Conducting Qualitative Interviews with AI

Abstract

Qualitative interviews offer rich insights into human behavior, but their lack of scalability has limited their use in economics. This paper introduces and evaluates AI-led interviews as a novel method for collecting qualitative data. Across different economic contexts,  a comprehensive set of quality metrics demonstrates the high quality of AI-led interviews. We then demonstrate that AI-led interviews generate thematically richer responses than other survey methods for collecting qualitative data at scale. A decomposition exercise shows that the benefits of AI-led interviews mainly arise from asking dynamic probing questions. Importantly, AI-led interviews make themes visible that would otherwise be unobserved. These themes matter: We show that responses in AI-led interviews are internally valid and predict future behavior six months later. Finally, we demonstrate that AI-led interviews allow hypothesis generation about important economic phenomena, reaching thematic saturation in small samples comparable to studies using human-led interviews.

Keywords: Artificial Intelligence, Interviews, Large Language Models, Qualitative Methods, Stock Market Participation, Mental Models, Surveys

JEL Classification: C83, C90, D14, D91, Z13