用生成式AI辅助BIM课程，提升合规检查效率但需加强提示工程指导
Integrating Generative AI in BIM Education: Insights from Classroom Implementation
- 在BIM课中引入LLM进行设计合规性检查，结合提示工程教学
- 55名学生参与，虽达成学习目标但调试AI代码和工具不稳带来负担
- 适合想探索AI+建筑教育的师生，尤其需配套提示工程训练
本研究评估了一项生成式AI驱动的规则检查工作流在一所美国大学研究生级建筑信息模型（BIM）课程中的实施效果。两个学期共55名学生参与课堂试点，探索使用GenAI进行BIM合规任务。教学设计包括提示工程与AI驱动规则检查讲座，随后学生使用大语言模型（LLM）分析Autodesk Revit中的设计代码违规。通过NASA-TLX量表与回归分析，调查学生工作量、学习成效与整体体验。结果显示，学生普遍达成学习目标，但面临调试AI生成代码困难及工具性能不稳定问题，可能源于提示工程经验不足。这些挑战增加了认知与情绪负担，尤其对编程基础薄弱的学生更为明显。尽管如此，学生对未来生成式AI应用表现出强烈兴趣，尤其是有清晰教学支持时。
原文摘要 · Abstract (English)
This study evaluates the implementation of a Generative AI-powered rule checking workflow within a graduate-level Building Information Modeling (BIM) course at a U.S. university. Over two semesters, 55 students participated in a classroom-based pilot exploring the use of GenAI for BIM compliance tasks, an area with limited prior research. The instructional design included lectures on prompt engineering and AI-driven rule checking, followed by an assignment where students used a large language model (LLM) to identify code violations in designs using Autodesk Revit. Surveys and interviews were conducted to assess student workload, learning effectiveness, and overall experience, using the NASA-TLX scale and regression analysis. Findings indicate students generally achieved learning objectives but faced challenges such as difficulties debugging AI-generated code and inconsistent tool performance, probably due to their limited prompt engineering experience. These issues increased cognitive and emotional strain, especially among students with minimal programming backgrounds. Despite these challenges, students expressed strong interest in future GenAI applications, particularly with clear instructional support.
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