AInterviewer: Automated Interviews for Large-Scale Qualitative Data Collection

To develop AInterviewer, a method and platform enabling qualitative interviewing at scale through AI. The research examines how AI-led interviews can gather rich insights across large populations while ensuring methodological integrity and researcher control.

AInterviewer addresses a fundamental methodological tension in social research; the trade-off between qualitative depth and quantitative scale. Built on open-source language models and a multi-agent workflow, the platform enables researchers to conduct AI-led qualitative interviews while retaining full control over interview design, execution, and data management.

The project pursues four main objectives. First, to develop AI-interview as a distinct social science data collection method. Secondly, to develop a platform that supports the entire research process from pilot testing to large-scale deployment. Third, to ensure methodological integrity by keeping researchers in control of every stage of the process. Third, to demonstrate the benefit of AI-interviews for substantive social science agendas.

At its core, the AInterviewer project seeks to answer the question: can generative AI be used to conduct interviews that contain qualitative depth at large scale? Rather than replacing the researcher, the platform is designed as a methodological contribution, a tool which can be used to bridge the gap between depth and scale.

Funded by:

Project: AInterviewer: Automated Interviews for Large-Scale Qualitative Data Collection.

Period:  2024-2027