Hybrid Panels: Toward Human-AI Collaboration in Survey Research

2026-08-23

AI Summary

为解决大规模人口调查中的响应率低、成本高及数据延迟等问题，研究提出结合人类与大语言模型的混合面板方法，通过迭代优化提高数据质量。

Abstract

Large-scale population surveys are essential for generating robust social and scientific insights, yet they face significant challenges, including declining response rates, increasing data collection costs, long delays between data collection and data provision, and the risk of nonresponse bias. Advances in artificial intelligence (AI) have opened up new opportunities for AI-supported survey infrastructures where the goal is to overcome these challenges without limiting the data quality. A promising AI-enabled survey infrastructure for which we build a first pilot is a hybrid panel. A hybrid panel is a longitudinal AI-enabled survey which allows to iteratively improve the alignment between large language models (LLMs) and the population they aim to simulate and use the errors to inform the design and implementation of the next survey wave (e.g., inform the participant recruitment, assignment of questions to participants). It incorporates both human participants and LLMs as fundamental elements of its design. In this research note, we introduce the concept of a hybrid panel by providing a definition and outlining an overarching framework, spanning data collection to data validation. We detail results from a first pilot study to illustrate (open) challenges that we identify for hybrid panels.

Problem

• large-scale population surveys
• response rates
• data collection costs
• nonresponse bias
• artificial intelligence

Innovation

• Hybrid Panels
• Large Language Models
• Human-AI Collaboration
• Longitudinal Surveys
• Survey Infrastructure