Neither reject nor adopt: why “defensibility” is the right frame for AI in qualitative research
In 2025, 419 qualitative researchers signed a letter rejecting generative AI outright. Computational methodologists pushed back just as forcefully. This article argues that rejecting AI wholesale doesn’t make the practical question go away — and sets out a more useful way to answer it.
Will using AI stop you from getting published?
If you’re using AI in your qualitative research and worried about the implications for getting your work published, you have a reasonable concern — in a field where serious researchers currently disagree, sometimes sharply, about where AI belongs. Here’s what the evidence, and journal policies, actually say.
Your research question is a commitment: how focus shapes qualitative inquiry
A research question isn’t just a starting point — it’s a commitment to a way of seeing, asking, and interpreting. Here’s how your question locates you within the different layers of meaning a qualitative project can explore.
Coding qualitative data: what shapes the way we analyse?
If you’re doing qualitative research, you’re probably coding your data — but coding isn’t just a step before the “real” analysis begins. Here’s what actually shapes how coding is done, and why there’s no single correct way to do it.
What researching sensitive topics has taught me about qualitative research
Researching sensitive topics brings the usual methodological challenges into sharp focus — but it also reveals something less discussed: how much the experience shapes the researcher. Here’s what a decade of this work has taught me about qualitative research as a whole.
What is a theme in qualitative research? Common misunderstandings explained
If there’s one word that quietly causes more confusion in qualitative research than almost any other, it’s “theme.” Here’s why the confusion happens — and how to recognise the early signs that your themes haven’t yet reached analytic depth.
Can AI recognise meaning in qualitative data or just patterns?
When an AI tool analysed an interview transcript, it returned a tidy list of themes — accurate, but hollow. It missed the most powerful moment in the conversation entirely. This article uses reflexive thematic analysis to explore why AI can detect patterns but cannot grasp meaning, and introduces a practical way to judge when AI use is defensible.
AI in qualitative research: what artificial intelligence can and cannot do
Artificial intelligence is transforming qualitative research — from transcription to coding — but does it deepen insight, or flatten it? Here’s what AI can genuinely contribute, where human interpretation remains essential, and a practical way to judge any specific use case by case.
Why research designs fail: 5 signs of misalignment in qualitative research
It’s a familiar, sinking feeling: a supervisor’s comment, a reviewer’s question, or a line of questioning in the viva that suddenly makes the whole project feel shaky. Too often, researchers only discover a problem with their research design at that point — by which time it’s expensive to fix.
Ten years of teaching qualitative research: five lessons I’ve learned
This year marks ten years since I first started teaching qualitative research — a workshop I said yes to almost on instinct. A decade and thousands of researchers later, here are the five lessons that have stayed with me the whole way through.