Also, CG’s ability to operate in different languages

allows researchers to use their datasets in their native

language when analyzing". This is a gamechanger.

Interesting that you did not list MAXQDA, which has both incorporated ChatGPT throughout its coding system and launched a new program, Tailwind, that is entirely based on ChatGPT.

Also, Atlas.ti has been purchased the same company that owns NVivo, so I suppose they will follow similar paths (assuming that Atlas.ti survives).

Of course, all of this depends on whether coding remains the dominant means of analyzing qualitative data. My own version of a Generative AI-based alternative to coding, "Query-Based Analysis: A Strategy for Analyzing Qualitative Data Using ChatGPT" is available for downloading here.

David L Morgan I am aware of MAXQDA and developments in Atlas ti. Tailwind, yes I have not explored. Thank you. I got your argument that if coding remains the dominant paradigm of Qual research - then AI might replace it with a new paradigm. I downloaded the QBA article. Thank you.

I just read the abstract of David's paper and agree with it. "AIs" (they are not intelligent) will be replacing traditional methods for all those who didn't think much while coding anyway. For the others: Would you trust a memo written by the "AI" (they are not intelligent) during coding? (See David on coding being part of a larger analytical process.) Would you use an "AI" that gets it right in 95% of the cases for a qualitative analysis? I think the whole coding process is at least as much about the analyst getting insights as it is about the text getting codes.

Jochen Glaser I agree that coding is way of gaining insights, but I also think it is an incredibly inefficient and poorly specified way of doing so. For me, working consciously with my data with an AI as a tool is much more engaging.

David L Morgan I really appreciated your paper on query-based analysis. I agree it will likely become an important part of qualitative analysis in the future. That said, I think certain aspects of coding are worth preserving in whatever new tools emerge.

One key benefit of coding is the ability to group together similar quotes or pieces of data. This helps both humans and LLMs move beyond surface-level responses toward more nuanced analysis. LLMs in particular struggle when they are given too much data at once—they can become distracted or produce confused outputs. Coding, by narrowing the focus to a smaller set of relevant quotes, reduces that cognitive “noise” and makes deeper analysis possible. Even as context windows expand, there’s still value in intentional focus. In that sense, coding acts as a focusing mechanism—something LLMs need just as much as we do.

Another advantage of coding is transparency. It provides a record of how conclusions were reached, which makes it easier to review your own work or understand someone else’s. Coding isn’t the only way to achieve this, of course. I really liked your paper on Iterative Thematic Inquiry, which I think pairs well with query-based analysis. Updating themes as you query the data, combined with good memoing, could offer transparency that’s equal to—or even better than—traditional coding. Still, I think building some kind of transparency mechanism into the process is essential.