AI-POWERED MUSIC THERAPY FOR EDUCATION
DOI:
https://doi.org/10.29121/shodhkosh.v7.i1s.2026.7089Keywords:
Artificial Intelligence, Music Therapy, Affective Computing, Adaptive Learning, Educational Technology, Emotional Regulation, Reinforcement LearningAbstract [English]
The artificial intelligence (AI) has offered new possibilities to improve the sphere of music therapy as the means of adaptive support within the educational settings. The traditional music therapy methods are useful but very stagnant and cannot be scaled and personalized. This paper suggests an adaptive music therapy program that will be driven by AI and will help individuals regulate their emotions, maintain engagement, and be ready to learn in classroom, inclusive education, and online classes. It is a closed-loop architecture based on the uniting framework of multimodal learner sensing, affective computing, cognitive state modeling, music information retrieval, and reinforcement learning. Experimental assessment shows that adaptive music interventions based on AI are more effective than no-music and stable music interventions in reducing the stress proxy, enhancing long-term engagement, and increasing the ability to re-engage with learning activities in learners. The results indicate the relevance of individualized and context-based therapeutic intervention based on the learning and psychological theory. The research paper confirms that AI-driven music therapy is a scalable and emotionally intelligent tool of educational support, and it provides both practical and theoretical contributions to the affective learning systems.
References
Bhandarkar, S., Salvi, B. V., and Shende, P. (2024). Current Scenario and Potential of Music Therapy in the Management of Diseases. Behavioural Brain Research, 458, Article 114750. https://doi.org/10.1016/j.bbr.2023.114750 DOI: https://doi.org/10.1016/j.bbr.2023.114750
Chen, B., and Huang, L. (2022). A Systematic Review of Multimodal Interaction in Artificial Intelligent Systems Supporting Children to Learn Music. In Human-Computer Interaction: Theoretical Approaches and Design Methods (pp. 125–140). Springer. https://doi.org/10.1007/978-3-031-05311-5_38 DOI: https://doi.org/10.1007/978-3-031-05311-5_38
Dai, D., and Ding, B. (2021). Artificial Intelligence Technology Assisted Music Teaching Design. Scientific Programming, 2021, Article 9141339. https://doi.org/10.1155/2021/9141339 DOI: https://doi.org/10.1155/2021/9141339
Gandolfo, G., and Hugues, M. (2021). The Use of Music in Therapy Through the Ages. Journal of Interdisciplinary Methodological Issues in Science, 10, Article 9324. https://doi.org/10.18713/JIMIS-210121-10-01
Jiang, Q. (2022). Application of Artificial Intelligence Technology in Music Education Supported by Wireless Network. Mathematical Problems in Engineering, 2022, Article 2138059. https://doi.org/10.1155/2022/2138059 DOI: https://doi.org/10.1155/2022/2138059
Johnels, L., Vehmas, S., and Wilder, J. (2023). Musical Interaction with Children and Young People with Severe or Profound Intellectual and Multiple Disabilities: A Scoping Review. International Journal of Developmental Disabilities, 69(4), 487–504. https://doi.org/10.1080/20473869.2021.1940628 DOI: https://doi.org/10.1080/20473869.2021.1959875
Kobus, S., et al. (2022). Music Therapy Supports Children with Neurological Diseases During Physical Therapy Interventions. International Journal of Environmental Research and Public Health, 19(3), Article 1492. https://doi.org/10.3390/ijerph19031492 DOI: https://doi.org/10.3390/ijerph19031492
Lee, L., and Liu, Y. S. (2021). Training Effects and Intelligent Evaluated Pattern of the Holistic Music Educational Approach for Children with Developmental Delay. International Journal of Environmental Research and Public Health, 18(19), Article 10064. https://doi.org/10.3390/ijerph181910064 DOI: https://doi.org/10.3390/ijerph181910064
Li, K., Weng, L., and Wang, X. (2021). The State of Music Therapy Studies in the Past 20 Years: A Bibliometric Analysis. Frontiers in Psychology, 12, Article 697726. https://doi.org/10.3389/fpsyg.2021.697726 DOI: https://doi.org/10.3389/fpsyg.2021.697726
Sampaio, A.-D. S. C. (2023). Music-Based Interventions in Rehabilitation of Children and Adolescents with Chronic Diseases: Sharing an Experience from a Brazilian Public Hospital. Frontiers in Rehabilitation Sciences, 4, Article 1116914. Https://Doi.Org/10.3389/Fresc.2023.1116914 DOI: https://doi.org/10.3389/fresc.2023.1116914
Sikri, A., Sikri, J., and Gupta, R. (2024). AI-Powered Dentistry: Revolutionizing Oral Care. ShodhAI: Journal of Artificial Intelligence, 1(1), 1–8. https://doi.org/10.29121/shodhai.v1.i1.2024.2 DOI: https://doi.org/10.29121/shodhai.v1.i1.2024.2
Sciancalepore, F., et al. (2022). Computer-Based Cognitive Training in Children with Primary Brain Tumours: A Systematic Review. Cancers, 14(15), Article 3879. https://doi.org/10.3390/cancers14153879 DOI: https://doi.org/10.3390/cancers14163879
Tramontano, M., et al. (2021). Music-Based Techniques and Related Devices in Neurorehabilitation: A Scoping Review. Expert Review of Medical Devices, 18(8), 733–749. https://doi.org/10.1080/17434440.2021.1954050 DOI: https://doi.org/10.1080/17434440.2021.1947793
Venugopal, K., and Madhusudan, P. (2017, July). Feasibility of Music Composition Using Artificial Neural Networks. In Proceedings of the International Conference on Computing Methodologies and Communication (ICCMC) (pp. 524–525). IEEE. https://doi.org/10.1109/ICCMC.2017.8282520 DOI: https://doi.org/10.1109/ICCMC.2017.8282520
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
Xu, Z. (2022). Construction of Intelligent Recognition and Learning Education Platform of National Music Genre Under Deep Learning. Frontiers in Psychology, 13, Article 843427. https://doi.org/10.3389/fpsyg.2022.843427 DOI: https://doi.org/10.3389/fpsyg.2022.843427
Yang, J., Min, C., Mathur, A., and Kawsar, F. (2022, May). SleepGAN: Towards Personalized Sleep Therapy Music. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 966–970). IEEE. https://doi.org/10.1109/ICASSP43922.2022.9747033 DOI: https://doi.org/10.1109/ICASSP43922.2022.9747033
Zheng, H., and Dai, D. (2022). Construction and Optimization of Artificial Intelligence-Assisted Interactive College Music Performance Teaching System. Scientific Programming, 2022, Article 3199860. https://doi.org/10.1155/2022/3199860 DOI: https://doi.org/10.1155/2022/3199860
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Dr. Vikrant Nangare, Tanveer Ahmad Wani, Ashutosh Kulkarni, Dr. Neeta Karhadka, Vinit Khetani, Pushpa Nagini Sripada
This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
