The role of shared mental models in human-AI teams: a theoretical review

Mental models are knowledge structures employed by humans to describe, explain, and predict the world around them. Shared Mental Models (SMMs) occur in teams whose members have similar mental models of their task and of the team itself. Research on human teaming has linked SMM quality to improved team performance. Applied understanding of SMMs should lead to improvements in human-AI teaming. Yet, it remains unclear how the SMM construct may differ in teams of human and AI agents, how and under what conditions such SMMs form, and how they should be quantified. This paper presents a review of SMMs and the associated literature, including their definition, measurement, and relation to other concepts. A synthesized conceptual model is proposed for the application of SMM literature to the human-AI setting. Several areas of AI research are identified and reviewed that are highly relevant to SMMs in human-AI teaming but which have not been discussed via a common vernacular. A summary of design considerations to support future experiments regarding Human-AI SMMs is presented. We find that while current research has made significant progress, a lack of consistency in terms and of effective means for measuring Human-AI SMMs currently impedes realization of the concept.