Reframing L2 Pragmatic Competence in the Generative AI Era: A Co-Design-Oriented Critical Curriculum
DOI:
https://doi.org/10.65021/mwsj.v2.i3.61Keywords:
Critical Algorithmic Literacy, English as a Lingua Franca, Generative Artificial Intelligence, L2 Pragmatics, TranslanguagingAbstract
The rapid incorporation of generative artificial intelligence (GAI) into second-language (L2) education has reopened a foundational question in language pedagogy: what should count as successful communication when English is routinely used among speakers who do not share a native-speaker norm? Much L2 pragmatic instruction continues to treat pragmatics as the acquisition of socially appropriate forms modelled on idealised native-speaker practices. This conceptual review article proposes a co-design-oriented critical pragmatic curriculum that positions GAI (implemented primarily through large language models, or LLMs) not as an infallible tutor but as a contestable intercultural interlocutor. Drawing on English as a Lingua Franca (ELF), World Englishes, translanguaging, critical pragmatics, critical digital literacy, and design-based research (DBR), the paper advances a unified pedagogical architecture comprising three modules: Persona Engineering and Dialectic Diversity, Real-Time Meaning Negotiation, and Deconstructing Algorithmic Bias, each enacted through a four-stage learning episode (persona engineering → GAI interaction → critical negotiation → reflection and transfer) and organised within an iterative co-design cycle. The paper contributes a theoretically grounded model for integrating GAI with intercultural and critical language pedagogy while retaining teacher mediation, learner agency, multilingual resources, and ethical safeguards. It argues that the educational value of GAI lies less in making learners sound more native-like than in helping them become more observant, adaptive, critical, and strategically competent participants in multilingual communication.
Downloads
References
1. Albeihi, H. H. M., & Rice, M. F. (2025). Generative AI and language diversity: Implications for teachers and learners. Arab World English Journal, 16(1), 43–54. https://doi.org/10.24093/awej/vol16no1.3
2. Ampadu, E., Narh-Kert, M., & Yeboah, R. (2024). Teachers’, researchers’, and educators’ partnerships: The effect of co-creation on pupils’ problem-solving performance in mathematics. Education Sciences, 14(12), 1328. https://doi.org/10.3390/educsci14121328
3. Bayaro, M., Olorunfemi, P. A., Michael, C. I., & Adeleke, H. A. (2026). Beyond native norms: Decolonizing AI speech recognition for accent equity in Nigerian ESL classrooms. Center of Artificial Intelligence, 1(2), 111–121. https://doi.org/10.65591/CAI-206-2026
4. Bonacina-Pugh, F., da Costa Cabral, I., & Huang, J. (2021). Translanguaging in education. Language Teaching, 54(4), 439–471. https://doi.org/10.1017/S0261444821000173
5. Cenoz, J., & Gorter, D. (2022). Pedagogical translanguaging. Cambridge University Press. https://doi.org/10.1017/9781009029384
6. Deng, R., Jiang, M., Yu, X., Lu, Y., & Liu, S. (2024). Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies. Computers & Education, 215, 105224. https://doi.org/10.1016/j.compedu.2024.105224
7. Design-Based Research Collective. (2003). Design-based research: An emerging paradigm for educational inquiry. Educational Researcher, 32(1), 5–8. https://doi.org/10.3102/0013189X032001005
8. Gallegos, I. O., Rossi, R. A., Barbosa, D. R., Barrow, J., Tanjim, M. M., Kim, S., Dernoncourt, F., Yu, T., Zhang, R., & Ahmed, N. K. (2024). Bias and fairness in large language models: A survey. Computational Linguistics, 50(3), 1097–1179. https://doi.org/10.1162/coli_a_00524
9. García, O., & Wei, L. (2014). Translanguaging: Language, bilingualism and education. Palgrave Macmillan. https://doi.org/10.1057/9781137385765
10. Ishihara, N., & Cohen, A. D. (2010). Teaching and learning pragmatics: Where language and culture meet. Pearson Longman.
11. Jenkins, J. (2015). Global Englishes: A resource book for students (3rd ed.). Routledge. https://doi.org/10.4324/9781315761596
12. Jeon, J., Wei, L., Tai, K. W. H., & Lee, S. (2025). Generative AI and its dilemmas: Exploring AI from a translanguaging perspective. Applied Linguistics, 46(4), 709–717. https://doi.org/10.1093/applin/amaf049
13. Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). ChatGPT for language teaching and learning. RELC Journal, 54(2), 537–550. https://doi.org/10.1177/00336882231162868
14. Kwak, Y., & Pardos, Z. A. (2024). Bridging large language model disparities: Skill tagging of multilingual educational content. British Journal of Educational Technology, 55(5), 2039–2057. https://doi.org/10.1111/bjet.13465
15. Lee, J., Brooks, C., & Kizilcec, R. F. (2024). The life cycle of large language models in education: A framework for understanding sources of bias. British Journal of Educational Technology. Advance online publication. https://doi.org/10.1111/bjet.13505
16. Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), Article 410. https://doi.org/10.3390/educsci13040410
17. McKenney, S., & Reeves, T. C. (2018). Conducting educational design research (2nd ed.). Routledge. https://doi.org/10.4324/9781315105642
18. Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO. https://www.unesco.org/en/articles/guidance-generative-ai-education-and-research
19. Nie, S., Fromm, M., Welch, C., Görge, R., Karimi, A., Plepi, J., Mowmita, N., Flores-Herr, N., Ali, M., & Flek, L. (2024). Do multilingual large language models mitigate stereotype bias? In Proceedings of the 2nd Workshop on Cross-Cultural Considerations in NLP (pp. 65–83). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.c3nlp-1.6
20. Olorunfemi, P. A., Nnaji, C. M., & Michael, C. I. (2026b). An investigation into AI-assisted aspect-based and integrated English teaching approaches using ADLL and CPCT theories. Center of Artificial Intelligence, 1(2), 84–95. https://doi.org/10.65591/CAI-147-2026
21. Olorunfemi, P. A., & Mohammed, S. (2026). Evaluating the Syntax-Somatic Model’s impact on grammatical accuracy among learners of English as a second language (ESL). Journal of Arts, Meaning and Public Life, 1(1), 380. https://doi.org/10.61298/jampl.2026.1.1.380
22. Olorunfemi, P. A., Mohammed, S., Michael, C. I., & Nnaji, C. M. (2026a). Trans-lingual scaffolding and creative writing among slow learners in multilingual Nigerian classrooms. Journal of Arts, Meaning and Public Life, 1(1), 388. https://doi.org/10.61298/jampl.2026.1.1.388
23. Rajendram, S. (2022). “Our country has gained independence, but we haven’t”: Collaborative translanguaging to decolonize English language teaching. Annual Review of Applied Linguistics, 42, 78–86. https://doi.org/10.1017/S0267190521000155
24. Seidlhofer, B. (2011). Understanding English as a lingua franca. Oxford University Press.
25. Sim, S., Kim, Y., & Ku, K. (2025). Exploring generative AI as a roleplay interlocutor in L2 task-based pragmatics learning: Comparing ChatGPT-learner and technology-mediated peer interactions. TESOL Quarterly, 59(S1), S86–S116. https://doi.org/10.1002/tesq.70010
26. Taguchi, N. (Ed.). (2019). The Routledge handbook of second language acquisition and pragmatics. Routledge. https://doi.org/10.4324/9781351164085
27. Tao, Y., Viberg, O., Baker, R. S., & Kizilcec, R. F. (2024). Cultural bias and cultural alignment of large language models. PNAS Nexus, 3(9), pgae346. https://doi.org/10.1093/pnasnexus/pgae346
28. Zou, D. (2025). Generative AI (GenAI) in the language classroom: A systematic review. Interactive Learning Environments. Advance online publication. https://doi.org/10.1080/10494820.2025.2498537
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Philip Abayomi Olorunfemi (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright of their work and grant the Milky Way Scientific Journal (MWSJ) the right of first publication. All published articles are licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly cited.
