Towards a methodologically congruent framework for GenAI use in nonpositivist qualitative research

Abstract

GenAI is increasingly used in qualitative research, yet much current practice remains methodologically incongruent. Attempts to make LLMs replicate qualitative coding often create a mismatch between stated qualitative values (e.g., depth, context, and reflexivity) and actual AI-supported analytic practices (e.g., surface-level extraction and automated coding). This article distinguishes between technological incongruence, where GenAI is asked to perform tasks it is not designed to handle, and qualitative methodological incongruence, where AI workflows reproduce the surface procedures of qualitative analysis while neglecting its interpretive and reflexive commitments. We propose a shift from ‘AI as coder’ to ‘AI as interlocutor’. We outline key tenets for methodologically congruent GenAI use in non-positivist qualitative research: relational epistemology, ethical, responsible and rigorous use, researcher’s lead role, and abductive dialoguing. GenAI can support qualitative analysis by surfacing, retrieving, comparing, and suggesting material for interpretation, but meaning-making remains the responsibility of the human researcher.

Keywords: generative AI, large language models, methodological incongruence, nonpositivist qualitative research, qualitative data analysis, thematic analysis