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[2602.21136] SparkMe: Adaptive Semi-Structured Interviewing for Qualitative Insight Discovery
Summary
The paper presents SparkMe, a multi-agent LLM system designed for adaptive semi-structured interviewing, enhancing qualitative data collection by balancing predefined topics and emergent insights.
Why It Matters
This research addresses the limitations of traditional qualitative interviewing methods by leveraging large language models to improve data collection efficiency and depth. As organizations increasingly rely on qualitative insights for decision-making, tools like SparkMe can significantly enhance the quality and relevance of the information gathered.
Key Takeaways
- SparkMe optimizes interview utility by balancing topic coverage and emergent insights.
- The system outperforms previous LLM interview methods, improving coverage by 4.7%.
- User studies indicate high-quality adaptive interviews that yield profession-specific insights.
- SparkMe's open-source availability promotes further research and application in qualitative data collection.
- The approach highlights the potential of AI in enhancing human-computer interaction for qualitative research.
Computer Science > Human-Computer Interaction arXiv:2602.21136 (cs) [Submitted on 24 Feb 2026] Title:SparkMe: Adaptive Semi-Structured Interviewing for Qualitative Insight Discovery Authors:David Anugraha, Vishakh Padmakumar, Diyi Yang View a PDF of the paper titled SparkMe: Adaptive Semi-Structured Interviewing for Qualitative Insight Discovery, by David Anugraha and 2 other authors View PDF Abstract:Qualitative insights from user experiences are critical for informing product and policy decisions, but collecting such data at scale is constrained by the time and availability of experts to conduct semi-structured interviews. Recent work has explored using large language models (LLMs) to automate interviewing, yet existing systems lack a principled mechanism for balancing systematic coverage of predefined topics with adaptive exploration, or the ability to pursue follow-ups, deep dives, and emergent themes that arise organically during conversation. In this work, we formulate adaptive semi-structured interviewing as an optimization problem over the interviewer's behavior. We define interview utility as a trade-off between coverage of a predefined interview topic guide, discovery of relevant emergent themes, and interview cost measured by length. Based on this formulation, we introduce SparkMe, a multi-agent LLM interviewer that performs deliberative planning via simulated conversation rollouts to select questions with high expected utility. We evaluate SparkMe through contr...