Co-Adaptation in Learner–ChatGPT Dyadic Interaction: A Multi-Leveled Linguistic Analysis

Abstract

Despite the rapid uptake of large language models (LLMs) like ChatGPT in second language (L2) learning environments, the interactional dynamics of LLM–learner dyads remain under-examined. Existing research has primarily focused on the products of LLM–learner interactions, while the interactional process is rarely a central concern. For example, Sok and Shin (2025) compared learners’ task performance before and after interacting with ChatGPT, emphasizing the importance of outcome gains rather than the turn-by-turn exchanges with the LLM. Kusumaningrum et al. (2024) analyzed the degree of conceptual, lexical, and structural overlap between ChatGPT-generated text and learners’ final email drafts, focusing on learners’ appropriation of AI output rather than the dynamics of learner–ChatGPT interaction.