@inproceedings{gardhus-etal-2026-ainterviewer,
    title = "{AI}nterviewer: A Platform for Designing and Conducting {AI}-led Qualitative Interviews",
    author = "G{\r{a}}rdhus, Tobias  and
      Vitsakis, Nikolas  and
      Frederiksen, Fie Lejre  and
      Rogers, Anna  and
      Carlsen, Hjalmar Bang",
    editor = "Durrett, Greg  and
      Jian, Ping",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-demo.12/",
    pages = "119--127",
    ISBN = "979-8-89176-392-0",
    abstract = "There are now multiple proposals for systems based on Large Language Models(LLMs) to conduct automated qualitative interviews. This approach scales up qualitative interview techniques that have traditionally been constrained by the high costs of data collection. However, most of the current solutions rely on proprietary LLMs, which compromise reproducibility and data security. They also rely on LLMs for all interview tasks, which limits standardisation of question wording as well as control over question order. To address these issues, we introduce the AInterviewer platform, based on a multi-agent framework that combines controlled question administration of survey software with the flexibility of LLMs. AInterviewer can run with locally hosted models to ensure security and transparency. Our platform provides a web-based GUI supporting each phase of data collection: from interview guide design and pilot testing to interview distribution and data collection monitoring."
}