发表机构
Technical University of Munich（慕尼黑工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出CARES对话式AI系统，将法规检索与多智能体对话结合，支持建筑安全教育中的日常安全报告生成与审查，初步评估显示响应忠实度0.74、相关性1.00，验证了技术可行性。
AI 中文摘要
建筑安全报告通常依赖人工日志和静态模板，这些方式提供的反馈有限，且使日常活动与相关法规脱节。本文介绍了CARES（增强安全性的对话式AI报告系统），一个将法规指导融入日常报告以支持建筑安全教育的对话式AI系统。CARES结合了主动式多智能体对话、检索增强生成（RAG）和自动化报告生成。该系统引导用户完成报告任务，使用混合检索检索相关法规段落，并将对话转换为结构化的日常报告。法规来源和不断演进的报告与对话并排显示，以支持用户审查和更正。一项涉及15名建筑管理学生的初步评估评估了检索质量、响应忠实度和对话相关性。CARES实现了0.74的总体忠实度得分和1.00的答案相关性得分，而初始检索排名仍有改进空间。这些结果为基于法规的对话式报告的技术可行性提供了初步证据。该研究强调了将法规知识融入日常文档记录的机会，未来工作需要评估对报告质量、安全意识和学习成果的影响。
英文摘要
Construction safety reporting often relies on manual logs and static templates that provide limited feedback and leave daily activities disconnected from relevant regulations. This paper introduces CARES (Conversational AI Reporting for Enhanced Safety), a conversational AI system that integrates regulatory guidance into daily reporting to support construction safety education. CARES combines proactive multi-agent dialogue, retrieval-augmented generation (RAG), and automated report generation. The system guides users through reporting tasks, retrieves relevant regulatory passages using hybrid retrieval, and converts conversations into structured daily reports. Regulatory sources and the evolving report are displayed alongside the dialogue to support user review and correction. A preliminary evaluation involving 15 construction management students assessed retrieval quality, response faithfulness, and conversational relevance. CARES achieved an overall faithfulness score of 0.74 and an answer relevance score of 1.00, while initial retrieval ranking remained an area for improvement. These results provide preliminary evidence of the technical feasibility of regulation-grounded conversational reporting. The study highlights opportunities to integrate regulatory knowledge into routine documentation, with future work needed to evaluate effects on report quality, safety awareness, and learning outcomes.
