Hybrid Panels: Toward Human-AI Collaboration in Survey Research

• URL: http://arxiv.org/abs/2608.22582v1
• Date: Sun, 23 Aug 2026 20:27:48 GMT
• Title: Hybrid Panels: Toward Human-AI Collaboration in Survey Research
• Abstract summary: Large-scale population surveys are essential for generating robust social and scientific insights.<n>Advances in artificial intelligence have opened up new opportunities for AI-supported survey infrastructures.<n>A promising AI-enabled survey infrastructure for which we build a first pilot is a hybrid panel.
• Score: 9.837591528122095
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

Related papers

• HybridQuestion: Human-AI Collaboration for Identifying High-Impact Research Questions [48.1029746371619]
  "AI Scientist" paradigm is transforming scientific research by automating key stages of the research process.<n>Key question remains unclear: can AI scientists identify meaningful research questions?<n>We propose a human-AI hybrid solution that integrates scalable data processing capabilities of AI with the value judgment of human experts.
  arXiv  Detail & Related papers  (2025-12-18T15:10:38Z)
• Prompts to Proxies: Emulating Human Preferences via a Compact LLM Ensemble [46.82793004650415]
  Large language models (LLMs) have demonstrated promise in emulating human-like responses across a range of tasks.<n>We propose a novel alignment framework that treats LLMs as agent proxies for human survey respondents.<n>We introduce P2P, a system that steers LLM agents toward representative behavioral patterns using structured prompt engineering, entropy-based sampling, and regression-based selection.
  arXiv  Detail & Related papers  (2025-09-14T15:08:45Z)
• Generative AI as a Safety Net for Survey Question Refinement [0.0]
  ChatGPT and other generative AI model platforms have been integrated into everyday life processes.<n>We have implemented a prompt experiment to systematically test what kind of feedback on survey questions an average ChatGPT user can expect.<n>Results from our zero-shot prompt experiment, which randomized the version of ChatGPT and the persona given to the model, shows that generative AI is a valuable tool today, even for an average AI user.
  arXiv  Detail & Related papers  (2025-09-10T15:52:10Z)
• Second FRCSyn-onGoing: Winning Solutions and Post-Challenge Analysis to Improve Face Recognition with Synthetic Data [104.30479583607918]
  2nd FRCSyn-onGoing challenge is based on the 2nd Face Recognition Challenge in the Era of Synthetic Data (FRCSyn), originally launched at CVPR 2024.<n>We focus on exploring the use of synthetic data both individually and in combination with real data to solve current challenges in face recognition.
  arXiv  Detail & Related papers  (2024-12-02T11:12:01Z)
• Increasing the Difficulty of Automatically Generated Questions via Reinforcement Learning with Synthetic Preference [11.31451429456241]
  This research contributes: (1) A methodology for increasing question difficulty using PPO and synthetic data; (2) Empirical evidence of the method's effectiveness, including human evaluation; and (3) An in-depth error analysis and study of emergent phenomena.
  arXiv  Detail & Related papers  (2024-10-10T18:21:00Z)
• Generative AI for Secure and Privacy-Preserving Mobile Crowdsensing [74.58071278710896]
  generative AI has attracted much attention from both academic and industrial fields. Secure and privacy-preserving mobile crowdsensing (SPPMCS) has been widely applied in data collection/ acquirement.
  arXiv  Detail & Related papers  (2024-05-17T04:00:58Z)
• Qsnail: A Questionnaire Dataset for Sequential Question Generation [76.616068047362]
  We present the first dataset specifically constructed for the questionnaire generation task, which comprises 13,168 human-written questionnaires. We conduct experiments on Qsnail, and the results reveal that retrieval models and traditional generative models do not fully align with the given research topic and intents. Despite enhancements through the chain-of-thought prompt and finetuning, questionnaires generated by language models still fall short of human-written questionnaires.
  arXiv  Detail & Related papers  (2024-02-22T04:14:10Z)
• UNK-VQA: A Dataset and a Probe into the Abstention Ability of Multi-modal Large Models [55.22048505787125]
  This paper contributes a comprehensive dataset, called UNK-VQA. We first augment the existing data via deliberate perturbations on either the image or question. We then extensively evaluate the zero- and few-shot performance of several emerging multi-modal large models.
  arXiv  Detail & Related papers  (2023-10-17T02:38:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.