Knowledge-enhanced large language model based history subject exam question generation system - JI Tianyun; ZHANG Zheng; ZHAO Yuze; HUANG Zhenya; HUANG Wei; TONG Wei; LIU Qi; CHEN Enhong (大数据, 2025)
With the advent of large language models, their powerful language and reasoning abilities can mimic the question design methods of teachers, analyze the materials for questions, generate corresponding questions, and ensure the quality of the generated questions through self-checking. Inspired by this, a knowledge graph-enhanced large language model historical question generation system (KG-QGH) was designed, aiming to generate questions that meet teachers' needs, are content in accurate, aligned with the syllabus, and diverse in form. Taking short answer questions in the history subject as an example, the system automatically generated questions by integrating relational information from the historical knowledge graph with textbook knowledge points, a feedback mechanism was introduced to verify and correct the generated questions. The comparative experimental evaluations, was demonstrated that the KG-QGH system significantly outperformed the single large language model approach in key metrics such as correctness, relevance, and diversity of question generation. This study not only validated the effectiveness of the knowledge graph enhancement method in the field of question generation but also offered new research insights and practical references for intelligent applications in the educational domain.