Studies in Applied Linguistics and TESOL

Co-Adaptation in an Ecosystem of Human–Machine Dyadic Interaction

2025-12-27T14:53:29+00:00

In August 2025, The Washington Post published a headline that captured widespread public attention: “It’s happening: People are starting to talk like ChatGPT” (Aleksic, 2025). Reporting on findings by Juzek and Ward (2025), the article described how lexical items overrepresented in ChatGPT’s output such as delve were appearing with increasing frequency in human speech. Striking as this discovery may be, it is not entirely surprising. Artificial intelligence (AI) systems have rapidly permeated everyday life, and large language models (LLMs) such as ChatGPT are now used by millions for writing, learning, planning, and problem-solving. Many users engage with these systems not once, but repeatedly over time.

Stylistic (A)synchrony in Human-Machine Dyadic Interaction: Investigating Co-Adaptation Through the Lens of Communicative Naturalness

2025-12-27T14:59:38+00:00

Naturalness in interaction is generally perceived as a necessary foundation for ensuring spontaneous, fluent and comfortable exchange of information. Though naturalness is a given, more or less, in interpersonal (i.e., human-human) interaction, this is not always the case for human-machine interaction. While large language models like ChatGPT undergo continual improvement, they still tend to fall short of natural “humanlike” communication (Voss & Waring, 2024).

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

2025-12-27T15:08:23+00:00

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.

It Takes Two to Tango: A Longitudinal Mixed-Methods Investigation of Human-AI Co-Adaptation Across Iterative Dialogues

2025-12-27T15:12:26+00:00

Given AI’s capacity to emulate human-like cognition, its integration into educational contexts, particularly in second language (L2) learning, has drawn increasing scholarly attention (Han, 2024; Zhang, 2023). Studies have shown that AI tools can support the development of lexical diversity, grammatical accuracy, and coherence in L2 writing (e.g., Chen, 2025; Escalante et al., 2023). There is also growing interest in how these tools influence learner autonomy, engagement, and motivation (Blake, 2007; Wei, 2023). However, despite this growing body of literature, little remains known about how learners and AI systems mutually influence one another during interaction.

Co-Adaptation in Human–Machine Interaction: What We Learned and Where To Go Next

2025-12-27T15:17:28+00:00

This forum set out to explore the question: How does co-adaptation unfold in an ecosystem of learner–ChatGPT dyadic interaction? Working with one graduate-level EFL learner from a seven-week interactional dataset from RECIPE4U, the three contributions approached this question from complementary perspectives: communicative naturalness and stylistic synchrony; formal and topical alignment; and cognitive-psychological trajectories indexed by LIWC and grounded in interactional moves.

Readers’ Credits for Volume 25, Issue 2

2025-12-27T15:22:49+00:00

Resolving Misunderstanding through an Extended Sequence

2025-10-22T13:37:03+00:00

This study investigates the resolution of misunderstandings through complex repair practices in Korean conversation. Utilizing data from a 45-minute phone call between two native Korean speakers, Sophia and Chan, the paper examines how an extended sequence of repair practices is employed to address a misunderstanding regarding a third person, Kihoon. Through a conversational analytic framework, the analysis reveals how the participants navigate and resolve the trouble source over multiple turns, demonstrating the use of various repair initiators. The findings highlight three key points: (1) the progression from weaker to stronger repair initiators as the repair sequence unfolds, (2) the elastic and robust nature of repair mechanisms that allow for extended trouble resolution, and (3) the implications for language teaching, emphasizing the importance of repair practices in second language acquisition. While the study offers insights into complex repair sequences in a non-English language, it acknowledges limitations in generalizability and suggests further research into diverse repair practices across different languages. This paper contributes to expanding the theoretical understanding of repair practices and provides practical insights for language educators.

Introducing Second Language Assessment

2025-10-28T14:40:51+00:00

Published by Cambridge University Press in 2025, Introducing Second Language Assessment by Gary J. Ockey, Professor of Applied Linguistics at Iowa State University, is a recent addition to the field of second language (L2) assessment. The book is aimed at educators, researchers, and students who are interested in gaining a general understanding of how language assessment works in theory and practice.