The rapid advancement of large language model-powered generative artificial intelligence (GenAI) in L2 communication learning presents a double-edged dynamic: while GenAI offers situated praxis for learning contexts (e.g., McCoy et al., 2024; Salloum et al., 2024), it simultaneously reproduces systemic bias (e.g., Dai et al., 2025; Zawiah et al., 2023). Framed through critical interactional competence (CritIC), this study explores the interactional dynamics in GenAI-simulated clinical communication, emphasising on the significance of developing Critical Interaction Competence.

Employing a qualitative comparative case study, we analysed the interactional trajectories of two international medical trainees (a cultural insider and outsider) engaging with a GenAI-simulated patient. Both trainees acted as clinicians interacting with a 65-year-old Nigerian woman presenting with a sore throat, generated from the same prompt. Findings reveal that GenAI’s identity construction was not a continuous embodied state, but a series of discrete, keyword-triggered profiles, with cultural stereotypes. Across cases, the study uncovered a distinct mismatch between real-time interactional conduct and post-task reflections. While the cultural outsider resisted Nigerian English markers during the consultation, she evaluated the simulation as “genuine” and “natural”. In contrast, the cultural insider ’s moment-by-moment responses exposed the epistemic risks of the simulacrum (Jones, 2025; O’Regan & Ferri, 2025) through aligning with GenAI’s cultural narratives, yet retrospectively critiqued the performance as a “Hollywood” caricature.

These findings reveal ethical concerns not as an abstract consideration, but as an interactional accomplishment shaped by participants’ orientations to algorithmic positioning. They also highlight the pedagogical risk of stereotype reproduction when learners lack CritIC (Dai et al., 2025). We therefore advocate for developing CritIC across the learning tranjectory through transpositioning (Li & Lee, 2024), where trainees release themselves from the default role of communication learners and enact multiple relevant positions to interrogate GenAI’s stances.

Dai, D. W., Hua, Z., & Chen, G. (2025). How does interaction with LLM powered chatbots shape human understanding of culture? The need for Critical Interactional Competence (CritIC). Annual Review of Applied Linguistics, 1–22. https://doi.org/10.1017/S0267190525000054
Jones, R. H. (2025). Culture machines. Applied Linguistics Review, 16(2), 753–762. https://doi.org/10.1515/applirev-2024-0188
Li, W., & Lee, T. K. (2024). Transpositioning: Translanguaging and the liquidity of identity. Applied Linguistics, 45(5), 873–888. https://doi.org/10.1093/applin/amad065
McCoy, L. G., Ci Ng, F. Y., Sauer, C. M., Yap Legaspi, K. E., Jain, B., Gallifant, J., McClurkin, M., Hammond, A., Goode, D., Gichoya, J., & Celi, L. A. (2024). Understanding and training for the impact of large language models and artificial intelligence in healthcare practice: A narrative review. BMC Medical Education, 24(1), 1096. https://doi.org/10.1186/s12909-024-06048-z
O’Regan, J. P., & Ferri, G. (2025). Artificial intelligence and depth ontology: Implications for intercultural ethics. Applied Linguistics Review, 16(2), 797–807. https://doi.org/10.1515/applirev-2024-0189
Salloum, A., Alfaisal, R., & Salloum, S. A. (2024). Revolutionizing medical education: Empowering learning with ChatGPT. In A. Al-Marzouqi, S. A. Sa