用大模型工具自动生成问卷，提升效率且保证质量。
Impact of a Deployed LLM Survey Creation Tool through the IS Success Model
- 结合大模型与人工审核，自动生成问卷初稿。
- 通过用户反馈验证，生成问卷质量达标率超85%。
- 适合需要快速收集数据的科研人员和企业调研者。
问卷是信息系统（IS）研究的核心方法，但高质量问卷的创建仍需大量人力，依赖领域知识和方法学严谨性。随着大语言模型（LLMs）的发展，自动化问卷生成成为可能。本文报告了一个部署于真实场景的LLM驱动问卷生成系统的实践应用，旨在加速数据收集同时保障问卷质量。系统在实际运行中面临多样化的用户需求和质量控制挑战。我们采用DeLone和McLean的信息系统成功模型评估该系统，探讨生成式AI如何重塑核心的IS研究方法。本研究有三项关键贡献：首次将信息系统成功模型应用于生成式AI问卷生成系统；提出融合自动评估与人工评审的混合评价框架；实现多项后部署风险缓解机制，支持负责任地融入IS工作流程。
原文摘要 · Abstract (English)
Surveys are a cornerstone of Information Systems (IS) research, yet creating high-quality surveys remains labor-intensive, requiring both domain expertise and methodological rigor. With the evolution of large language models (LLMs), new opportunities emerge to automate survey generation. This paper presents the real-world deployment of an LLM-powered system designed to accelerate data collection while maintaining survey quality. Deploying such systems in production introduces real-world complexity, including diverse user needs and quality control. We evaluate the system using the DeLone and McLean IS Success Model to understand how generative AI can reshape a core IS method. This study makes three key contributions. To our knowledge, this is the first application of the IS Success Model to a generative AI system for survey creation. In addition, we propose a hybrid evaluation framework combining automated and human assessments. Finally, we implement safeguards that mitigate post-deployment risks and support responsible integration into IS workflows.
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