Abstract for: Birds of a Feather: Clustering Mental Models to Explore How People Think Alike

Understanding the diversity of mental models that individuals hold regarding how the world works is important for fostering mutual comprehension, social learning, and collective decision-making. This paper presents a novel approach utilizing generative artificial intelligence (AI), network science, and clustering to analyze individuals' mental models represented in text. Two key outcomes were intended: 1) a network representation where individuals with similar thinking are proximately positioned, and 2) a collective mental model depiction of the system. We validated our approach with data from sixty-four undergraduate engineering students who articulated their views on the impacts of ChatGPT. By modeling the clustering results as a function of validation data including survey responses, psychographics, and demographics, we tested the efficacy of our approach. This research bears significance for scaling up the elicitation of mental models to enhance collective understanding of complex issues and promote social learning.