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DOI: https://doi.org/10.63345/ijre.v12.i9.1
Ishu Anand Jaiswal
Independent Researcher
Civil Lines, , Kanpur, UP, India-208001
Abstract— Global education platforms increasingly rely on artificial intelligence to deliver personalized learning at scale; however, existing systems remain limited in their ability to serve linguistically and culturally diverse learners. Prior research demonstrates strong advances in multilingual machine translation, intelligent tutoring systems, and adaptive recommendation engines, yet these components largely operate in isolation. Multilinguality is often treated as a translation layer, while cultural adaptation is typically rule-based, static, and narrowly scoped. Moreover, large-scale models rarely incorporate dynamic learner identities, low-resource languages, community-specific pedagogical norms, or fairness considerations across linguistic and cultural groups. This research addresses these gaps by proposing a unified framework for multilingual and culturally adaptive AI models designed specifically for global education ecosystems. The study integrates multilingual representation learning, culturally aware learner modeling, and context-sensitive adaptive sequencing into an end-to-end architecture that supports diverse populations at scale. Through empirical evaluation using multilingual educational datasets and culturally heterogeneous learner profiles, the proposed model demonstrates improved accessibility, relevance, and equity in learning outcomes compared to conventional monolingual or culture-agnostic systems. The findings highlight the necessity of embedding linguistic diversity and cultural intelligence directly into core AI components rather than treating them as add-on features. This work contributes a foundational step toward globally inclusive AI-driven learning platforms capable of supporting learners across languages, regions, and cultural contexts.
Keywords— Multilingual AI Models, Cultural Adaptivity, Intelligent Learning Systems, Global Education Platforms, Inclusive Personalization
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