Scale, Engage, or Both?: Potential and Perils of Applying Large Language Models in Interview and Conversation-Based Research
An increasing number of studies apply tools powered by large language models (LLMs) to interview and conversation-based research, one of the most commonly used research methods in CSCW. This panel invites the CSCW community to critically debate the role of LLMs in reshaping interview-based methods. We aim to explore how these tools might (1) address persistent challenges in conversation-based research, such as limited scalability and participant engagement, (2) introduce novel methodological possibilities, and (3) surface additional practical, technical, and ethical concerns. The panel discussion will be grounded on the panelists’ prior experience applying LLMs to their own interview and conversation-based research.We ask whether LLMs offer unique advantages to enhance interview research, beyond automating certain aspects of the research process. Through this discussion, we encourage researchers to reflect on how applying LLM tools may require rethinking research design, conversational protocols, and ethical practices.
Authors
- Shivani Kapania (ORCID: https://orcid.org/0000-0002-0152-4311)
- Hope Schroeder (ORCID: https://orcid.org/0000-0001-7826-3298)
- Marianne Aubin Le Quéré (ORCID: https://orcid.org/0000-0003-4189-8040)
- Steven P. Dow (ORCID: https://orcid.org/0000-0002-1354-9866)
- Eugenia Ha Rim Rho (ORCID: https://orcid.org/0000-0002-0961-4397)
- Alejandro Cuevas (ORCID: https://orcid.org/0000-0001-6507-1334)
- Angel Hsing‐Chi Hwang (ORCID: https://orcid.org/0000-0002-0951-7845)
Institutions
- University of Southern California (US)
- Princeton University (US)
- University of California San Diego (US)
- Massachusetts Institute of Technology (US)
- Carnegie Mellon University (US)
- Virginia Tech (US)
Publication Details
- Published
- 2025-10-17
- DOI
- https://doi.org/10.1145/3715070.3748284
- Citations
- 1
- Primary Topic
- Focus Groups and Qualitative Methods
- Type
- article
- Field-Weighted Citation Impact
- 4.20