The fourth industrial revolution in the Architecture, Engineering, and Construction (AEC) Industry is transforming and enhancing conventional practices through the adoption of smart and autonomous systems fueled by advanced data processing and machine learning. Although construction management (CM) students are exposed to current fundamentals of construction technologies including BIM, students may potentially lack the fundamental knowledge and technological skills required for efficiently integrating, programming, and controlling robotics on construction sites. As such, it is critical to investigate CM students' skill gaps in order to prepare the graduating future workforces with the required advanced automation-based technologies. This study aims to investigate: (1) the preparedness of CM students in terms of their ability to understand machine learning techniques and work with smart technologies such as Robotics and Internet of Things (IoT); (2) the level of interest of CM students to understand and work with data mining techniques as well as autonomous technologies; and (3) factors that impacts inclination of construction management students towards understanding and developing skills in advanced construction technology. To achieve this, the authors conducted a questionnaire survey as well as interviews with undergraduate and graduate students in a Minority-Serving Institution. The obtained data is analyzed through ordered probit regression to determine variables influencing the students' interest in understanding and developing skills in advanced construction technology. The results of the study demonstrate the need to bridge the technological skill gaps of graduating STEM workforce to meet the transforming industry based on anticipated AEC workforce required qualifications. The findings of the study contribute to the construction workforce education/development and construction automation bodies of knowledge to ensure job security among STEM graduates in an era of frequently advancing and altering skill profiles, which in turn will support economic growth by producing new work skills without having to replace jobs.
