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@article{191210,
        author = {Smruti Vadher and Sejad Chokiya},
        title = {Artificial Intelligence and Multilingualism in NEP-2020: A Comparative Linguistic Analysis of Indo-Aryan, Dravidian, and Global Languages},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {12},
        number = {no},
        pages = {1109-1116},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191210},
        abstract = {The National Education Policy (NEP-2020) places unprecedented emphasis on multilingualism, linguistic diversity, and the integration of modern technologies in higher education. In this context, Artificial Intelligence (AI) emerges as a transformative force in comparative linguistics, translation studies, and digital language learning. This study examines how AI-driven tools such as machine translation systems, natural language processing (NLP) models, and corpus-based linguistic analysis can support the comparative study of Indo-Aryan, Dravidian, and selected global languages. The paper explores structural, semantic, and phonological differences between these language families and evaluates the performance of AI tools in managing linguistic complexity, especially in low-resource Indian languages like Gujarati, Kannada, Tamil, and Odia. Using a mixed-method approach, the research integrates theoretical linguistic analysis with practical evaluation, including AI translation testing, corpus comparisons, and perception surveys of students and educators regarding AI-assisted multilingual learning. Findings reveal that while advanced AI models show significant accuracy with global languages, challenges persist in handling morphological richness, syntactic variation, and cultural nuance in Indian languages. The study highlights opportunities for AI-enhanced multilingual pedagogy aligned with NEP-2020, while emphasizing ethical, cultural, and pedagogical considerations. Ultimately, the research argues that AI, when thoughtfully integrated, can strengthen India’s multilingual vision by enhancing translation quality, language accessibility, and cross-cultural communication.},
        keywords = {NEP-2020; Artificial Intelligence; Multilingualism; Comparative Linguistics; Indian Languages},
        month = {},
        }