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The rapid advancements in artificial intelligence (AI) have led to a growingtrend of human-AI teaming (HAT) in various fields. As machines continue toevolve from mere automation to a state of autonomy, they are increasinglyexhibiting unexpected behaviors and human-like cognitive/intelligentcapabilities, including situation awareness (SA). This shift has the potentialto enhance the performance of mixed human-AI teams over all-human teams,underscoring the need for a better understanding of the dynamic SA interactionsbetween humans and machines. To this end, we provide a review of leading SAtheoretical models and a new framework for SA in the HAT context based on thekey features and processes of HAT. The Agent Teaming Situation Awareness (ATSA)framework unifies human and AI behavior, and involves bidirectional, anddynamic interaction. The framework is based on the individual and team SAmodels and elaborates on the cognitive mechanisms for modeling HAT. Similarperceptual cycles are adopted for the individual (including both human and AI)and the whole team, which is tailored to the unique requirements of the HATcontext. ATSA emphasizes cohesive and effective HAT through structures andcomponents, including teaming understanding, teaming control, and the world, aswell as adhesive transactive part. We further propose several future researchdirections to expand on the distinctive contributions of ATSA and address thespecific and pressing next steps.

Agent Teaming Situation Awareness (ATSA): A Situation Awareness Framework for Human-AI Teaming