@inproceedings{wuttke-etal-2025-ai,
title = "{AI} Conversational Interviewing: Transforming Surveys with {LLM}s as Adaptive Interviewers",
author = {Wuttke, Alexander and
A{\ss}enmacher, Matthias and
Klamm, Christopher and
Lang, Max M. and
W{\"u}rschinger, Quirin and
Kreuter, Frauke},
editor = "Kazantseva, Anna and
Szpakowicz, Stan and
Degaetano-Ortlieb, Stefania and
Bizzoni, Yuri and
Pagel, Janis",
booktitle = "Proceedings of the 9th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2025)",
month = may,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.latechclfl-1.17/",
doi = "10.18653/v1/2025.latechclfl-1.17",
pages = "179--204",
ISBN = "979-8-89176-241-1",
abstract = "Traditional methods for eliciting people{'}s opinions face a trade-off between depth and scale: structured surveys enable large-scale data collection but limit respondents' ability to voice their opinions in their own words, while conversational interviews provide deeper insights but are resource-intensive. This study explores the potential of replacing human interviewers with large language models (LLMs) to conduct scalable conversational interviews. Our goal is to assess the performance of AI Conversational Interviewing and to identify opportunities for improvement in a controlled environment. We conducted a small-scale, in-depth study with university students who were randomly assigned to a conversational interview by either AI or human interviewers, both employing identical questionnaires on political topics. Various quantitative and qualitative measures assessed interviewer adherence to guidelines, response quality, participant engagement, and overall interview efficacy. The findings indicate the viability of AI Conversational Interviewing in producing quality data comparable to traditional methods, with the added benefit of scalability. We publish our data and materials for re-use and present specific recommendations for effective implementation."
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%0 Conference Proceedings
%T AI Conversational Interviewing: Transforming Surveys with LLMs as Adaptive Interviewers
%A Wuttke, Alexander
%A Aßenmacher, Matthias
%A Klamm, Christopher
%A Lang, Max M.
%A Würschinger, Quirin
%A Kreuter, Frauke
%Y Kazantseva, Anna
%Y Szpakowicz, Stan
%Y Degaetano-Ortlieb, Stefania
%Y Bizzoni, Yuri
%Y Pagel, Janis
%S Proceedings of the 9th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2025)
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-241-1
%F wuttke-etal-2025-ai
%X Traditional methods for eliciting people’s opinions face a trade-off between depth and scale: structured surveys enable large-scale data collection but limit respondents’ ability to voice their opinions in their own words, while conversational interviews provide deeper insights but are resource-intensive. This study explores the potential of replacing human interviewers with large language models (LLMs) to conduct scalable conversational interviews. Our goal is to assess the performance of AI Conversational Interviewing and to identify opportunities for improvement in a controlled environment. We conducted a small-scale, in-depth study with university students who were randomly assigned to a conversational interview by either AI or human interviewers, both employing identical questionnaires on political topics. Various quantitative and qualitative measures assessed interviewer adherence to guidelines, response quality, participant engagement, and overall interview efficacy. The findings indicate the viability of AI Conversational Interviewing in producing quality data comparable to traditional methods, with the added benefit of scalability. We publish our data and materials for re-use and present specific recommendations for effective implementation.
%R 10.18653/v1/2025.latechclfl-1.17
%U https://aclanthology.org/2025.latechclfl-1.17/
%U https://doi.org/10.18653/v1/2025.latechclfl-1.17
%P 179-204
Markdown (Informal)
[AI Conversational Interviewing: Transforming Surveys with LLMs as Adaptive Interviewers](https://aclanthology.org/2025.latechclfl-1.17/) (Wuttke et al., LaTeCHCLfL 2025)
ACL
- Alexander Wuttke, Matthias Aßenmacher, Christopher Klamm, Max M. Lang, Quirin Würschinger, and Frauke Kreuter. 2025. AI Conversational Interviewing: Transforming Surveys with LLMs as Adaptive Interviewers. In Proceedings of the 9th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2025), pages 179–204, Albuquerque, New Mexico. Association for Computational Linguistics.