Visual Allocation of Teams in the Construction Industry: Shared Situation Awareness Under Information Overload in Human-AI Collaboration

Abstract: The integration of AI has significant o pportunities f or e nhancing human-machine collaboration, particularly in dynamic environments like the construction industry, where excessive information affects decision-making and coordination. This study investigates how visual attention distribution relates to SA development under information overload by addressing three research questions: (1) How does visual allocation relate to individual SA under information overload? (2) How does visual allocation influence s hared S A f ormation? ( 3) D o h igh-shared S A t eams exhibit different visual allocation patterns compared to low-shared SA teams? To answer these questions, a multi-sensor virtual reality (VR) construction environment is created as testbed that includes realistic task simulations involving both human teammates and AI-powered cobots (e.g., drones and robotic dog). Participants completed a pipe installation task when navigating construction hazards like falls, trips, and collisions, while experiencing varying degrees of information overload. Shared situation awareness (shared SA)—the shared understanding of tasks and environmental conditions—was assessed using the situation awareness global assessment technique (SAGAT) and eye movements were tracked using Meta Quest Pro. The relationship between eye-tracking metrics and SA/shared SA scores is analyzed using linear mixed-effects models (LMMs) and a two-sample t-test compared visual allocation patterns between high- and low-shared SA teams. Results indicate that eye tracking metrics can predict SA’s levels, an individual’s SA may also be enhanced through dyadic communication with team members, allowing participants to acquire updates without directly seeing the changes. Furthermore, high shared SA teams significantly allocated more attention to environment-related objects and exhibited a more balanced visual allocation pattern (run count and dwell time) on task- and environment-related objects. In contrast, low shared SA teams were more task-focused, potentially reducing their awareness of broader situational risks. These findings helps to identify at-risk workers using their psychophysiological responses. This research contributes to developing safer and more effective human-AI collaboration in construction and other high-risk industries by prioritizing shared SA and AI-driven personalized feedback.

Keywords: Shared situation awareness (shared SA), Construction safety, Hazard perception, Eye tracking, Human-AI teamwork (HAT), SAGAT

DOI: 10.54941/ahfe1006369

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