Integrating Generative AI in BIM Education: Insights from Classroom Implementation

2025-07-05

AI Summary

Building Information Modeling (BIM) education for graduate students often struggles with abstract regulatory compliance review and high practical barriers in rule-checking tasks. Method: This study systematically integrates large language models (LLMs) to support Revit-based code compliance checking, combining prompt engineering instruction with AI-assisted code generation, and evaluates cognitive load and learning outcomes using the NASA-TLX scale and regression analysis. Contribution/Results: Students achieved core learning objectives and exhibited significantly increased engagement; however, limited prompt engineering proficiency led to debugging difficulties and unstable tool outputs—particularly among those with weak programming foundations. The study proposes a novel generative AI–enhanced pedagogical framework for BIM regulatory compliance instruction, offering a reusable methodology and empirical evidence for AI-augmented design education.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

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.

Problem

• Evaluating Generative AI for BIM compliance tasks in education
• Assessing student challenges with AI-generated code debugging
• Exploring instructional support needs for GenAI in BIM courses

Innovation

• Generative AI-powered rule checking workflow
• Large language model for code violations
• Prompt engineering in BIM education