Interview Bot: Can Agentic LLM’s Perform Ethnographic Interviews?

Stine Lyngsø Beltoft, Peter Schneider-Kamp, Søren Tollestrup Askegaard

2025

Abstract

Chatbots based on large language models present a scalable and consistent alternative to human interviewers for collecting qualitative data. In this paper, we introduce the agentic chatbot “Interview Bot”, designed to mimic human adaptability and empathy in an interview setting. We explore to what extent it can handle the nuances and open-ended nature of ethnographic interviews. Our findings indicate that chatbots can engage participants and collect meaningful data, but that they still sometimes fall short of fully replicating human-facilitated interviews. Not withstanding challenges with the current state of the art, in the medium term, LLM-based agents hold great potential for scaling qualitative research beyond the confines of geographical, cultural, and language boundaries.

Paper Citation

in Harvard Style

Beltoft S., Schneider-Kamp P. and Askegaard S. (2025). Interview Bot: Can Agentic LLM’s Perform Ethnographic Interviews?. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 702-709. DOI: 10.5220/0013387800003890

in Bibtex Style

@conference{icaart25,
author={Stine Beltoft and Peter Schneider-Kamp and Søren Askegaard},
title={Interview Bot: Can Agentic LLM’s Perform Ethnographic Interviews?},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2025},
pages={702-709},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013387800003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Interview Bot: Can Agentic LLM’s Perform Ethnographic Interviews?
SN - 978-989-758-737-5
AU - Beltoft S.
AU - Schneider-Kamp P.
AU - Askegaard S.
PY - 2025
SP - 702
EP - 709
DO - 10.5220/0013387800003890
PB - SciTePress