Development of Mental Models in Human-AI Collaboration: A Conceptual Framework
It addresses a gap in human-AI collaboration literature by focusing on dynamic mental models, which is incremental as it builds on existing research but shifts focus from static human assumptions.
The paper tackles the neglected evolution of decision-makers' mental models in human-AI collaboration by developing a conceptual framework that identifies mechanisms like data contextualization and reasoning transparency to guide effective design.
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 collaboration.