Knowledge Structure

Knowledge structure research investigates how knowledge is organized and represented, both in humans and artificial intelligence systems. Current research focuses on analyzing the internal knowledge structures of large language models (LLMs), often using graph-based representations and Bayesian networks to model relationships between knowledge components and assess learning pathways. This work is crucial for improving the explainability, reliability, and effectiveness of AI systems, particularly in applications like intelligent tutoring systems and scientific discovery, where understanding how AI "thinks" is paramount. Furthermore, comparing AI knowledge structures to human cognition offers insights into the nature of knowledge itself.