Klowait2026
| Klowait2026 | |
|---|---|
| BibType | ARTICLE |
| Key | Klowait2026 |
| Author(s) | Nils Klowait, Maria Erofeeva |
| Title | Nonhuman situational enmeshments — How participants build temporal infrastructures for ChatGPT |
| Editor(s) | |
| Tag(s) | EMCA, ChatGPT, Human-Computer Interaction, Interactional rhythms, Multimodal conversation analysis, Conversation analysis, Temporal enmeshment, AI Reference List |
| Publisher | |
| Year | 2026 |
| Language | English |
| City | |
| Month | |
| Journal | Linguistic Anthropology |
| Volume | 36 |
| Number | 1 |
| Pages | e70037 |
| URL | Link |
| DOI | 10.1111/jola.70037 |
| ISBN | |
| Organization | |
| Institution | |
| School | |
| Type | |
| Edition | |
| Series | |
| Howpublished | |
| Book title | |
| Chapter | |
Abstract
This paper investigates how participants recruit Large Language Models (LLMs) like ChatGPT as interactional co-participants depending on their temporal enmeshment within an interactional flow. Using Charles Goodwin's co-operative action framework, we analyze video data of human–AI interaction to trace the temporal structures established by human participants that define the LLM's participant role. We show how participants alternately orient to the artificial conversational agent as a dynamic human-like interlocutor and as a static, persistent document. Our findings contribute to understanding the role of temporality in establishing participation frameworks in human–AI interaction and problematize the effects of technological features on interaction, the malleability of language and tools, and the unique nature of language machines.
Notes
