Integrating Generative AI in BIM Education: Insights from Classroom Implementation
This addresses the problem of integrating Generative AI into BIM education for students, but it is incremental as it builds on limited prior research in this area.
This study implemented a Generative AI-powered rule checking workflow in a graduate-level BIM course, finding that 55 students generally achieved learning objectives but faced challenges like debugging AI-generated code and inconsistent tool performance, which increased cognitive and emotional strain.
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.