ALGORITHMIC LANGUAGE EXPOSURE AND LINGUISTIC IDENTITY FORMATION: EXAMINING THE IMPACT OF AI-DRIVEN EDUCATIONAL PLATFORMS ON MULTILINGUAL LEARNERS IN PAKISTAN

Abstract

The increasing integration of artificial intelligence (AI)-driven educational platforms in language learning has introduced new dynamics of algorithmic language exposure and its influence on linguistic identity formation. This study examined the impact of AI-mediated language learning environments on multilingual learners in Pakistan, with a particular focus on how continuous algorithmic feedback and standardized linguistic input shape language preferences and identity construction. Grounded in Sociocultural Theory, the study employed a mixed-methods research design involving 320 multilingual learners selected through stratified random sampling, along with in-depth interviews from 20 participants. Quantitative findings revealed a significant relationship between algorithmic language exposure and linguistic identity transformation, with AI-driven platforms strongly influencing English language preference and contributing to a gradual decline in local language usage. Regression analysis indicated that algorithmic exposure, linguistic standardization pressure, and English preference shift collectively explained 62% of the variance in linguistic identity transformation. Qualitative findings further supported these results, highlighting themes of algorithmic English dominance, reduced linguistic flexibility, and increased dependence on AI systems for linguistic validation. The study concludes that while AI-driven educational tools enhance language learning efficiency and accessibility, they also contribute to linguistic standardization and identity shifts in multilingual contexts. The findings underscore the need for culturally responsive AI design and balanced pedagogical integration to preserve linguistic diversity.