Automated survey collection with LLM-based conversational agents.

ABSTRACT:

Objectives

Phone surveys are crucial for collecting health data but are expensive, time-consuming, and difficult to scale. To overcome these limitations, we propose a survey collection approach powered by conversational Large Language Models (LLMs).

Materials and methods

Our framework leverages an LLM-powered conversational agent to conduct surveys and transcribe conversations, along with an LLM (GPT-4o) to extract responses from the transcripts. We evaluated the framework's performance by analyzing transcription errors, the accuracy of inferred survey responses, and participant experiences across 40 survey responses collected from a convenience sample of 8 individuals, each adopting the role of five LLM-generated personas.

Results

GPT-4o extracted responses to survey

SUBMITTER: Kaiyrbekov K 

PROVIDER: S-EPMC12574787 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature