GENERATIVE AI FOR MULTICULTURAL MUSIC EDUCATION
DOI:
https://doi.org/10.29121/shodhkosh.v6.i5s.2025.6910Keywords:
Generative AI, Multicultural Music Education, Ethnomusicology, Cultural Conditioning, Creative LearningAbstract [English]
Generative Artificial Intelligence is quickly transforming music education with the ability to create new creative experiences, opportunities of cultural interaction, and tailored learning. In this paper, the authors explain how to design and implement a Generative AI framework on multicultural music education to assist in preserving, exploring, and pedagogically integrating various musical traditions. The proposed approach is based on ethnomusicology and cross-cultural learning theory, and it presents the models of symbolic, audio, and multimodal musical presentation to reflect the rhythm, melody, timbre, structure, and culturally specific motifs. A cultural conditioning layer is added to make the generative models move towards stylistic authenticity to avoid the homogenization process, instead promoting creative diversity. The model combines architectures created with transformers with carefully curated and ethically sourced datasets inferred with cultural context, performance practice and expressive intent. Methodologically, the paper describes the data preprocessing pipelines and style adaptation mechanisms as well as evaluation protocols which are an extension of the quantitative measures of motif similarity and tonal coherence measures with qualitative measures of cultural fidelity, creativity and perception by learners. Findings suggest that AI-generated compositions, when transparently created and pedagogically mediated can help increase the engagement of learners, their intercultural knowledge, and their confidence in their creative abilities without jeopardizing the conventional teaching.
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Copyright (c) 2025 Vaibhav Kaushik, Ananta Narayana, Dr. Dayal Singh Satha, Dr. Joshila Grace, R. Elankavi, Gajanan Chavan
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