Improving Data-Sensing Technology Training in Construction Through Immersive Embodied Interaction

Abstract The construction industry increasingly relies on data-sensing technologies, such as drones and quadrupedal robots, for inspection, safety management, and decision-making, but effective training lags due to cost, limited expertise, and a lack of immersive, hands-on practice. Immersive embodied interaction (IEI) can increase effective training by coupling bodily movement to virtual actions, but its application in construction is underexplored. Grounded in embodied cognition theory, this study proposes a process for integrating IEI into data-sensing training and experimentally compared enhanced IEI with traditional virtual reality (VR). A total of 41 participants were assigned to 2 conditions: (1) enhanced embodied interaction using a programmed game controller, and (2) traditional VR interaction using standard controllers. Participants completed two VR-based construction tasks simulating real jobsites: a drone inspection and a quadrupedal robodog operation. Outcomes included task completion time, collision frequency, collision and recovery duration, and subjective ratings. Relative to traditional VR, enhanced IEI reduced completion time by 30% in the drone task ( p = 0.036 ) and 37% in the robodog task ( p < 0.001 ). In the drone task, collision frequency increased, but collision duration was significantly shorter ( p = 0.004 ), indicating faster recovery. In the robodog task, collision frequency was statistically unchanged ( p = 0.056 ), and recovery time decreased. Participants reported a higher sense of accomplishment with IEI. These findings suggest that IEI can improve the efficiency and intuitiveness of VR-based training for data-sensing technologies in construction, with potential benefits for learning, skill acquisition, and safety-related behaviors. The study advances knowledge by providing a framework for enhancing data-sensing education in the construction industry.