2026 ASEE Annual Conference & Exposition

This review systematically maps a decade of research (2015–2025) on the teaching, learning, and integration of Generative artificial intelligence (Gen AI) in engineering and construction management education, with particular attention to student-centered outcomes, curricular innovation, and professional preparation. The main objective of the paper is to provide a comprehensive and evidence-based synthesis of how Gen AI has been conceptualized, researched, and applied in educational contexts relevant to engineering and construction management students. By identifying patterns, gaps, and emerging opportunities, the review aims to guide educators, researchers, and curriculum designers in developing more responsive and effective educational practices in the era of rapid technological change.

The topical area of this work sits at the intersection of Gen AI adoption in higher education, engineering pedagogy, and construction management training in North America. While Gen AI has advanced rapidly in professional practice, shaping project design, construction planning, risk analysis, and decision-making, its translation into educational settings has been uneven and fragmented. Engineering and construction management programs are beginning to acknowledge Gen AI as a transformative force, yet the extent to which students are being prepared to engage with these technologies critically and effectively remains unclear. This review situates itself within that emerging conversation by synthesizing evidence on student learning experiences, curricular approaches, and institutional strategies related to Gen AI integration.

To address this objective, a literature search was conducted across multiple databases and outlets central to engineering and construction education research. Specifically, sources included the American Society of Engineering Education (ASEE) proceedings, American Society of Civil Engineers (ASCE) publications, the International Journal of Construction Education and Research (IJCER), Web of Science, and the Education Resources Information Center (ERIC via EBSCO). These outlets were selected for their visibility within the engineering and construction education community and their record of publishing research at the intersection of pedagogy and technical innovation. The dataset was organized and managed using Zotero, an open-source reference management software, while keyword analysis and bibliometric mapping were performed in VOSviewer. Through these tools, co-occurrence of terms, author collaborations, and institutional affiliations were analyzed to provide insights into the intellectual structure and evolution of the fields.

The relevance of this review to the engineering and construction educational community is clear and urgent. Preparing the next generation of engineers and construction managers to understand, apply, and critically evaluate Generative AI tools and platforms (e.g., ChatGPT, Claude ai, Copilot, etc.) is a pressing responsibility for educational instructors and institutions. However, without a consolidated understanding of what has already been studied, where the gaps lie, and how students are actually engaging with Gen AI in their learning environments, educators risk developing fragmented curricula that lag behind industry needs. By providing a structured overview of the literature, our research paper offers educators, program directors, and accrediting bodies an evidence base from which to design more coherent and impactful educational strategies.

Keywords: Gen AI, Engineering Education, Construction Management, Bibliometric Literature Review, Curriculum Integration