  The rapid advancements in artificial intelligence (AI) have led to a growing
trend of human-AI teaming (HAT) in various fields. As machines continue to
evolve from mere automation to a state of autonomy, they are increasingly
exhibiting unexpected behaviors and human-like cognitive/intelligent
capabilities, including situation awareness (SA). This shift has the potential
to enhance the performance of mixed human-AI teams over all-human teams,
underscoring the need for a better understanding of the dynamic SA interactions
between humans and machines. To this end, we provide a review of leading SA
theoretical models and a new framework for SA in the HAT context based on the
key features and processes of HAT. The Agent Teaming Situation Awareness (ATSA)
framework unifies human and AI behavior, and involves bidirectional, and
dynamic interaction. The framework is based on the individual and team SA
models and elaborates on the cognitive mechanisms for modeling HAT. Similar
perceptual cycles are adopted for the individual (including both human and AI)
and the whole team, which is tailored to the unique requirements of the HAT
context. ATSA emphasizes cohesive and effective HAT through structures and
components, including teaming understanding, teaming control, and the world, as
well as adhesive transactive part. We further propose several future research
directions to expand on the distinctive contributions of ATSA and address the
specific and pressing next steps.
