Publication Date

3-22-2026

Document Type

Conference Proceeding

Publication Title

International Conference on Intelligent User Interfaces Proceedings IUI

DOI

10.1145/3742414.3794947

First Page

268

Last Page

271

Abstract

This half-day (≈ 180 minutes), hands-on tutorial translates recent findings on large language model (LLM) support for qualitative research into a concise, end-to-end workflow grounded in our prior studies. Part I (LLM-supported semi-structured interviewing) distills design principles for AI-generated follow-up questions—covering role assignment, engagement patterns, user perceptions, and practical tactics for integrating LLM prompts during interviews while honoring consent and study protocols. Part II (LLM-supported coding and analysis) operationalizes results from prior work on using ChatGPT/LLMs for qualitative coding: moving from open coding to categories/themes, interpreting human–LLM alignment and inter-rater reliability findings at a conceptual level, and building a lightweight RAG-backed evidence path that links codes/themes to supporting excerpts. We conclude with a short discussion on applying these methods across domains and in instructional contexts. Attendees leave with slides and exemplar materials derived from the papers, plus a minimal RAG tookit that they can adapt to their own datasets.

Keywords

AI for research, experiment desgin, LLM supports qualitative study, qualitative methods

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Department

Industrial and Systems Engineering

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