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GENERATIVE AI DRIVEN SOCIO PRAGMATIC SCAFFOLDING FOR DEVELOPING CROSS CULTURAL COMMUNICATIVE COMPETENCE IN ADVANCED LANGUAGE LEARNERS

Choi So YoungSenior Lecturer, Tashkent Institute of Finance and Technology,Tashkent,UzbekistanMoximir MamadjanovaSenior Lecturer, Teacher of Russian Language and Literature, Andijan State Medical Institute,Andijan,UzbekistanFatima VakhidovaAssociate Professor, English Linguistics Department, Bukhara State University,Bukhara,UzbekistanGavkhar TursunovaSamarkand State Medical University,Samarkand,UzbekistanBakhtijon Isroilova5Head, Department of English, Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, National Research University,Tashkent,UzbekistanОзода АчиловаAssociate Professor, Faculty of Oriental Languages, Samarkand State Institute of Foreign Languages,Samarkand,UzbekistanKomila ShoyimovaDepartment of Foreign Language and Literature, Termez University of Economics and Service,Termez,Uzbekistan
2026
ABI

Annotatsiya

The cultivation of cross-cultural communicative competence (CCCC) among advanced language learners remains ongoing, particularly regarding exposure to authentic socio-pragmatic contexts and the lack of adaptive feedback in traditional learning. The research paper suggests a socio-pragmatic scaffolding system that uses large language models to generate socio-pragmatic dialogues and provide context-sensitive remediation, based on generative artificial intelligence Architectures like GPT-4 from OpenAI. A quasi-experimental design was used with 120 advanced learners, who were separated into an experimental (n=60) and a control group (n=60), with the intervention lasting 12 weeks. In both the experimental and control groups, AI-mediated role-play, discourse adaptation tasks, and pragmatic feedback cycles were undertaken, while traditional task-based instruction was delivered. Pragmatic appropriateness, discourse flexibility, and intercultural sensitivity were measured by pre- and post-tests based on validated rubrics and a standardized communicative competence scale. Findings show that there is a statistically significant benefit in the experimental group in terms of an overall increase of CCCC scores by 32.8 % (p < 0.01), as compared to 12.3 % in the control group. The accuracy of pragmatic appropriateness increased by 29.4%, the score of intercultural sensitivity indices increased by 88.2%. The impact of AI-driven scaffolding was strong, as evidenced by the effect size analysis (Cohen's d = 0.86). Moreover, the learner engagement metrics showed a 41% voluntary task completion and a 41.6% drop in pragmatic transfer errors. The result proves that socio-pragmatic scaffolding supported by generative AI can substantially enhance cross-cultural communicative competence by providing adaptive, context-rich, and iterative feedback. The research finds that an AI-based discourse simulation, when incorporated into a high-level language course, can help fill pragmatic gaps and promote the development of culturally responsive communication skills in a globalised learning setting.

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