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

• URL: http://arxiv.org/abs/2510.08104v1
• Date: Thu, 09 Oct 2025 11:40:41 GMT
• Title: Development of Mental Models in Human-AI Collaboration: A Conceptual Framework
• Authors: Joshua Holstein, Gerhard Satzger,
• Abstract summary: It has largely been neglected that decision-makers' mental models evolve through their continuous interaction with AI systems.<n>This paper addresses how the design of human-AI collaboration influences the development of three complementary mental models necessary for this collaboration.
• Score: 3.0116322614803726
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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