MANAGING CROSS-CULTURAL MUSIC LEARNING WITH AI TOOLS
DOI:
https://doi.org/10.29121/shodhkosh.v7.i1s.2026.7112Keywords:
Cross-Cultural Music Education, Artificial Intelligence in Music, Music Information Retrieval, Cultural Fidelity, AI-Assisted Learning Systems, Global Music PedagogyAbstract [English]
Teaching cross-cultural music is a pedagogical problem of cross-cultural differences in musical structure or notation, ornamentation, rhythmic patterns, and linguistic articulation. New opportunities to overcome these challenges are introduced by recent advances in artificial intelligence (AI) that allow making music education settings adaptive and culturally informed. This paper gives a proposal of an AI-based framework to regulate the process of learning cross-cultural music by introducing music information retrieval (MIR) models, Transformer-based architecture, generative models, and intelligent feedback mechanisms. The framework is aimed at the learners and educators who work with different musical traditions like the Western, non-Western, indigenous and folk music systems. The suggested system includes the recognition of style and ornamentation to locate the culturally specific melodic and rhythmic patterns, cross-cultural notation and rhythm transformation to overcome the differences in the representational systems, and AI-provided feedback on the pronunciation, phrasing and expressiveness nuances. To make sure that it is pedagogically valid and culturally sensitive, several groups of users are involved including students, teachers and experts in cross-cultural music. Measurements against technical accuracy are not limited to evaluate the achievement, but cultural fidelity, interpretive correctness, and quantifiable learning outcomes are considered the evaluation metrics. The system has proven to be relevant in East-West classical fusion classes, AI-driven orchestration of indigenous and folk repertoires and cross-language syllabus voice training and lyric-alignment.
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Copyright (c) 2026 Dr. Shweta Bajaj, Om Prakash, Amol Barde, Damodaran B, Chaitali, Monali Gulhane
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