Rethinking Human-AI Collaboration in Interpretive Research

Speakers: Kevin Corley (Imperial) Xule Lin (Imperial) Current conversations about AI in qualitative research often focus on capability—what AI can or cannot do. But for interpretive researchers with inductive and abductive goals, the more fundamental question is epistemological: how does human-AI collaboration change the way understanding emerges? In this workshop, we move beyond the “tool vs. threat” framing to explore what actually emerges when human and artificial intelligence work together on meaning-making. Join us for a guided exploration that offers frameworks for developing your own informed epistemological stance—whether you end up embracing, questioning, or redefining the possibilities. Recommended readings: Bechky, B.A. & Davis, G.F. (2025). Resisting the algorithmic management of science: Craft and community after generative AI. Administrative Science Quarterly, 70(1): 1–22 Shanahan, M. (2024). Talking about large language models. Communications of the ACM, 67(2): 68–79. Lin, X., Corley, K., & Claude. (2025). LOOM XIV: The Calculator Fallacy. https://www.threadcounts.org/p/loom-x... Lin, X., Corley, K., & Claude. (2025). LOOM V: The Third Space. https://www.threadcounts.org/p/loom-v...