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.
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.
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.