Hallucination-Free Causal Graph-Guided AI Framework for Intuitive Question and Answer Generation

Large language models have advanced automatic question generation, yet hallucinations continue to undermine correctness and instructional reliability. This paper introduces a unified framework that integrates causal-graph-guided chain-of-thought reasoning with a multi-agent hallucination-mitigation architecture to generate accurate and pedagogically sound question-answer pairs. Causal graphs provide structured domain knowledge, while specialized agents collaboratively detect and correct logical, factual, solvability, and computational errors through iterative refinement. A formal hallucination-scoring model guides optimization, enabling lightweight models to achieve high fidelity. Experiments on a large learning platform show up to a 90% reduction in hallucination and a 70% improvement in question quality over baseline systems, demonstrating a scalable foundation for trustworthy artificial intelligence-powered education.