# AI Conversational Interviewing: Transforming Surveys with LLMs as Adaptive Interviewers

arXiv: https://arxiv.org/abs/2410.01824 · HTML: https://mlsanity.com/paper/2410.01824/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 65 references that are in the map.
- First posted on arXiv 16 Sep 2024.

## Builds on (most cited first, 8 of 8)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting](https://mlsanity.com/paper/2310.11324/) (2023)
- [Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model](https://mlsanity.com/paper/2402.07827/) (2024)
- [PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts](https://mlsanity.com/paper/2306.04528/) (2023)
- [Do large language models resemble humans in language use?](https://mlsanity.com/paper/2303.08014/) (2023)

## Built on by (most cited first, 11 of 11)

- [AutoSurvey2: Empowering Researchers with Next Level Automated Literature Surveys](https://mlsanity.com/paper/2510.26012/) (2025)
- [Telephone Surveys Meet Conversational AI: Evaluating a LLM-Based Telephone Survey System at Scale](https://mlsanity.com/paper/2502.20140/) (2025)
- [AI-Assisted Conversational Interviewing: Effects on Data Quality and Respondent Experience](https://mlsanity.com/paper/2504.13908/) (2025)
- [Too Open for Opinion? Embracing Open-Endedness in Large Language Models for Social Simulation](https://mlsanity.com/paper/2510.13884/) (2025)
- [Mic Drop or Data Flop? Evaluating the Fitness for Purpose of AI Voice Interviewers for Data Collection within Quantitative & Qualitative Research Contexts](https://mlsanity.com/paper/2509.01814/) (2025)
- [Artificially intelligent agents in the social and behavioral sciences: A history and outlook](https://mlsanity.com/paper/2510.05743/) (2025)
- [AI-Driven Decision-Making System for Hiring Process](https://mlsanity.com/paper/2512.20652/) (2025)
- [Equitable Evaluation via Elicitation](https://mlsanity.com/paper/2602.21327/) (2026)
- [AI and Collective Decisions: Strengthening Legitimacy and Losers' Consent](https://mlsanity.com/paper/2604.05368/) (2026)
- [AInterviewer: A Platform for Designing and Conducting AI-led Qualitative Interviews](https://mlsanity.com/paper/2606.20588/) (2026)
- [Positive Ratings, Hidden Concerns: Employee Voice Disclosure in AI-Mediated Organizational Listening](https://mlsanity.com/paper/2609.38788/) (2026)

## Abstract (arXiv)

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.