i AI Integration in LEED-Focused Construction Management Education: Curriculum Analysis, Technology Evaluation, and Stakeholder Perspectives

Abstract

ABSTRACT This thesis investigates the integration of artificial intelligence technologies into Leadership in Energy and Environmental Design (LEED)-focused construction management education at East Carolina University. Through a three-part investigation encompassing curriculum analysis, systematic literature review, and stakeholder survey research, the study examines current LEED education practices, evaluates AI application opportunities, and assesses faculty and student perspectives on technology-enhanced sustainable building education. The curriculum analysis revealed critical gaps in post-occupancy evaluation methods, real-time performance data integration, and human-centered design instruction within the existing residential sustainability course. A systematic review of approximately 50 peer-reviewed publications identified four primary AI application categories with demonstrated educational effectiveness: energy performance optimization tools, AI-enhanced Building Information Modeling, indoor environmental quality monitoring systems, and predictive analytics for building performance assessment, with documented learning outcome improvements of 25–45%. Survey results from seven faculty and 48 student respondents revealed strong faculty support for AI integration (86%) alongside significant student uncertainty (39% neutral), with both groups identifying infrastructure, professional development, and structured support as critical implementation requirements. Findings indicate that phased integration approaches aligned with stakeholder readiness, combined with robust support infrastructure, offer the most promising pathway for enhancing LEED education through AI technologies while maintaining educational integrity and preparing graduates for evolving industry demands.