AI-Assisted Conversational Interviewing: Effects on Data Quality and User Experience

• URL: http://arxiv.org/abs/2504.13908v1
• Date: Wed, 09 Apr 2025 13:58:07 GMT
• Title: AI-Assisted Conversational Interviewing: Effects on Data Quality and User Experience
• Authors: Soubhik Barari, Jarret Angbazo, Natalie Wang, Leah M. Christian, Elizabeth Dean, Zoe Slowinski, Brandon Sepulvado,
• Abstract summary: This study bridges the gap between standardized surveys and conversational interviews by introducing a framework for AI-assisted interviews.<n>We conducted a web survey experiment where 1,800 participants were randomly assigned to text-based conversational AI agents, or "textbots"<n>Our findings reveal the feasibility of using AI methods to enhance open-ended data collection in web surveys.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Standardized surveys scale efficiently but sacrifice depth, while conversational interviews improve response quality at the cost of scalability and consistency. This study bridges the gap between these methods by introducing a framework for AI-assisted conversational interviewing. To evaluate this framework, we conducted a web survey experiment where 1,800 participants were randomly assigned to text-based conversational AI agents, or "textbots", to dynamically probe respondents for elaboration and interactively code open-ended responses. We assessed textbot performance in terms of coding accuracy, response quality, and respondent experience. Our findings reveal that textbots perform moderately well in live coding even without survey-specific fine-tuning, despite slightly inflated false positive errors due to respondent acquiescence bias. Open-ended responses were more detailed and informative, but this came at a slight cost to respondent experience. Our findings highlight the feasibility of using AI methods to enhance open-ended data collection in web surveys.

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