The Gen-AI landscape from RAG to riches
Antony Bryant
Generative AI has arrived and will have massive consequences for all of us. In the realm of qualitative research its impact has been significant, eliciting calls from some for a wholescale rejection its use in qualitative data analysis. For many this is locking the stable door after the horses have escaped. For others Gen-AI is a tool, albeit very powerful and highly distinctive, which can be used effectively with sufficient insight and care. In this session Tony Bryant will give an overview of Gen-AI and will then develop a focus around the issues effecting qualitative research in general and GTM in particular.
Grounded Theory, Interpretivism and the Automated Turn
Carrie Friese
The meaning of interpretivism is shifting. Its key characteristics – a focus on multiple meanings, the symbolic nature of interaction, the ongoing and performative making of social and material worlds and the use qualitative methodologies – are being problematized by digitization generally and AI specifically. At the very least, digitization and, thus, quantification, underpins a significant portion of contemporary meaning making processes and interactions. To understand social life, interpretivism must account for this quantification and, often in turn, needs to use at least some of these tools of quantification for the purpose of achieving the hermeneutic goal of understanding.
In the process, the meaning of interpretivism is changing in ways that are often subliminal and thus are not being clearly accounted for. For example, computational grounded theory asserts that the goal of grounded theory has always been to ‘measure’ meaning. This characterisation does not resonate with me. What does it mean to shift from interpreting to measuring meaning?
I will argue that in this era it is crucial that we be careful about how ‘interpretive’ is used in designating research. My goal is not to police boundaries of grounded theory, but rather to emphasize the implications of automation for grounded theory as well as the contested meanings of AI.
Coding Without Coding: Returning to the Analytic Spirit of Strauss and Corbin’s Grounded Theory through AI-Supported Conversational Analysis
Susanne Friese
This presentation presents a methodological argument and practical illustration: that AI-assisted analysis, implemented through the CA²AI (Conversational Analysis with AI) framework, enables researchers to work closer to the original spirit of Strauss and Corbin’s grounded theory than many contemporary software-assisted approaches allow.
Strauss and Corbin’s grounded theory has long been associated with the technical act of coding — the systematic labelling of data segments. Yet a closer reading of their foundational texts, including Strauss’s (1987) field notes, reveals that coding was never merely a tagging exercise. Those notes contained analytical thoughts, verbatim fragments, theoretical memos, sampling directions, and early articulations of the coding paradigm. The emphasis was always on immersive engagement with data, reflective thinking, and constant comparison — with coding as a vehicle for that thinking, not its destination.
Over time, the adoption of qualitative data analysis software shifted attention toward the technical management of codes, fragments, and categories, often at the expense of interpretive depth. Researchers found themselves managing data architectures rather than developing theory.
AI changes this equation. Rather than requiring researchers to fragment data into labelled units before analysis can proceed, AI enables direct, dialogic engagement with the data. The researcher converses with the material — questioning it, probing for meaning, generating conceptual possibilities — in a process that more closely resembles Strauss and Corbin’s original vision of analytic immersion.
As part of the presentation, I illustrates the argument through an example of open coding reimagined within the CA²AI framework showing that AI does not replace the analytic work of Strauss and Corbin’s method; it removes a procedural layer that had obscured it. By reducing the technical aspect of coding, AI repositions the researcher as an interpretive thinker engaged directly with data — precisely the role Strauss and Corbin envisioned.
Using MAXQDA AI Assist for Grounded Theory: Practice and Methodological Reflections
Stefan Räddiker
Grounded Theory’s iterative process of exploring data, coding, memoing, and theorizing can be well supported by artificial intelligence. This session demonstrates how MAXQDA’s AI Assist features integrate into a Grounded Theory workflow — from initial open coding through more advanced stages of coding to theoretical integration.
The session begins with MAXQDA’s core tools for conducting Grounded Theory: organising documents and codes, coding data (e.g. in open-coding mode), and writing memos. Building on this foundation, it shows how AI-powered features can accompany manual analysis as an assistant and as a thought partner:
Explaining unfamiliar terms during data exploration
Suggesting initial/open codes for generating conceptual labels
Summarising cases and coded passages for data exploration and category development
Interactive chats with coded passages and memos for comparison and theory development
Throughout, participants will see how interactive source references keep the original data accessible — ensuring that AI outputs remain grounded in the empirical material.
The session also clarifies how MAXQDA’s integrated AI features differ from general-purpose chatbots like ChatGPT or Claude, and addresses key methodological and ethical questions: data privacy, the role of AI in the analytical process, understanding AI bias, and how AI can help researchers reflect on their own assumptions.
Responding to the Challenge of AI and Auto Coding: How does it change the practice of Grounded Theory?
Cathy Urquhart
The nature of qualitative data has changed hugely since the inception of grounded theory in 1967. We now have access to huge tranches of digital data, and computational assistance in the form of AI to help us analyse that data. People also talk about ‘computational grounded theory’ (Nelson 2020) where automated approaches are used to detect patterns in large datasets. AI is the latest iteration of this, giving us ‘auto coding’. This presentation considers the auto coding tools available, and how these tools may – or may not – challenge the very tenets of grounded theory. What might be gained, and what might be lost? How can AI and auto coding be used to help researchers without losing key aspects such as theoretical sensitivity?
Issues using ChatGPT in Grounded Theory research – a guide for researchers
Nieky van Veggel, Hilary Engward, Melanie Birks and Jane Mills
Abstract to follow.
To infinity and beyond!
Antony Bryant
In this session, we will try to summarize the key topics and concerns of the previous sessions, offering some ideas about how the GTM community – particularly those working consciously as constructivists – can move forward, both in concert and within distinctive varieties of GTM.