用大模型替代人工面试官，实现大规模深度访谈。
AI Conversational Interviewing: Transforming Surveys with LLMs as Adaptive Interviewers
- 用大模型模拟对话式访谈，自动追问并理解回答。
- AI访谈生成的数据质量与人工相当，且可扩展性强。
- 适合需要大量深度反馈的社科研究或市场调研。
传统意见采集方法在深度与规模间存在权衡：结构化问卷便于大规模收集数据，但限制受访者自由表达；对话式访谈能获取深层见解，却成本高昂。本研究探索以大语言模型（LLMs）替代人类面试官，开展可扩展的对话式访谈。我们对大学生进行小规模深入实验，随机分配至由AI或人类面试官进行的政治议题访谈，双方使用相同问卷。通过定量与定性指标评估面试员遵循指南情况、回答质量、参与者投入度及整体访谈效果。结果表明，AI对话访谈在生成高质量数据方面具有可行性，表现接近传统方法，且具备显著可扩展优势。研究公开了数据与材料，提出具体实施建议。
原文摘要 · Abstract (English)
Traditional methods for eliciting people's opinions face a trade-off between depth and scale: structured surveys enable large-scale data collection but limit respondents' ability to voice their opinions in their own words, while conversational interviews provide deeper insights but are resource-intensive. This study explores the potential of replacing human interviewers with large language models (LLMs) to conduct scalable conversational interviews. Our goal is to assess the performance of AI Conversational Interviewing and to identify opportunities for improvement in a controlled environment. We conducted a small-scale, in-depth study with university students who were randomly assigned to a conversational interview by either AI or human interviewers, both employing identical questionnaires on political topics. Various quantitative and qualitative measures assessed interviewer adherence to guidelines, response quality, participant engagement, and overall interview efficacy. The findings indicate the viability of AI Conversational Interviewing in producing quality data comparable to traditional methods, with the added benefit of scalability. We publish our data and materials for re-use and present specific recommendations for effective implementation.
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