Exploring ChatGPT’s potential as a qualitative research partner: researcher and participant perspectives on AI-generated insights

Abstract

The increasing presence of generative AI in research presents both opportunities and challenges for qualitative data analysis. While generative AI tools such as ChatGPT can assist with pattern recognition, text classification, and summarisation, their role in in-depth, interpretive qualitative analysis remains under-theorised. This article draws on Actor-Network Theory to examine the integration of ChatGPT as a non-human (and arguably more-than-human) actor within a socio-material assemblage of qualitative data analysis. Using a researcher–participant–ChatGPT triadic model, we explore how analytic insight develops through processes of translation, reflexivity, and relational engagement. Our findings suggest that ChatGPT participates in the co-construction of meaning, prompting theoretical reflection, unsettling researcher assumptions, and contributing to distributed agency within the research network. Rather than streamlining analysis, ChatGPT reconfigures it, offering a new mode of participatory research in which power, interpretation, and knowledge are dynamically negotiated across human and non-human actors.

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