Updated: Jul 6
In this series of posts I’m reflecting on the role of AI in qualitative data analysis that is receiving a lot of attention at the moment. There’ll be at least three posts, currently planned as followed (but subject to change if different things come to light to discuss or I end up with more to say than fits into three posts…
1. What’s afoot in the Qualitative AI space? Looking at the history of AI in qualitative software (CAQDAS-packages) – it’s not new folks!! – with an overview of the options and the topics being discussed right now.
CAQDAS-packages that incorporate forms of AI that have been around for a while include Qualrus, Discovertext, QDA Miner/WordStat and Leximancer.
Genres of new “Qual-AI” tools: using ChatGPT to facilitate different aspects of qualitative data analysis; new integrations of AI into existing CAQDAS packages and the development of new Aps designed specifically to harness recent AI developments for qualitative analysis.
2. Experimentations with new CAQDAS-AI tools. Discussing what is possible in newly-released AI, including “AI open-coding” in ATLAS.ti, AI Assist in MAXQDA and CoLoop. Sharing my experimentations with these tools and reflections on their utility in different contexts. Check out the following postst in this series
3. (COMING SOON) The methodological piece: the role of AI in qualitative analysis. Discussing when and why it might be appropriate to harness AI tools, and indeed, when it might not be. Discussing philosophical underpinnings of working qualitatively, the importance of units of meaning in assessing and using qual-AI tools, reflections on why it’s important to consider when the assistance happens, as well as how it happens, and some bits of advice about experimenting with and using them.