This position paper argues that the deliberate, pedagogically grounded integration of ChatGPT into English as a Foreign Language (EFL) instruction represents a meaningful advance toward more communicative, learner-centered language education. Drawing on established frameworks in communicative language teaching (CLT), Computer-Assisted Language Learning (CALL), and recent scholarship on generative artificial intelligence in education, the paper advances three core claims: that ChatGPT's conversational architecture aligns structurally with the demands of communicative competence development; that an integrative, multi-skill approach to AI-assisted instruction is pedagogically superior to isolated skill-based applications; and that effective integration requires explicit teacher scaffolding, ethical guidance, and institutional support. The paper proposes a concrete framework for ChatGPT integration across the four language skills and offers actionable recommendations for EFL practitioners, curriculum designers, and institutional leaders, with particular reference to the Uzbekistan EFL context.
Integrating CHATGPT Into EFL Instruction: A Position Paper on Communicative Competence Development
DOI:
Keywords:
Abstract
References
Ur-Rehman, S., Ali, Z., & Hussain, I. (2023). Task-based language teaching and oral communicative competence: A systematic review. TESOL Journal, 14(2), e00698.
Godwin-Jones, R. (2022). Scaling the tower of Babel: Language learning and AI in the age of generative models. Language Learning & Technology, 26(2), 1–28.
Baskara, R., & Mukarto, F. X. (2023). Exploring the implications of ChatGPT for language learning in higher education. Indonesian Journal of English Language Teaching and Applied Linguistics, 7(2), 343–358.
Rahimi, M., & Zhang, L. J. (2015). Exploring non-native English-speaking teachers’ cognitions about corrective feedback in teaching English for academic purposes. System, 55, 111–122.
Canale, M., & Swain, M. (1980). Theoretical bases of communicative approaches to second language teaching and testing. Applied Linguistics, 1(1), 1–47.
Peng, H. (2022). The impact of artificial intelligence on EFL learners’ motivation and self-regulated learning: A mixed-methods study. Computers & Education, 191, 104628.
Horwitz, E. K., Horwitz, M. B., & Cope, J. (1986). Foreign language classroom anxiety. The Modern Language Journal, 70(2), 125–132.
Hymes, D. H. (1972). On communicative competence. In J. B. Pride & J. Holmes (Eds.), Sociolinguistics: Selected Readings (pp. 269–293). Penguin.
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274.
Krashen, S. D. (1985). The Input Hypothesis: Issues and Implications. Longman.
Richards, J. C., & Rodgers, T. S. (2014). Approaches and Methods in Language Teaching (3rd ed.). Cambridge University Press.
Sadullayeva, Sh. (2023). Communicative competence as the foundation of foreign language teaching. Uzbek Journal of Education, 4(2), 88–95.
Warschauer, M., & Healey, D. (1998). Computers and language learning: An overview. Language Teaching, 31(2), 57–71.
Zhao, Y. (2003). Recent developments in technology and language learning: A literature review and meta-analysis. CALICO Journal, 21(1), 7–27.