Design and Implementation of an Intelligent Question Generation System Based on Large Language Models

The intelligent question-generation system based on large Language models aims to address the issues of low efficiency and susceptibility to subjective factors in traditional manual question-setting, as well as the limited scale of existing question banks and potential copyright risks. This system integrates multi-turn dialogue mechanisms, Retrieval-Augmented Generation (RAG) technology, and the Agent framework to automatically generate questions based on teaching syllabi and knowledge points. The working principle of the system involves first constructing a knowledge base through an electronic resource parsing module. It then continuously optimizes the accuracy of the generated questions through multi-turn dialogue prompt learning. Meanwhile, the Agent generation module breaks down the question-setting process into extraction, generation, and evaluation stages, with specialized agents monitoring and optimizing each stage. Finally, the question evaluation module assesses the questions from multiple dimensions, such as difficulty and accuracy. After practical testing, the system generated 1000 questions for the “Wireless Communication” course, and a random inspection of 100 questions revealed an accuracy rate of 94%. Moreover, the generated questions cover different cognitive levels. This system can significantly improve the efficiency of teachers’ question-setting, provide personalized learning experiences for students, and inject new vitality into the digital transformation of education and the improvement of teaching quality.