Impact of Different Levels of Automation on Student Learning Outcomes in Construction Estimating

As artificial intelligence (AI) continues to transform the architecture, engineering, and construction (AEC) industry, integrating advanced technologies into academic curricula has become essential to meet the evolving industry demands. This study investigates how different levels of automation (LoAs), from manual methods to computer-assisted and AI-driven tools, impact students’ learning efficiency and performance in construction estimating. A total of 64 students from Florida International University and the University of Florida participated in this study, where students were tasked with quantity takeoff exercises using three methods: manual takeoff, on-screen takeoff (OST), and the AI-powered tool Togal.AI. An exit survey was also conducted to capture the demographic data, educational background, prior experience with estimating tools, as well as task-specific feedback on ease of use, accuracy, and confidence levels. Preliminary results indicate that AI-augmented learning shows promising potential in improved student engagement and learning outcomes in construction estimating education.