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01ARCS Lab — Research Area · Core Focus

Human-AI Teaming.

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.

Publications
36
Lab Authors
4

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.

Research focus

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

Methods

Like all ARCS Lab research, this area is mixed-methods: Quantitative Qualitative Computational

How we work
H1H2H3AI-1AI-2SHARED MENTAL MODELTRANSACTIVE MEMORYSA LINKFIG. 01
Fig. 01 · Team topology3 humans · 2 AI agents

01 · Selected Work

Representative publications

Publications

36 papers in Human-AI Teaming

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ARCS Lab member

People

Lab members in this area

ARCS Lab members who have authored publications in Human-AI Teaming.

Yayun Tian

Yayun Tian

PhD Student, Industrial & Systems Engineering