电话调查遇上对话式人工智能：大规模评估基于大语言模型的电话调查系统
Telephone Surveys Meet Conversational AI: Evaluating a LLM-Based Telephone Survey System at Scale
AI总结：
本研究提出并大规模评估了一个集成TTS、LLM和STT的人工智能电话调查系统，在美国和秘鲁的测试表明其数据质量在结构化项目上接近人工访员，并支持无需培训访员的快速部署。
摘要：
电话调查仍然是收集见解的重要工具，但通常需要在培训和协调人工访员方面投入大量资源。本研究提出了一个由人工智能驱动的电话调查系统，该系统集成了文本转语音（TTS）、大语言模型（LLM）和语音转文本（STT），能够在大规模场景下模拟人工主导访谈的灵活性（全双工对话）。我们在两个人群中测试了该系统：一项在美国进行的试点研究（n = 75）和一项在秘鲁进行的大规模部署（n = 2,739），通过基于网络的链接邀请参与者，并通过直接电话联系他们。该人工智能代理成功实施了开放式和封闭式问题，处理了基本的澄清请求，并动态导航分支逻辑，从而实现了快速的大规模调查部署，而无需招聘或培训访员。我们的研究结果表明，尽管人工智能系统在定性深度追问方面比人工访员更为有限，但在结构化项目上，整体数据质量接近人工主导的标准。本研究是首次在真实调查情境中成功大规模部署基于大语言模型的电话访员的研究之一。该人工智能驱动的电话调查系统有潜力在市场研究、社会科学和舆论研究中扩展可规模化且一致的数据收集，从而在保持研究所需数据质量的同时提高运营效率。
英文摘要：
Telephone surveys remain a valuable tool for gathering insights but typically require substantial resources in training and coordinating human interviewers. This work presents an AI-driven telephone survey system integrating text-to-speech (TTS), a large language model (LLM), and speech-to-text (STT) that mimics the versatility of human-led interviews (full-duplex dialogues) at scale. We tested the system across two populations, a pilot study in the United States (n = 75) and a large-scale deployment in Peru (n = 2,739), inviting participants via web-based links and contacting them via direct phone calls. The AI agent successfully administered open-ended and closed-ended questions, handled basic clarifications, and dynamically navigated branching logic, allowing fast large-scale survey deployment without interviewer recruitment or training. Our findings demonstrate that while the AI system's probing for qualitative depth was more limited than human interviewers, overall data quality approached human-led standards for structured items. This study represents one of the first successful large-scale deployments of an LLM-based telephone interviewer in a real-world survey context. The AI-powered telephone survey system has the potential for expanding scalable, consistent data collecting across market research, social science, and public opinion studies, thus improving operational efficiency while maintaining appropriate data quality for research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
