Digital Technologies Research and Applications

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Advancing Sociocultural Competence in Linguistics Education through Global Simulation Enhanced by Natural Language Processing and Speech Recognition Technologies

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https://doi.org/10.54963/dtra.v5i1.1964
Khakimova, M., Mamarajabov, S., Fayzullaeva, N., Ochilova, G., Musakhanova, G., Akbarova, S., Kayumova, M., Khojiyeva, I., & Begmatov, D. (2026). Advancing Sociocultural Competence in Linguistics Education through Global Simulation Enhanced by Natural Language Processing and Speech Recognition Technologies. Digital Technologies Research and Applications, 5(1), 265–276. https://doi.org/10.54963/dtra.v5i1.1964
Volume 5 Issue 1: March 2026
Copyright (c) 2026 Mukhabbat Khakimova, Shavkat Mamarajabov, Nilufar Fayzullaeva, Gulnoza Ochilova, Gulnora Musakhanova, Sayyora Akbarova, Mekhribonu Kayumova, Iroda Khojiyeva, Davlat Begmatov
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Authors

  • Mukhabbat Khakimova

    Department of Economic Theory, Tashkent State University of Economics, Tashkent 100066, Uzbekistan
  • Shavkat Mamarajabov

    Department of Economic Theory, Tashkent State University of Economics, Tashkent 100066, Uzbekistan
  • Nilufar Fayzullaeva

    Department of Economic Theory, Tashkent State University of Economics, Tashkent 100066, Uzbekistan
  • Gulnoza Ochilova

    Department of Economic Theory, Tashkent State University of Economics, Tashkent 100066, Uzbekistan
  • Gulnora Musakhanova

    Department of Economic Theory, Tashkent State University of Economics, Tashkent 100066, Uzbekistan
  • Sayyora Akbarova

    Department of Economic Theory, Tashkent State University of Economics, Tashkent 100066, Uzbekistan
  • Mekhribonu Kayumova

    Department of Economic Theory, Tashkent State University of Economics, Tashkent 100066, Uzbekistan
  • Iroda Khojiyeva

    Department of Economic Theory, Tashkent State University of Economics, Tashkent 100066, Uzbekistan
  • Davlat Begmatov

    Department of Economic Theory, Tashkent State University of Economics, Tashkent 100066, Uzbekistan

Received: 30 November 2025; Revised: 21 January 2026; Accepted: 2 February 2026; Published: 3 March 2026

This study examines the development of sociocultural competence among international linguistics students learning English as a second foreign language through global simulation supported by Natural Language Processing (NLP) and speech recognition technologies. Sociocultural competence is defined as the capacity to communicate effectively and appropriately across cultural contexts and is assessed through indicators such as intercultural awareness, context-sensitive language use, communicative adaptability, and pragmatically appropriate choices. The instructional intervention integrates NLP techniques including text classification, semantic similarity analysis, and language modeling to generate real-time feedback on the cultural appropriateness of learners’ responses. Speech recognition technology is employed to analyze oral production and to support immediate corrective guidance across varying proficiency levels. Simulation tasks are structured around authentic intercultural dilemmas and role-based interactions, with methodological precautions taken to reduce cultural bias and stereotyping. A quasi-experimental design (n = 32) compares technology-enhanced simulation with conventional instruction. Data are collected through validated assessment rubrics, pre- and post-test measures, and interaction logs to quantify changes in sociocultural performance. Findings indicate measurable improvement in students’ intercultural communicative competence in the experimental group, suggesting that AI-supported simulation can strengthen both linguistic accuracy and sociocultural appropriateness beyond traditional classroom practices. The study also addresses ethical considerations related to data privacy and speech processing. Although limited by sample size and institutional scope, the research offers a replicable framework for integrating computational tools into linguistics education and highlights directions for broader, cross-contextual implementation.

Keywords:

Global Simulation Sociocultural Competence Natural Language Processing Speech Recognition Technologies Linguistics Education Technology-Enhanced Language Learning

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