December 9, 2024
@
12:00 PM
–
1:00 PM
Eye tracking research has shown that teammates in complex systems often exhibit overlapping eye gaze during critical moments of operation. This finding has driven the development of a new emerging technology called gaze sharing, which involves real-time visualization of team members’ eye movements on their respective displays. Initial evaluations of gaze sharing have primarily focused on simple task-specific domains, overlooking the nuanced interplay between the technology, teammates, and operational environments. In a comprehensive study, we assessed gaze sharing in Unmanned Aerial Vehicle (UAV) systems, recruiting 70 participants divided into 35 teams. Teams performed UAV tasks under three conditions: one with no gaze sharing and two with gaze sharing (fixation dot and trail). Our results showed that the fixation trail significantly reduced workload, enhanced situation awareness, and improved team performance by 10%, while the fixation dot led to a 9% reduction in team performance. Follow-up studies explored additional sociotechnical dimensions of gaze sharing, including its use as a complement or replacement for verbal communication, its effectiveness during interruptions, and the introduction of user-controlled on/off toggles to manage this technology, underscoring the importance of leveraging a sociotechnical systems approach. As other emerging technologies such as artificial intelligence (AI) systems continue to expand in complex systems, future work will explore the dynamics of teamwork in environments where AI functions as a collaborative agent in various workload conditions, addressing challenges such as the misuse of AI systems and the design of explainable AI to enhance transparency, trust, and accountability.
Bio: Jad is a PhD Candidate in the Department of Systems and Information Engineering at the University of Virginia (UVA). He was a Research Scientist II Intern at Amazon in the Workforce Planning Science Team. His research focuses on cognitive engineering and human-machine interaction, with an emphasis on understanding the attributes, requirements, and design guidelines for effective human-machine teams operating in complex systems. Jad’s research adopts eye tracking and a sociotechnical systems perspective to evaluate team dynamics with emerging technologies such as artificial intelligence, shifting the paradigm from single-user interactions to team-based collaborations. By identifying potential risks and mitigating harmful outcomes, Jad advances human-centered design principles and enhances machine intelligence to support safe, effective, and ethical team operations.