Blog Posts

The recently published editorial by Jűrgen Rudolph and colleagues, Don’t believe the hype: AI myths and the need for a critical approach in higher education covers several topics concerning the use of Generative-AI (GenAI) in Higher Education (HE), that I’ve been thinking about and working on in relation to Qualitative Data Analysis (QDA). And I’m reflecting on them in this post. For more info on my related recent and forthcoming work in these areas see links to resources thr

The potential implications of Generative-AI (Gen-AI) for the practice of qualitative data analysis (QDA) heightens the need for researchers to critically reflect on what it means to interpret. It's more important now than ever before to remember that it is the “interpretive intelligence” that humans bring to analysis that makes it what it is, that makes it valuable. I caution against jumping on the AI-bandwagon without very carefully considering what appropriate use of these

Discussion of the impact and risks of AI and qualitative deepfake on credibility and impartiality.

As we approach the end of another Gregorian calendar year it's natural to become reflective about what's gone before and what's potentially to come. The past year has seen a lot of movement in the computer-assisted qualitative data analysis (CAQDAS) space; a lot more than we've seen in any 12 month period for a good while. Sparked by the emergence of generative-ai, qualitative researchers have been considering their craft. But as the furore settles down, it’s clear that many

There are many different Qualitative-AI tools – some very new, others that have been around a while – and just as many different views about their appropriateness and implications. The previous posts in this series touched on the history of AI in qualitative software programs (CAQDAS-packages), and reviewed three implementations of generative-AI that have been released in 2023; ATLAS.ti’s AI “open-coding”; MAXQDA’s AI Assist, and CoLoop’s AI copilot. Rather than jumping on th