Using AI in Qualitative Research: Will It Get You Published?

In this article, you’ll learn:

What peer-reviewed journals are already publishing when it comes to disclosed AI use
Why major publishers are asking for transparency, not prohibition
Why disclosure alone isn’t enough — and what paradigm coherence is important
What editors are actually screening for when AI has been part of your analysis
How to make a defensible call for your own project, regardless of how the wider debate eventually settles

Introduction

A few weeks ago, a researcher who’d been in one of my workshops sent me an email. She was an experienced academic, but relatively new to qualitative research, and she was hesitant — not about whether AI could help her work, but about what would happen afterwards. Her worry was simple and specific: if she started using AI in her qualitative research, would that stop her getting published?

If you’ve had a version of that thought, you’re not alone. It’s a reasonable question to have right now, in a field where an important recent statement — an open letter signed by 419 qualitative researchers (Jowsey et al., 2025) — argues that generative AI has no place in reflexive qualitative research at all. When a position that firm, articulated by so many respected qualitative research experts and academics, is circulating, “will this get my work rejected” starts to feel like the safer question to sit with rather than ask out loud.

This article looks at what’s actually true, as best the current evidence shows it: what’s already being published, what publishers’ policies actually say, and what editors seem to genuinely be looking for. The throughline is this — the real question isn’t whether using AI in qualitative research is allowed. It’s whether you can explain and stand behind what you did.

Disclosed AI use is already being published

This isn’t hypothetical. There’s a small but growing body of peer-reviewed work that explicitly reports AI use in qualitative analysis, transparently, in the methods section. Perkins and Roe (2024) describe inductive thematic analysis conducted with ChatGPT. Cevik and colleagues (2025) give a detailed, step-by-step account of using an LLM to support thematic analysis, including what didn’t work. Jayawardene and Ewing (2026) go further, laying out a full AI-augmented thematic analysis workflow with the researcher’s judgement made explicit throughout. None of these are fringe outlets pushing an agenda — they’re ordinary peer-reviewed journals, publishing ordinary transparent methods sections.

Major publishers have already written policies for this

Elsevier and Taylor & Francis both permit AI use in research, provided authors disclose how it was used and remain accountable for the analytic and intellectual work — keeping the analytic judgement with the researcher, not the AI. That’s a meaningfully different bar than “no AI.” It’s a transparency bar.

That said, this isn’t uniform, and pretending otherwise wouldn’t be honest. Journal policies vary, and how much disclosure they expect — and how it should read — differs from one to the next. Before you submit, check your target journal’s specific author guidelines rather than assuming a general policy applies. That single check is worth more than any reassurance I can offer here.

Disclosure alone isn’t the whole test

Being transparent about AI use matters, but it isn’t sufficient on its own. The more fundamental question is whether AI’s role is coherent with the paradigm your research sits within — whether your research design and analytic choices are actually pulling in the same direction. AI support that fits comfortably within one methodological approach can directly undermine another, regardless of how clearly it’s disclosed. Getting this right starts earlier than the write-up — it starts with the research design itself.

What editors seem to actually be looking for isn’t the absence of AI — it’s clarity about what it did and didn’t do, and whether that role made sense for the kind of analysis being conducted. What was AI used for. What decisions stayed with you as the researcher. Whether any use of AI was congruent with your research paradigm and methodology. That’s a very different — and much more answerable — set of questions than “am I allowed to use this at all.

This is really the heart of the current debate, and it’s worth naming plainly: reasonable, serious researchers currently disagree, sometimes sharply, about where AI belongs in qualitative work. That disagreement is real and it isn’t going to resolve itself by the time your next deadline arrives. Which means the more useful question isn’t “which side is right” — it’s can I explain and stand behind what I did, to a supervisor, a reviewer, or an ethics panel, if I’m asked to. Being able to answer that question won’t override a journal’s specific policy or a committee’s particular stance, but it’s the part that’s actually within your control, whatever their answer turns out to be.

Final takeaway

Using AI in qualitative research doesn’t have to be a binary choice between rejection and blind adoption. But there’s no single, settled test for what counts as coherent with a given methodology — reasonable experts can look at the same disclosed, methodologically-considered use of AI and reach different conclusions about whether it holds up. That, fundamentally, is the crux of the current debate: two careful, expert researchers can examine the same piece of work and land in genuinely different places.

Continue your learning

Given that, the more useful thing to focus on isn’t which side of the debate is right — it’s whether you can make a specific, defensible call for your own research, and justify it clearly and transparently as a matter of course. That’s exactly what I’ll be unpacking on August 25th in a free webinar. I’ll walk through how to reason through AI use in qualitative research so you can explain and stand behind your choices, whichever journal, supervisor, or ethics panel is doing the asking.

Register for the webinar here →

FAQs

Will disclosing AI use in my methods section hurt my chances of publication?

Not automatically. Peer-reviewed journals are already publishing work that transparently discloses AI use in analysis — what matters more to editors is clarity about what the AI did, and whether that role was coherent with the study’s methodology.

Do all journals allow AI use in qualitative research?

No — policies vary by publisher and journal. Elsevier and Taylor & Francis permit it with disclosure and researcher accountability, but you should always check your specific target journal’s author guidelines before submitting.

Is disclosing AI use enough on its own?

No. Disclosure is necessary but not sufficient — AI use also needs to be considered against the paradigm and research design being used. But there’s no single agreed test for what counts as “coherent,” and experts can genuinely disagree on the same case. The goal isn’t certainty that your use will be judged coherent — it’s being able to explain your reasoning clearly if that judgement is questioned.

Is there a consensus among qualitative researchers about AI use?

No. There’s active, sometimes sharp disagreement — including a widely signed open letter opposing AI in reflexive qualitative research. That disagreement isn’t likely to resolve soon, which is why being able to explain your own choices matters more than waiting for consensus.

References

Cevik, A. A., & Abu-Zidan, F. M. (2025). Utilizing AI-powered thematic analysis: methodology, implementation, and lessons learned. Cureus, 17(6), e85338.

Elsevier. (n.d.). Generative AI policies for journals. Retrieved 30 July 2026, from https://www.elsevier.com/en-au/about/policies-and-standards/generative-ai-policies-for-journals

Jayawardene, V., & Ewing, M. T. (2026). Generative AI-augmented thematic analysis. International Journal of Market Research, 68(2), 162–193.

Jowsey, T., Braun, V., Clarke, V., Lupton, D., & Fine, M. (2025). We reject the use of generative artificial intelligence for reflexive qualitative research. Qualitative Inquiry. Advance online publication. https://doi.org/10.1177/10778004251401851

Perkins, M., & Roe, J. (2024). The use of Generative AI in qualitative analysis: Inductive thematic analysis with ChatGPT. Journal of Applied Learning and Teaching, 7(1).

Taylor & Francis. (n.d.). Taylor & Francis’ position on the use of AI tools in research and publishing. Retrieved July 2026, from https://taylorandfrancis.com/our-policies/ai-policy/

Will using AI stop you from getting published?