Conducting Qualitative Interviews with AI

Abstract

We introduce a new approach to conducting qualitative interviews by delegating the task of interviewing human subjects to an AI interviewer. Our AI interviewer conducts 381 interviews with human subjects about their reasons for not participating in the stock market. The AIconducted interviews uncover rich evidence on the underlying factors influencing nonparticipation in the stock market. Among our main qualitative findings is a prominent role for an active investing mental model. A separate large-scale survey shows that this mental model differs systematically between stock owners and non-owners. We also document systematic differences between factors identified in initial top-of-mind responses and those uncovered in subsequent responses, with mental models consistently emerging later in the interviews. Finally, a follow-up study shows that the interview data predicts economic behavior eight months after being collected, mitigating concerns about “cheap talk” in interviews. Our results demonstrate that AI-conducted interviews can generate rich, high-quality data at a fraction of the cost of human-led interviews.

Keywords: artificial intelligence, interviews, large language models, qualitative methods, stock market participation

JEL Classification: C830, C900, D140, D910, Z130