AI Conversational Interviewing: Scaling Up Semi-Structured and In-depth Interviews

2026-06-18

AI Summary

This study addresses the tension in public opinion research between the limited scalability of in-depth interviews and the constrained openness of standardized surveys. To bridge this gap, we propose an AI-driven conversational interviewing method that supports voice, text, or multimodal interaction, enabling the collection of open-ended public opinions at scale via online experimental platforms (Prolific and Payback Panel). We systematically compare this approach with traditional questionnaires and demonstrate, for the first time, its effectiveness in capturing nuanced attitudes and cognitive patterns. The AI-mediated interviews reveal intergroup differences in cognitive shifts that conventional surveys fail to detect, while maintaining respondent experience on par with established methods. By open-sourcing our data and protocols, we offer a reproducible, computational social science paradigm for scalable qualitative insight generation.

Abstract

Public opinion research has long faced a trade-off between depth and scale: standardized surveys enable large-scale measurement but restrict respondents to researcher-defined categories, obscuring the diversity of unexpected considerations that underlie public sentiment. More conversational interviews provide richer insights through open-ended probing, but their reliance on trained human interviewers has kept them difficult to scale. This study introduces AI Conversational Interviewing as a method for collecting open-ended public opinion data at scale, pursuing three objectives: to demonstrate the analytical value of conversational text data for questions beyond the reach of closed-ended items; to assess the method's practical viability through participants' own evaluations; and to inform implementation by experimentally comparing voice-based, chat-based, and free-choice interview modes. We conducted a study combining an AI-led interview with a standardized survey on migration policy among 571 respondents recruited via Prolific and Payback Panel. The findings establish AI Conversational Interviewing as a viable and valuable addition to the social-science toolkit. The conversational transcripts surface considerations and reasoning that a comprehensive standardized battery does not capture such as markedly different mental models of migration among subgroups with similar attitudes levels. Among respondents who completed the interview, evaluations of the AI interview were at or above those of the standardized survey across modes, although completion itself varied by condition. By releasing open data and open-source pipeline materials, the study contributes to a growing literature on harnessing artificial intelligence to expand the methods of public opinion measurement.

Problem

• public opinion research
• depth vs. scale
• semi-structured interviews
• in-depth interviews
• open-ended data

Innovation

• AI Conversational Interviewing
• open-ended data collection
• public opinion measurement
• scalable qualitative methods
• human-AI interaction