Development of a Generative AI-Based Platform for Construction Education by Integrating Retrieval-Augmented Generation

Document Type

Article

Publication Date

7-1-2026

Department

Department of Civil, Environmental, and Geospatial Engineering; Department of Psychology and Human Factors

Abstract

The digitalization of the construction industry brings new opportunities for improving productivity, coordination, and information exchange. However, this digital transformation has reshaped construction management knowledge into a more interdisciplinary and fragmented form, making it harder for educators to structure coherent, standards-aligned instruction. Recent advancements in generative artificial intelligence (AI) offer potential solutions by supporting adaptive, context-aware, and scalable learning environments. Grounded in constructivist learning theory (CLT), this study proposes and examines a pedagogical framework for construction management education that integrates retrieval-augmented generation (RAG) to enable verifiable, context-aware learning interactions. Unlike general-purpose AI tools, the framework confines retrieval to course materials and structures responses around reference-grounded guidance. Evaluation through expert review, automated scoring, and a formative student study produced evidence of educational and functional effectiveness, expert ratings averaged 4.5/5, automated metrics averaged 0.96/1, and the student study (n=7) confirmed usability and instructional value across relevance, accuracy, clarity, and scope alignment. These findings demonstrate that the framework supports transparent, evidence-based learning. Built on a constructivist foundation, it demonstrates how generative AI can be pedagogically applied to strengthen teaching and learning in construction management and advance the broader goals of engineering education and development.

Publication Title

Journal of Management in Engineering

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