大数据2025，Vol.11Issue(5)：48-66,19.DOI:10.11959/j.issn.2096-0271.2025059
基于知识增强大语言模型的历史学科试题生成系统
Knowledge-enhanced large language model based history subject exam question generation system
纪天昀 1张征 1赵宇泽 1黄振亚 1黄威 2佟威 2刘淇 1陈恩红1
作者信息
- 1. 中国科学技术大学认知智能全国重点实验室,安徽 合肥 230026
- 2. 教育部教育考试院,北京 100084
- 折叠
摘要
Abstract
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.关键词
大语言模型/知识图谱/题目生成/历史教育/智能教育Key words
large language model/knowledge graph/question generation/history education/intelligence education分类
社会科学引用本文复制引用
纪天昀,张征,赵宇泽,黄振亚,黄威,佟威,刘淇,陈恩红..基于知识增强大语言模型的历史学科试题生成系统[J].大数据,2025,11(5):48-66,19.
