@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://preview.aclanthology.org/fix-sig-urls/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."
}