SOCIOLINGUISTIC IDENTITIES AND LANGUAGE LEARNING IN MULTILINGUAL CLASSROOMS. A CASE STUDY IN UZBEKISTAN
Keywords:
Artificial Intelligence, Multilingualism, Sociolinguistic identity, Language acquisition, Code-switching, Gender and language, Classroom discourse, Ethnicity and language use, Culturally responsive teaching, Urban education, Language assessment.Abstract
This article presents a sociolinguistic examination of multilingual students enrolled in an urban public school in Uzbekistan. The study investigates how variables such as age, gender, ethnicity, and socioeconomic background shape learners’ language behavior, code-switching tendencies, and classroom engagement. Furthermore, this paper explores the integration of Artificial Intelligence (AI) tools in analyzing sociolinguistic identities among multilingual learners. Building upon previous qualitative research, this study incorporates Natural Language Processing (NLP) and AI-based discourse analysis to examine how variables such as age, gender, ethnicity, and socioeconomic status influence students’ linguistic behavior, code-switching, and classroom interaction. The findings highlight the value of AI in enhancing data accuracy, interpreting language variation, and supporting culturally responsive teaching strategies.By analyzing subgroup profiles and individual learner characteristics, the paper highlights how linguistic identity and social positioning intersect with English language learning. Additionally, the findings emphasize the significance of culturally responsive pedagogy and differentiated assessment practices in linguistically diverse classrooms. This study contributes to the growing discourse on equity in multilingual education and offers practical insights for language instructors working with diverse student population.
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