AI and Qualitative Health Research: Working Through a Necessary Grieving Process

Abstract

Qualitative health research has been shaken by the rapid uptake of artificial intelligence (AI), especially large language models. Drawing on Kübler-Ross's five-stage grief heuristic, we articulate a provocative, yet constructive, map of the field's current tensions (denial, anger, negotiation, depression, acceptance) around AI's pros and cons. We argue that what is at stake is not simply efficiency but our very identity of qualitative researchers: reflexivity, intersubjectivity, temporality, and the role of researcher subjectivity. We propose concrete practices compatible with qualitative research's epistemic and ethical commitments: collective prompt-writing, "coding retreats" for critical oversight of outputs, explicit disclosures, and transparency about what is (and is not) delegated to machines. Rather than reject or romanticize AI, we advocate a rigorous, ethically grounded co-working with it, that safeguards slowness, presence, and dialogical sense-making. Our contribution is to reframe AI as a catalyst for renewed reflexivity and methodological clarity, while warning against the erosion of embodied and collective thinking when research becomes "alone-with-AI." We conclude with actionable recommendations for reviewers, editors, and researchers to evaluate AI-assisted manuscripts without abandoning qualitative health research's core: careful attention to meaning, situated ethics, and intersubjective critique.

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