The science of working together.

Human-AI teaming sits at the intersection of human factors, cognitive systems engineering, and AI. We study how humans and AI agents develop shared understanding, coordinate actions, and sustain effective collaboration — particularly in dynamic, high-stakes environments where mistakes are costly.

We examine team cognition constructs including shared mental models, transactive memory systems, and situational awareness within mixed human-AI teams, using both controlled laboratory experiments and real-world applied contexts.

• Shared mental model development and accuracy in human-AI teams
• Transactive memory systems and information distribution across mixed teams
• Team cognition under degraded communication and adversarial conditions
• Workload balancing and adaptive task allocation
• Situation awareness in electronically contested environments

Trust is the currency of effective human-AI collaboration. Our lab investigates how trust develops, breaks, and recovers in human-AI teams — with particular attention to the ethical dimensions of AI behavior and their downstream effects on team performance and decision-making.

• Trust calibration, repair, and spread in mixed human-AI teams
• The role of AI ethics and moral personas on trust and performance
• Context-dependent trust: how domain risk and team role shape reliance
• Designing transparent and explainable AI teammate behaviors
• Trust in ethically charged and adversarial decision environments

Effective human-AI teaming begins before deployment. We study how training environments, instructional design, and skill-transfer strategies prepare both humans and AI agents for collaborative performance in operational settings.

• Collaborative vs. independent training paradigms for human-AI teams
• Psychological fidelity in simulation-based training
• Skill transfer from training to operational settings
• Intelligent tutoring and adaptive training interventions
• Generative AI-powered simulation environments for future teaming

Maintaining shared situational awareness (SA) is critical when humans and AI operate together in dynamic, time-pressured environments. Our research examines how AI teammates can enhance — or undermine — the team's collective understanding of ongoing situations.

• Shared situation awareness frameworks for human-AI teams
• AI information-sharing strategies that improve collective SA
• Compromised AI detection through SA and mental-model accuracy
• Real-time adaptive interfaces for mission-critical environments
• Spatial information presentation in distributed multi-agent teams

We extend human-AI teaming research into physical collaborative systems — studying how humans interact with autonomous robots in manufacturing, logistics, and emergency-response environments.

• Human-robot teaming in advanced and flexible manufacturing
• Autonomous ground vehicles in logistics and contested environments
• Physical and cognitive trust calibration in embodied AI teammates
• Risk-based autonomy policies for collaborative robots
• Safety-critical handoffs between human and robotic teammates

Rigorous science requires rigorous methods. We develop and apply novel experimental frameworks to test the effectiveness, safety, and reliability of AI teammates in both controlled and naturalistic settings.

• Mixed-methods experimental designs for human-AI team research
• Behavioral and physiological measures of team performance
• Validated survey instruments for trust, SA, and team cognition
• Wizard-of-Oz and simulation platforms for AI teammate evaluation
• Longitudinal and within-subjects team dynamics studies