ALGORITHMIC LANGUAGE EXPOSURE AND LINGUISTIC IDENTITY FORMATION: A MULTILEVEL ANALYSIS OF AI-DRIVEN EDUCATIONAL PLATFORMS AMONG MULTILINGUAL UNIVERSITY STUDENTS IN PAKISTAN

Abstract

The rapid integration of Artificial Intelligence (AI)-driven educational platforms in higher education has significantly transformed multilingual learning environments and reshaped linguistic practices among university students. This study examined the influence of algorithmic language exposure on linguistic identity formation among multilingual university students in Pakistan. Grounded in Sociocultural Theory, the study employed a quantitative cross-sectional survey design using a sample of 450 multilingual students selected through multistage sampling. Data were collected through a structured Likert-scale questionnaire and analyzed using descriptive statistics, correlation, regression analysis, and Structural Equation Modeling. The findings revealed that algorithmic language exposure significantly influenced linguistic identity formation, emerging as the strongest predictor among all study variables. Results indicated that frequent interaction with AI-driven educational platforms increased students’ exposure to English-dominant linguistic structures, which in turn shaped their perceptions of linguistic prestige, communicative behavior, and identity negotiation. Sociocultural factors and perceived linguistic prestige were also found to significantly contribute to identity formation processes. The study further highlighted that AI-mediated educational systems function not only as learning tools but also as sociotechnical agents that shape multilingual learners’ linguistic self-concept and cultural affiliation. The study concludes that AI-driven educational platforms play a critical role in shaping linguistic identity among multilingual learners in Pakistan, while also reinforcing existing linguistic hierarchies. The research underscores the need for culturally responsive and linguistically inclusive AI integration in higher education to preserve multilingual diversity and reduce digital linguistic inequality.