AI Telephone Surveying: Automating Quantitative Data Collection with an AI Interviewer
Danny D. Leybzon, Shreyas Tirumala, Nishant Jain, Summer Gillen, Michael Jackson, Cameron McPhee, Jennifer Schmidt

TL;DR
This paper presents an AI telephone surveying system using large language models, enabling more natural and adaptive interviews for quantitative data collection, and demonstrates its effectiveness through pilot studies.
Contribution
It introduces a novel AI system for telephone surveys that improves respondent experience and data quality compared to traditional IVR methods.
Findings
Higher survey completion rates with AI interviewer
Reduced break-off rates in pilot studies
Improved respondent satisfaction scores
Abstract
With the rise of voice-enabled artificial intelligence (AI) systems, quantitative survey researchers have access to a new data-collection mode: AI telephone surveying. By using AI to conduct phone interviews, researchers can scale quantitative studies while balancing the dual goals of human-like interactivity and methodological rigor. Unlike earlier efforts that used interactive voice response (IVR) technology to automate these surveys, voice AI enables a more natural and adaptive respondent experience as it is more robust to interruptions, corrections, and other idiosyncrasies of human speech. We built and tested an AI system to conduct quantitative surveys based on large language models (LLM), automatic speech recognition (ASR), and speech synthesis technologies. The system was specifically designed for quantitative research, and strictly adhered to research best practices like…
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Taxonomy
TopicsBig Data and Business Intelligence · Data Quality and Management · Privacy-Preserving Technologies in Data
