Integrating Artificial Intelligence Within the Qualitative Workflow: An Introductory Primer

Current literature on Artificial Intelligence (AI) in qualitative research has largely focused on individual tools or models for data analysis, with less methodological attention paid to how researchers can strategically leverage or assess strengths or weaknesses of AI tools within qualitative research workflows. Furthermore, the use of AI in qualitative research has predominantly focused on the use of highly-visible, general purpose tools such as ChatGPT for qualitative analyses, while leaving a broader and increasingly consequential collection of AI technologies underexamined. This article provides a structured, methodological framework for selecting and integrating AI tools amid a crowded AI landscape. We present examples of how AI can be meaningfully integrated at several moments within qualitative research practice, from identifying grants, to enacting robust research and disseminating findings more broadly or through multiple media. As AI tools become more routinized within researchers’ everyday work, we believe that the use of AI has the potential to fundamentally reshape research practices. For this reason, we urge more curiosity-driven engagement with AI tools to understand their development, functions, and limitations. We propose a checklist to apply before using AI as well as a reporting tool for AI use. Moreover, we encourage senior researchers to take an active role in shaping norms around AI use within their fields, drawing on their disciplinary expertise to assess the validity of AI-generated content and to articulate clear guidelines, boundaries and standards for appropriate use. We also echo calls for greater investment in AI research and product development tailored to low- and middle-income countries, and for increased diversity in training data to reduce bias, address inequities, and improve fairness. We conclude by encouraging an open-yet-critical engagement with AI that prioritizes research quality, supports methodological innovation, and complements rather than replaces researchers’ expertise.