Author: Simon TLD

GenAI for QDA: LLM workflows, interactive tools, and methodological rigour

Here’s a short talk I recently gave at Monash University’s Symposium on Using AI in the Scholarship of Teaching & Learning Research Process: Methods, Tools and Practices, hosted by their AI in Research Reference Group and the Business Education Research Group. My thanks to Jess Co for inviting me.

It’s a snapshot of work underway in various projects with colleagues at UTS and beyond, and our PhD students. References + links in the slides. For more details on ThemeExplorer and GROPROE, see this seminar and the associated papers.

And here’s the ThemeExplorer video I showed, designed as part of the Student Voices on AI in HE project, codesigned with Antonette Shibani, Lisa-Angelique Lim, Trish McCluskey and the amazing Aneesha Bakharia (U Queensland) who coded it (paper below):

Bakharia, A., Shibani, A., Lim, L.-A., McCluskey, T., & Buckingham Shum, S. (2025). From Transcripts to Themes: A Trustworthy Workflow for Qualitative Analysis Using Large Language Models. Proceedings of Workshop From Data to Discovery: LLMs for Qualitative Analysis in Education, LAK25: 15th International Conference on Learning Analytics & Knowledge, Dublin, IRE, pp. 1-10. https://ceur-ws.org/Vol-3995/LLMQUAL_paper1.pdf

 

Meg Price (PSN 2026) Conversational AI and the Quiet Erosion of Relational Capacity

You’ve already had a conversation with AI that you used to have with a human. What did that cost you?

This was the focus of a talk by PhD researcher Meg Price (TD School) who just presented a snapshot of her work at the Annual Conference of the Possibility Studies Network. Her very transdisciplinary work  is co-supervised by academics from different backgrounds to my own, Barbara Doran and Paulina Larocca.

The abstract of her talk is below, and check out this interactive website version of her presentation. Dig further into her work on her Substack research blog.

What Are We Losing When It Gets This Easy?  Conversational AI and the Quiet Erosion of Relational Capacity

You’ve already had a conversation with AI that you used to have with a human. What did that cost you?

Millions of people now turn to generative AI, ChatGPT, Claude, Copilot, not just for task completion, but for emotional processing, relational advice, personal sense-making, and everyday decisions that were once distributed across human relationships. This shift is largely happening beneath conscious awareness: not in dramatic moments, but quietly, one prompt at a time.

This paper draws on preliminary research suggesting that these habits may narrow the field of relational possibilities. Meaningful human connection depends on conditions that conversational AI structurally removes: otherness, mutual risk, embodied co-presence, and the friction of disagreement, withdrawal, and repair, as well as the serendipity of encounters we could not have planned.  These are not obstacles to a relationship; they are the medium through which relational capacity develops. When the default response to uncertainty becomes “ask AI,” we may be practicing something quite different: a relational style that is smooth, low-effort, and low-stakes. What does this mean for our capacity to be creative, to flourish as the deeply relational, embodied, complexity-tolerating beings that we are? What possibilities close, quietly, as these habits consolidate?

These questions demand urgent, real-world investigation before patterns become too entrenched to examine clearly. To investigate, a Living Lab methodology has been developed precisely for this. Combining body mapping, guided AI reflection conversations, physiological monitoring, and facilitated group dialogue, it treats the body-mind as primary data and attends to relational capacity as it shifts in the texture of everyday life, not laboratory conditions. We draw on preliminary findings from our initial Living Lab sessions and expert interviews with researchers in relational neuroscience, attachment theory, and AI design to offer early and timely observations about what this methodology reveals and what it suggests about the stakes involved.