# Tell Me About Yourself: Using an AI-Powered Chatbot to Conduct Conversational Surveys with Open-ended Questions

arXiv: https://arxiv.org/abs/1905.10700 · HTML: https://mlsanity.com/paper/1905.10700/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2019.

## Built on by (most cited first, 16 of 16)

- [Leveraging Large Language Models to Power Chatbots for Collecting User Self-Reported Data](https://mlsanity.com/paper/2301.05843/) (2023)
- [ChoiceMates: Supporting Unfamiliar Online Decision-Making with Multi-Agent Conversational Interactions](https://mlsanity.com/paper/2310.01331/) (2023)
- [Machine-assisted quantitizing designs: augmenting humanities and social sciences with artificial intelligence](https://mlsanity.com/paper/2309.14379/) (2023)
- [What should I Ask: A Knowledge-driven Approach for Follow-up Questions Generation in Conversational Surveys](https://mlsanity.com/paper/2205.10977/) (2022)
- [Comparing How a Chatbot References User Utterances from Previous Chatting Sessions: An Investigation of Users' Privacy Concerns and Perceptions](https://mlsanity.com/paper/2308.04879/) (2023)
- [Inform the uninformed: Improving Online Informed Consent Reading with an AI-Powered Chatbot](https://mlsanity.com/paper/2302.00832/) (2023)
- [On LLM Wizards: Identifying Large Language Models' Behaviors for Wizard of Oz Experiments](https://mlsanity.com/paper/2407.08067/) (2024)
- [Deconstructing Depression Stigma: Integrating AI-driven Data Collection and Analysis with Causal Knowledge Graphs](https://mlsanity.com/paper/2502.06075/) (2025)
- [Feedback by Design: Understanding and Overcoming User Feedback Barriers in Conversational Agents](https://mlsanity.com/paper/2602.01405/) (2026)
- [Cognitive Spillover in Human-AI Teams](https://mlsanity.com/paper/2407.17489/) (2024)
- [Interview AI-ssistant: Designing for Real-Time Human-AI Collaboration in Interview Preparation and Execution](https://mlsanity.com/paper/2504.13847/) (2025)
- [Talk2Care: Facilitating Asynchronous Patient-Provider Communication with Large-Language-Model](https://mlsanity.com/paper/2309.09357/) (2023)
- [Talking Surveys: How Photorealistic Embodied Conversational Agents Shape Response Quality, Engagement, and Satisfaction](https://mlsanity.com/paper/2508.02376/) (2025)
- [Interview-Informed Generative Agents for Product Discovery: A Validation Study](https://mlsanity.com/paper/2603.29890/) (2026)
- [Examining the Effectiveness of Chatbots in Gathering Family History Information in Comparison to the Standard In-Person Interview-Based Approach](https://mlsanity.com/paper/2309.03223/) (2023)
- [VizTrust: A Visual Analytics Tool for Capturing User Trust Dynamics in Human-AI Communication](https://mlsanity.com/paper/2503.07279/) (2025)

## Abstract (arXiv)

The rise of increasingly more powerful chatbots offers a new way to collect information through conversational surveys, where a chatbot asks open-ended questions, interprets a user's free-text responses, and probes answers whenever needed. To investigate the effectiveness and limitations of such a chatbot in conducting surveys, we conducted a field study involving about 600 participants. In this study with mostly open-ended questions, half of the participants took a typical online survey on Qualtrics and the other half interacted with an AI-powered chatbot to complete a conversational survey. Our detailed analysis of over 5200 free-text responses revealed that the chatbot drove a significantly higher level of participant engagement and elicited significantly better quality responses measured by Gricean Maxims in terms of their informativeness, relevance, specificity, and clarity. Based on our results, we discuss design implications for creating AI-powered chatbots to conduct effective surveys and beyond.