This project promotes the progress of science by drawing on principles of human cognition and social behavior to develop artificial intelligence systems that are more interpretable, generalizable, and collaborative. Humans organize knowledge through structured concepts and hierarchies, reason through relational and logical patterns, and coordinate through effective social interactions. In contrast, current artificial intelligence systems often struggle with tasks requiring such structured, human-like understanding. This project uses graph structures as a unifying framework to bridge human cognition and artificial intelligence. It uses artificial intelligence models to uncover how knowledge is structured and processed in the brain and in learner interactions, and in turn uses principles of human cognition to guide the development of artificial intelligence systems with improved reasoning, interpretability, and collaboration. The project has potential benefits for neuroscience by providing computational tools to study cognitive organization and brain activity, for education by enabling personalized learning tools that map students' knowledge organization and support individualized feedback, and for artificial intelligence by advancing systems that are better aligned with the structural and collaborative principles that support human intelligence. To pursue this goal, the project is organized around three interconnected research tasks. First, the project uses artificial inte