21, February 2026

Artificial Intelligence and the Reshaping of Multilingual Language Acquisition Paradigms

Author(s): Dr. Vandana Singh

Authors Affiliations:

Senior Assistant Professor, P.G Department of English, B. R. R.V Pd. Singh College, Ara (erstwhile Maharaja College), Bihar, India

DOIs:10.2017/IJRCS/202602010     |     Paper ID: IJRCS202602010


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Abstract: Language learning has changed along with many other domains due to the emergence of artificial intelligence (AI). As globalization increases, the need for effective language acquisition in multilingual contexts has become paramount. AI technologies are now being integrated into language learning platforms, dramatically altering how learners acquire new languages and interact with multilingual environments. Multilingual contexts refer to environments where multiple languages coexist, either within a community, a classroom, or an individual learner's experience. These contexts are increasingly common due to globalization, migration, and technological advancements, leading to a rich tapestry of linguistic diversity. Multilingual learners often navigate multiple languages simultaneously, which can enhance cognitive flexibility but also present unique challenges in language acquisition. Artificial intelligence's (AI) incorporation into language instruction has transformed how learners acquire new languages, particularly in culturally diverse environments. As globalization intensifies, the need for effective language acquisition that accounts for cultural nuances has become increasingly important. AI technologies are uniquely positioned to facilitate this process by offering personalized, contextually relevant learning experiences. This paper explores the advent of AI in language learning acquisition, focusing on its role in cultural contextualization and the broader implications for learners and educators.

Artificial Intelligence, Multilingual Language Acquisition, Blended Learning, AI-Enhanced Learning, Adaptive Learning system, Immersive Education, Natural Learning Process.

Dr. Vandana Singh  (2026); Artificial Intelligence and the Reshaping of Multilingual Language Acquisition Paradigms, International Journal of Research Culture Society,    ISSN(O): 2456-6683,  Volume – 10,   Issue –  2,  Available on – https://ijrcs.org/

  1. Bond, M., Zawacki-Richter, O., & Nichols, M. (2020). Revisiting five decades of educational technology research: A content and authorship analysis of the British Journal of Educational Technology. British Journal of Educational Technology, 51(1), 12–63. https://doi.org/10.1111/bjet.12816
  2. Çayak, S. (2024). Investigating the relationship between teachers’ attitudes toward artificial intelligence and their AI literacy. Journal of Educational Technology & Online Learning, 7(4), 367–383.
  3. Central Board of Secondary Education. (2020). Artificial intelligence curriculum for classes IX-XII. https://cbseacademic.nic.in/skill-education.html
  4. Central Board of Secondary Education. (2021). Capacity building programmes for classes teachers on artificial intelligence. https://cbseacademic.nic.in
  5. Government of India. (2020). New Education Policy 2020. New Delhi: Ministry of Education. https://www.education.gov.in/sites/upload_files/mhrd/files/NEP_Final_English_0.pdf
  6. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
  7. Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). AI literacy: Definition, teaching, evaluation, and ethical issues. Proceedings of the Association for Information Science and Technology, 58(1), 504–509. https://doi.org/10.1002/pra2.487
  8. Sanusi, I. T., Oyelere, S. S., Vartiainen, H., & Suhonen, J. (2022). Teachers’ perceptions of the use of artificial intelligence in education. Education and Information Technologies, 27, 123–145. https://doi.org/10.1007/s10639-021-10697-9
  9. (2021). Artificial intelligence and education: Guidance for policy-makers. UNESCO. https://unesdoc.unesco.org/
  10. (2024). AI competency framework for teachers. UNESCO. https://unesdoc.unesco.org/
  11. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
  12. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
  13. Wang, S., Wang, H., Li, J., & Chen, Y. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 234, 121098. https://doi.org/10.1016/j.eswa.2023.121098
  14. Yim, I. H. Y., Park, S., & Kim, J. (2024). Teachers’ perceptions, attitudes, and acceptance of artificial intelligence learning tools: A systematic review. Frontiers in Education, 9, 1298456. https://doi.org/10.3389/feduc.2024.1298456
  15. Zawacki-Richter, O., Marin, V. I., Bond, M., & Gouverneur, F. (2019). Artificial intelligence in education: An overview of the state of the art. International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0

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