LLM-based chatbots such as ChatGPT have given technologies that traditionally operated on the back end a user-friendly, conversational interface. Their rapid adoption among students has prompted universities worldwide to issue guidelines and re-examine existing practices. Supplementing prior research based on discourse analysis and self-reported measures (e.g., surveys and interviews), we propose an approach for analyzing naturally occurring student–chatbot interactions, rooted in conversation analysis of chats (n = 503) donated by eight current Danish university students. The analysis identifies conversational patterns across three main types of activities and examines how agency is negotiated across the structural dimensions of signification, domination, and legitimation. Despite methodological limitations related to the sample, this study offers a promising path toward understanding how human–machine relations are recursively shaped in dialogue.
Einarsson et al. (Sat,) studied this question.
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