Shatri, E., et al. (2024). Foundation Models for Music: A Survey (arXiv:2408.14340). arXiv.
Messingschlager, T. V., and Appel, M. (2023). Mind Ascribed to AI and the Appreciation of Ai-Generated Art. New Media and Society, 27(6), 1673–1692. https://doi.org/10.1177/14614448231200248 DOI: https://doi.org/10.1177/14614448231200248
Wen, Y.-W., and Ting, C.-K. (2023). Recent Advances of Computational Intelligence Techniques for Composing Music. IEEE Transactions on Emerging Topics in Computational Intelligence, 7(3), 578–597. https://doi.org/10.1109/TETCI.2022.3221126 DOI: https://doi.org/10.1109/TETCI.2022.3221126
Williams, D., Hodge, V. J., and Wu, C. Y. (2020). On the Use of AI for Generation of Functional Music to Improve Mental Health. Frontiers in Artificial Intelligence, 3, Article 497864. https://doi.org/10.3389/frai.2020.497864 DOI: https://doi.org/10.3389/frai.2020.497864
Wu, J., Hu, C., Wang, Y., Hu, X., and Zhu, J. (2020). A Hierarchical Recurrent Neural Network for Symbolic Melody Generation. IEEE Transactions on Cybernetics, 50(6), 2749–2757. https://doi.org/10.1109/TCYB.2019.2953194 DOI: https://doi.org/10.1109/TCYB.2019.2953194
Yu, Y., Zhang, Z., Duan, W., Srivastava, A., Shah, R., and Ren, Y. (2023). Conditional Hybrid GAN for Melody Generation from Lyrics. Neural Computing and Applications, 35(5), 3191–3202. https://doi.org/10.1007/s00521-022-07863-5 DOI: https://doi.org/10.1007/s00521-022-07863-5
Zhang, Z., Yu, Y., and Takasu, A. (2023). Controllable Lyrics-To-Melody Generation. Neural Computing and Applications, 35(27), 19805–19819. https://doi.org/10.1007/s00521-023-08728-1 DOI: https://doi.org/10.1007/s00521-023-08728-1
Zhu, Y., Baca, J., Rekabdar, B., and Rawassizadeh, R. (2023). A Survey of AI Music Generation Tools and Models (arXiv:2308.12982). arXiv.
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