SparkMe: Adaptive Semi-Structured Interviewing for Qualitative Insight Discovery

2026-02-24

AI Summary

Existing LLM-based interview systems struggle to balance predefined topic coverage with adaptive exploration, limiting the scalable acquisition of high-quality qualitative user insights. This work proposes a multi-agent LLM architecture that frames adaptive semi-structured interviewing as a utility optimization problem, formally defining interview utility as a trade-off among topic coverage, discovery of novel insights, and conversational cost. The system dynamically plans high-expected-utility questions through simulated dialogue rollouts. Experiments demonstrate that, in LLM simulations, the approach improves topic coverage by 4.7% and yields richer insights in fewer turns. A user study with 70 participants further validates that domain experts recognize the method’s ability to uncover high-quality insights in professional contexts that existing approaches fail to capture.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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 controlled experiments with LLM-based interviewees, showing that it achieves higher interview utility, improving topic guide coverage (+4.7% over the best baseline) and eliciting richer emergent insights while using fewer conversational turns than prior LLM interviewing approaches. We further validate SparkMe in a user study with 70 participants across 7 professions on the impact of AI on their workflows. Domain experts rate SparkMe as producing high-quality adaptive interviews that surface helpful profession-specific insights not captured by prior approaches. The code, datasets, and evaluation protocols for SparkMe are available as open-source at https://github.com/SALT-NLP/SparkMe.

Problem

• semi-structured interviewing
• qualitative insight discovery
• adaptive interviewing
• large language models
• emergent themes

Innovation

• adaptive interviewing
• large language models
• interview utility optimization
• emergent theme discovery
• multi-agent LLM