mlsanityData to 14 Oct 2026

AInterviewer: A Platform for Designing and Conducting AI-led Qualitative Interviews

arXiv 2606.20588 ↗First posted 15 May 2026cs.HC cs.AIcited by 0

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Abstract (arXiv)

There are now multiple proposals for systems based on Large Language Models (LLMs) to conduct automated qualitative interviews, but most of the current solutions rely on proprietary LLMs, which compromises 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, an opensource solution based on a multi-agent pipeline that combines controlled question administration of survey software with the flexibility of LLMs. AInterviewer is an interdisciplinary effort designed to implement best practices of qualitative interviewing in social science, and it can run with locally hosted models to ensure security, transparency, and reproducibility. 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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 33 references that are in the map. First posted on arXiv 15 May 2026.

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References · 10

  1. 2024Jailbreaking Large Language Models Against Moderation Guardrails via Cipher Characterscited by 42
  2. 2024On Overcoming Miscalibrated Conversational Priors in LLM-based Chatbotscited by 24
  3. 2020If I Hear You Correctly: Building and Evaluating Interview Chatbots with Active Listening Skillscited by 20
  4. 2024An Audit on the Perspectives and Challenges of Hallucinations in NLPcited by 17
  5. 2024AI Conversational Interviewing: Transforming Surveys with LLMs as Adaptive Interviewerscited by 11
  6. 2024A Reproducibility and Generalizability Study of Large Language Models for Query Generationcited by 10
  7. 2025Conversational User-AI Intervention: A Study on Prompt Rewriting for Improved LLM Response Generationcited by 10
  8. 2025Chatbots for Data Collection in Surveys: A Comparison of Four Theory-Based Interview Probescited by 9
  9. 2026Agentic LLMs as Powerful Deanonymizers: Re-identification of Participants in the Anthropic Interviewer Datasetcited by 7
  10. 2025Privacy Meets Explainability: Managing Confidential Data and Transparency Policies in LLM-Empowered Sciencecited by 1

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