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Paper short abstract

This paper offers qualitative researchers an adjudicative vocabulary for LLM-assisted inquiry, drawing on a tripartite framework of affordances, experience, and conceptualization to interpret, situate, and adjudicate the epistemic status that AI-generated material holds within qualitative inquiry.

Paper long abstract

Qualitative researchers are rapidly adopting Large Language Models across different stages of inquiry. However, the conditions shaping their methodological use are often left unstated. A growing body of work shows LLMs can assist qualitative work while intensifying questions of judgment, opacity, and responsibility. This paper focuses on a narrower problem: how qualitative researchers interpret, situate, and account for AI-generated material.

Instead of treating AI as a neutral tool or a non-human informant, the paper asks how AI-generated text becomes part of inquiry, since it may function as heuristic prompt, condensed summary, coding aid, draft language, or evidence. In prior research, the author developed a tripartite framework for analyzing relations to technology through affordances, experience, and conceptualization. That research showed that digitally mediated objects are rarely accessible directly but are approached through practices shaped by the technologies that enable them and their social contexts.

This framework is applied to specify what LLMs afford researchers, how working with such material is experienced, and how its epistemic status is conceptualized in the field. The contribution is not a new method but an adjudicative vocabulary for describing and structuring how researchers decide what status AI-generated material holds within inquiry. This matters because each new technological capacity reshapes individual experience and collective sense-making, and chat-based LLMs intensify this process by presenting themselves as open-ended assistants while leaving norms of methodological use unsettled. The argument connects grounded theory's concern with analytic fit and ethnomethodology's focus on contextual interpretation with the interpretive demands of AI-supported qualitative research.