Automatic Question Generation for Intuitive Learning Utilizing Causal Graph Guided Chain of Thought Reasoning

Intuitive learning plays a vital role in building deep conceptual understanding, particularly in STEM education, where students often grapple with abstract and interdependent ideas. Automatic question generation has emerged as an effective strategy to support personalized and adaptive learning. However, its effectiveness is limited by hallucinations in large language models (LLMs), which can produce factually incorrect, ambiguous, or pedagogically inconsistent questions. To address this challenge, we propose a novel framework that combines causal-graph-guided Chain-of-Thought (CoT) reasoning with a multi-agent LLM architecture to ensure the generation of accurate, meaningful, and curriculum-aligned questions. In this approach, causal graphs offer an explicit representation of domain knowledge, while CoT reasoning enables structured, step-by-step traversal through related concepts. Dedicated LLM agents handle specific tasks such as graph pathfinding, reasoning, validation, and output, all operating under domain constraints. A dual validation mechanism-at both the conceptual and output stages-substantially reduces hallucinations. Experimental results show up to a 70% improvement in quality over reference methods and yielded highly favorable outcomes in subjective evaluations.