Artificial Intelligence in Language Education: Applications, Prospects, Challenges, and Risks: The Case of Georgia
Abstract
The rapid expansion of digital technologies in the 21st century has transformed domains traditionally controlled by human activity, including education. In developing countries such as Georgia, this transformation is particularly visible in higher education, where artificial intelligence (AI) is increasingly integrated into language teaching and learning. Based on an extensive literature review and an exploratory survey of language students in several Georgian universities, this article examines current uses of AI in language education, their pedagogical benefits, and the ethical and institutional challenges they raise. The findings show that students largely perceive AI as a valuable complementary tool that enhances personalization, accessibility, and engagement in language learning. A majority reported that AI tools helped them better understand and retain information, supported autonomous learning, and adapted to their individual pace. At the same time, the study reveals significant concerns regarding technological dependency, the reduction of human interaction, and the protection of personal data. Many students emphasized that AI cannot replace the teacher’s pedagogical and intercultural role. The article concludes that while AI has clear potential to improve language education in Georgia, its integration must be ethical, regulated, and teacher-mediated to avoid risks related to overreliance on technology and data misuse.
Downloads
PlumX Statistics
References
2. Blin, F. (2016). CALL and the development of learner autonomy: Towards an activity-theoretical perspective. ReCALL, 28(1), 3–19. https://doi.org/10.1017/S095834401500015X
3. Chikovani, N., Gogoladze, M., & Kapanadze, L. (2022). Digital transformation in Georgian higher education: Challenges and opportunities. Journal of Education and Development, 6(2), 45–58.
4. Cui, Y., Liu, J., & Wang, S. (2019). The role of artificial intelligence in personalized language learning. International Journal of Emerging Technologies in Learning, 14(6), 4–15. https://doi.org/10.3991/ijet.v14i06.10150
5. Griffiths, C. (2015). Language learning strategies: An holistic view. Palgrave Macmillan.
6. Kukulska-Hulme, A., & Shield, L. (2008). An overview of mobile assisted language learning: From content delivery to supported collaboration and interaction. ReCALL, 20(3), 271–289. https://doi.org/10.1017/S0958344008000335
7. Kukulska-Hulme, A., & Shield, L. (2018). Language learning and technology in the twenty-first century. The Language Learning Journal, 46(3), 1–14. https://doi.org/10.1080/09571736.2018.1505150
8. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education.
9. OECD. (2021). Artificial intelligence in education: Challenges and opportunities for sustainable development. OECD Publishing. https://doi.org/10.1787/4b89a3cf-en
10. Reinhardt, J., Warner, C., & Lange, K. (2020). Digital games and AI in second language learning. Computer Assisted Language Learning, 33(3), 1–25. https://doi.org/10.1080/09588221.2020.1728201
11. Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.
12. Tharp, R. G., Estrada, P., Dalton, S. S., & Yamauchi, L. A. (2021). Teaching transformed: Achieving excellence, fairness, inclusion, and harmony. Routledge.
13. UNESCO. (2019). Artificial intelligence in education: Guidance for policy-makers. UNESCO Publishing.
14. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
Copyright (c) 2026 Tamar Gagoshidze
This work is licensed under a Creative Commons Attribution 4.0 International License.
