Qualitative research with LLM chatbots: Technological reflexivity for interpretative technology

In this article, we advocate for qualitative research using LLM chatbots. While qualitative research may seem incompatible with generative artificial intelligence, we argue that these tools are fundamentally qualitative as they are built from text and are sensitive to social meaning. However, research using LLM chatbots should adhere to standards for reflexivity, in which researchers critically reflect on and accept accountability for the complexity and ambiguity inherent in the research process. Instead of emphasizing statistical validity and generalizability, responsible research with LLM chatbots requires technological reflexivity : examining model bias; researcher-algorithm interaction; critical evaluation; transparency; methodological reflexivity; and ethical considerations. We describe LLM chatbots, highlighting key considerations with using them. We demonstrate technological reflexivity in research with LLM chatbots and consider the impact of LLM chatbots on qualitative research outcomes. LLM chatbots can accelerate the research process, assist with thematic analysis, reveal researchers’ background assumptions, and make coding decisions more transparent. However, LLMs also introduce additional complexity into the research process, requiring researchers to manage important issues related to LLM model selection, personal and sensitive data protection, and the limits of informed consent. We conclude that, when used reflexively, LLM chatbots can make a positive contribution to the analysis of qualitative data. However, LLM chatnots cannot replace human researchers because research results must still be interpreted qualitatively.