Development of Mental Models in Human-AI Collaboration: A Conceptual Framework

Artificial intelligence has become integral to organizational decision-making and while
research has explored many facets of this human-AI collaboration, the focus has mainly
been on designing the AI agent(s) and the way the collaboration is set up—generally
assuming a human decision-maker to be "fixed". However, it has largely been neglected
that decision-makers' mental models evolve through their continuous interaction with AI
systems. This paper addresses this gap by conceptualizing how the design of human-AI
collaboration influences the development of three complementary and interdependent
mental models necessary for this collaboration. We develop an integrated socio-technical
framework that identifies the mechanisms driving the mental model evolution: data
contextualization, reasoning transparency, and performance feedback. Our work
advances human-AI collaboration literature through three key contributions:
introducing three distinct mental models (domain, information processing,
complementarity-awareness); recognizing the dynamic nature of mental models; and
establishing mechanisms that guide the purposeful design of effective human-AI
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collaboration.