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How do we maintain scientific rigour when AI “makes sense” of our data? This event aims to discuss how complementary ideas and approaches intersect in tackling the emerging technology of our times. This is not about resisting AI; it’s about ensuring human scholarship remains its sovereign architect.
Event details of Beyond CAQDAS: Reclaiming Methodological Agency in the Age of GenAI - A Workshop on Epistemic Partnership in Qualitative Research
Date
7 May 2026
Time
10:00 -15:00

For 30+ years, we’ve used CAQDAS tools to organise our data. But GenAI represents a fundamental shift: from structuring our analysis to conversing with an AI that generates interpretations for us. This creates urgent questions:

  • How do we maintain scientific rigour when AI “makes sense” of our data?
  • What analytical skills do researchers need to work with (not for) AI?
  • How do we teach the next generation to be critical partners, not passive prompters?

The integration of Generative AI into qualitative research represents not a technical upgrade, but a fundamental rupture. For three decades, CAQDAS tools helped researchers structure and organise data.
GenAI does something different: it converses, interprets, and generates meaning.

This transformation exposes a long-hidden vulnerability. Qualitative methodology has historically prioritised data collection over the formalisation of analytical reasoning. Most researchers were never taught how analysis works; they learned templates and software features, not the underlying logic. Now, as AI offers instant interpretations, this gap becomes dangerous.

The Core Challenge:
A methodologically “weak” researcher cannot effectively partner with a rhetorically “strong” AI. Without understanding the fundamentals of analytical thinking, researchers risk becoming passive consumers of
AI-generated insights are outsourcing sense-making to algorithms.

Our Proposal:
We aim to discuss how three complementary ideas and approaches intersect. The first draws on Ian Dey’s systematic procedures for qualitative analysis—classifying, linking, and connecting. The second brings in a complexity-informed approach to social science methodology, attending to emergence, non-linearity, and context-dependence. The third engages with a philosophy-of-science perspective on causal evidence, integrating variational and mechanistic reasoning. Together, these three orientations provide a basis for positioning AI as a “Sense-Making Agent” rather than a mere tool. This means, among other things, that researchers must validate AI suggestions through mechanistic reasoning and reflexive judgment—exercising a profound critical attitude and sustained engagement with AI in qualitative methods.

Workshop Goals:

  • Develop standards for AI-assisted qualitative analysis
  • Bridge methodologists and software developers
  • Build a research agenda for epistemic responsibility in automated workflows

This is not about resisting AI; it’s about ensuring human scholarship remains its sovereign architect.

Speakers

Elif Kus Saillard
Lasse Gerrits
Judith Schoonenboom

If you are interested in attending this event, please send an email including a short motivation to Federica Russo or Elif Kus Saillard, before 31 March. 

About the organiser

Federica Russo is Professor of Philosophy and Ethics of Techno-Science at Utrecht University and External Faculty Member at IAS. She has long-standing interest in methods in the sciences. At IAS, Federica has organised several seminar series and events in the area of health complexity and mixed methods research.