Impact of AI-Based Personalized English Learning on Cognitive Offloading and Breadth of Formal Curriculum

Abstract

Drawing on a comprehensive review of empirical studies through pedagogical and SLA theoretical lenses, the study looked into the pedagogical implications of the complex relationship between AI-driven personalization and curricular narrowing in language education. The findings are synthesized into a theoretically grounded framework that explains AI’s impact on the breadth and depth of language education, particularly in contexts where English is not used as a native language. While AI promises individualized learning experiences, the study revealed a paradox in which algorithmic standardization and market-driven priorities risk homogenizing language curricula and constraining pedagogical diversity. To address these challenges, the study situated its analysis within an interdisciplinary AI framework for education that emphasizes collaboration among educators, linguists, technologists, and designers. The framework promotes transparency, accountability, cultural and linguistic inclusion, and ethical digital literacy. It is a contribution to developing balanced curricular designs that ensure AI-driven personalized learning platforms support comprehensive, equitable, and culturally responsive educational experiences, rather than narrowing learners’ linguistic exposure or limiting critical engagement, while highlighting areas where pedagogical innovation can counterbalance emerging risks.

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