Critical AI literacy in qualitative research: knowing when – and when not – to use AI
Learning about AI in qualitative research doesn’t mean learning how to use AI at every opportunity. Critical AI literacy means understanding enough about the technology, the research and the competing arguments around its use to make deliberate decisions about whether, why and how AI belongs in your research — including when not to use it.
Prompt design for qualitative researchers: getting better results from AI
AI tools like ChatGPT are increasingly woven into the everyday workflows of qualitative researchers. But the quality and consistency of that support depends almost entirely on how you prompt the tool. This article explains the difference between task and system prompts, and introduces a simple framework for designing prompts that actually reflect your methodology.
Theme or topic? Understanding the difference in qualitative analysis
Many qualitative researchers reach a familiar moment in analysis: the labels are tidy, the quotes fit, the structure looks professional — and yet something still feels off. That instinct is often correct. Here’s how to tell whether you’re looking at a topic or a theme, and why the distinction matters more than it seems.
Writing as analysis: building interpretation through iterative drafting
In qualitative research, analysis is often described as a layered process — starting with descriptive codes and building toward complex, interpretive themes. Writing should work the same way, yet it’s too often treated as a linear afterthought. Here’s what it means to write with layers, and why it deepens interpretation rather than just improving clarity.
The essential guide to publishing qualitative research
Publishing qualitative research comes with its own set of challenges — from methodological flexibility to convincing reviewers used to quantitative norms. Here’s what I’ve learned, across years of submissions, rejections, and eventual acceptances, about setting a manuscript up for success.
Seeing through layers: how qualitative research explores interconnected systems of meaning
In qualitative research, we’re not just asking what happens — we’re asking how people make sense of it, and how that sense-making connects to the wider world around them. Here’s why meaning is never singular, and how understanding its layers sharpens every stage of a project.
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.