This week’s blogpost is authored by Dr. Sean Halpin, who is a qualitative and mixed-methods researcher at RTI Health Solutions specializing in patient-centered outcomes research, clinical outcome assessment development, and qualitative methodology. His work focuses on concept elicitation, cognitive interviewing, and the design and evaluation of observer-, caregiver-, and clinician-reported outcome measures, particularly within rare genetic and neurodevelopmental conditions. In addition to his applied research, Dr. Halpin publishes on qualitative rigor, epistemic alignment, team-based qualitative analysis, and methodological coherence in qualitative inquiry. His work has appeared in journals including Journal of Patient-Reported Outcomes, The Gerontologist, Innovation in Aging, and American Journal of Qualitative Research. Dr. Halpin serves on the editorial board of Innovation in Aging as a Qualitative Research Specialist and is a Fellow of the Gerontological Society of America.
“And all this science, I don’t understand. It’s just my job, five days a week.” – Elton John (Rocket Man)
These lyrics from Elton John’s song Rocket Man have lingered in my mind recently as artificial intelligence (AI) becomes increasingly integrated into qualitative research workflows. The song was inspired by Ray Bradbury’s short story The Rocket Man, which tells the story of a father who repeatedly leaves his family to travel into space despite understanding the danger (Bradbury, 1951). The father warns his son never to become a “rocket man,” yet he ultimately returns to space one final time and dies when his ship falls into the sun. Afterwards, his wife and son begin living largely at night, avoiding the sun that took him away. What strikes me about both the song and Bradbury’s story is the sense of technological estrangement running through them. The father participates in something vast, complex, and powerful, yet the technology gradually distances him from ordinary life and from the people around him. Increasingly, I worry qualitative researchers may be entering a similar relationship with AI.
At work, AI tools have quietly become part of many researchers’ everyday routines. Some use Microsoft Teams AI assistants to summarize meetings. Others use Microsoft Copilot to condense long email threads into digestible bullet points. New interview platforms now offer a nearly seamless pipeline for qualitative research: scheduling interviews, hosting interviews, automatically generating transcripts, and producing AI-generated “themes” or “insights” across interviews. Importantly, these tools often work remarkably well for administrative tasks. AI-generated summaries can save time. Automated transcription has already transformed qualitative workflows. Yet I have become increasingly concerned about a more specific trend: the growing desire to use AI to perform inductive qualitative analysis itself.
In many applied research settings, the appeal is obvious. Inductive analysis is time intensive, labor intensive, and expensive. Researchers face pressure to move quickly, reduce costs, and produce more deliverables for more clients. AI-assisted inductive coding appears to offer a solution (Morgan, 2026). However, the growing normalization of AI-generated inductive insights raises important methodological questions. Qualitative induction is not simply a process of extracting recurring words or identifying surface-level patterns. Inductive analysis involves interpretation. Researchers make decisions about meaning, context, emphasis, contradiction, and conceptual relationships throughout the analytic process. Even highly structured qualitative approaches rely upon assumptions about how meaning should be identified and interpreted. Recent comparisons between large language models and human qualitative analysts suggest AI may perform reasonably well at identifying broad thematic patterns while still struggling with contextual nuance and interpretive depth (Hill et al., 2026).
Yet many researchers already conduct qualitative analysis procedurally, following coding steps without deeply engaging with the methodological assumptions underlying those procedures (Halpin, in press). AI may intensify this tendency. Increasingly polished AI outputs may create the illusion that interpretation itself has become automated. What concerns me most is not necessarily the use of AI, but the gradual habituation to outsourcing interpretive work without fully recognizing what is being outsourced. Once researchers become accustomed to AI-generated summaries, AI-generated action items, AI-generated transcripts, and AI-generated themes, it becomes easier to accept AI-generated interpretations as simply another routine component of the workflow.
The danger is especially pronounced for inductive qualitative analysis because inductive approaches often lack the guardrails present in more deductive or structured methodologies. In deductive coding, researchers may begin with an existing framework, predefined concepts, or carefully articulated research questions. Inductive analysis, however, often depends heavily on researcher judgment concerning what concepts matter, how concepts relate to one another, and how patterns should ultimately be interpreted. Delegating these decisions to AI risks transforming qualitative analysis into a process of procedural pattern detection divorced from methodological reflection.
To be clear, I am not arguing that AI should be excluded from qualitative research. AI tools may eventually become highly valuable supports for qualitative inquiry. Researchers may use AI productively for data organization, retrieval, preliminary exploration, or identifying areas for further analytic attention. Rather, my concern is that researchers may begin mistaking computational plausibility for qualitative understanding.
Bradbury’s The Rocket Man was not fundamentally a story about technology. It was a story about becoming consumed by a system powerful enough to gradually reshape how people live, think, and relate to one another. Qualitative researchers should be careful not to allow AI-assisted induction to distance them from the interpretive foundations of qualitative inquiry itself. Otherwise, we may eventually find ourselves producing increasingly efficient analyses while understanding less and less about how those analyses were truly constructed.
References
Bradbury, R. (1951). The Illustrated Man. Doubleday.
Halpin, S.N. (2026). Recalibrating epistemic alignment: A researcher’s journey towards methodological coherence. Qualitative Research. Online first. DOI: 10.1177/14687941261451385
Hill, C., Dahil, A., Simpson, G., Hardisty, D., Keast, J., Pinn, C. K., & Dambha-Miller, H. (2026). Large language models for thematic analysis in healthcare research: A blinded mixed-methods comparison with human analysts. PLOS Digital Health, 5(4), e0001189.
Morgan, D. L. (2026). Query-based analysis: A strategy for analyzing qualitative data using ChatGPT. Qualitative Health Research, 36(2-3), 206-217.
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