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

arXiv: https://arxiv.org/abs/2606.20588 · HTML: https://mlsanity.com/paper/2606.20588/

## 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.

## Builds on (most cited first, 10 of 10)

- [Jailbreaking Large Language Models Against Moderation Guardrails via Cipher Characters](https://mlsanity.com/paper/2405.20413/) (2024)
- [On Overcoming Miscalibrated Conversational Priors in LLM-based Chatbots](https://mlsanity.com/paper/2406.01633/) (2024)
- [If I Hear You Correctly: Building and Evaluating Interview Chatbots with Active Listening Skills](https://mlsanity.com/paper/2002.01862/) (2020)
- [An Audit on the Perspectives and Challenges of Hallucinations in NLP](https://mlsanity.com/paper/2404.07461/) (2024)
- [AI Conversational Interviewing: Transforming Surveys with LLMs as Adaptive Interviewers](https://mlsanity.com/paper/2410.01824/) (2024)
- [A Reproducibility and Generalizability Study of Large Language Models for Query Generation](https://mlsanity.com/paper/2411.14914/) (2024)
- [Conversational User-AI Intervention: A Study on Prompt Rewriting for Improved LLM Response Generation](https://mlsanity.com/paper/2503.16789/) (2025)
- [Chatbots for Data Collection in Surveys: A Comparison of Four Theory-Based Interview Probes](https://mlsanity.com/paper/2503.08582/) (2025)
- [Agentic LLMs as Powerful Deanonymizers: Re-identification of Participants in the Anthropic Interviewer Dataset](https://mlsanity.com/paper/2601.05918/) (2026)
- [Privacy Meets Explainability: Managing Confidential Data and Transparency Policies in LLM-Empowered Science](https://mlsanity.com/paper/2504.09961/) (2025)

## 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.