Investigating the Impact of an Immersive VR-based Learning Environment for Learning Human-Robot Collaboration in Construction Robotics Education

This project aims to serve the national interest by advancing the innovation needed to enhance the work environments for construction workforce interacting with robots. As the construction industry gears towards widespread acceptance of robots, there is a need to prepare Construction Engineering and Management (CEM) students to excel in highly technological work environments. However, the current curriculum is inadequate to equip CEM students with the required competencies for working alongside construction robots. This project intends to investigate an immersive virtual reality (VR)-based learning environment to develop students' experiential skills so that the students would interact safely with robots in the construction industry after they graduate. The proposed project has the potential to provide the CEM communities with tangible benefits for advancing robotics in engineering education. This interdisciplinary research seeks to advance the understanding of CEM students' challenges when the students learn how to interact safely with robots during construction work. The project will first identify the key competencies required for CEM students to interact with construction robots. Next, the characteristics of a virtual environment that can enhance those competencies will be investigated. The immersive VR-based learning environment including an interactive diagnostic will be developed to help students to cultivate the hands-on skills of working with construction robots. The project intends to implement and evaluate the learning environment in terms of learning outcomes and improvement in competencies. In addition, the researchers plan to provide a learning environment adaptable to different student demographics. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.