erdEducation Reform and Development2652-53642652-5372Bio-Byword Scientific Publishing Pty. Ltd.10.26689/erd.v6i12.9384ArticleThe Practice of Large Language Models in Automated Question Generation: A Case Study of ChatGLM in High School Information Technology Curriculumhttps://artdesignp.com/journal/erd/6/12/10.26689/erd.v6i12.9384ChenYanxin,HeLing20256122025-01-15With the advancement of information technology, the field of education is undergoing transformation. In the teaching of high school information technology, scientific and efficient question formulation is crucial for enhancing the quality of education. Traditional methods of question formulation rely on the experience of teachers, leading to issues such as inconsistent difficulty levels and incomplete coverage of knowledge points. Large language models (LLMs), such as ChatGLM, leverage natural language processing and deep learning technologies to automatically generate questions that align with the curriculum, thereby improving scientific accuracy and precision, enhancing diversity and innovation, and catering to students’ personalized needs. Additionally, LLMs can enhance the efficiency of question formulation and reduce the burden on teachers. This paper explores the application value of large language models in the formulation of questions for high school information technology, through empirical research comparing the performance of human-generated and ChatGLM-generated questions in terms of accuracy, relevance, clarity, and willingness. The study selected two chapters, “Data and Information” and “Fundamentals of Algorithms,” and employed both human and ChatGLM-generated questions, inviting teachers to evaluate them. Through data analysis and statistical testing, we reveal the advantages and limitations of large language models in educational question formulation, providing insights for the intelligent development of educational assessment systems.

Song R, 2023, What Can Linguistics Contribute to the Improvement of Large-Scale Language Models? Language Strategy Research, 8(4): 53.

Chen Z, Lang W, An H, et al., 2024, A Proposition Intelligent Assistance System Based on Large Language Models. Telecommunications Express, 2024(4): 1–6.

Zhang C, Du L, Zhu X, et al., 2023, Research on Educational Question-Answering Systems Based on Large Language Models. Journal of Beijing University of Posts and Telecommunications (Social Sciences Edition), 25(6): 79–88.

He L, 2024, The Application of Large Language Models in Language Assessment. Foreign Language Teaching and Research, 2024(6): 10–22.

Wang C, 2024, A Paradigm Comparison Between Large Language Models and Generative Linguistics. Journal of Nanjing University of Science and Technology (Social Sciences Edition), 37(4): 61–69.

Yu H, 2010, On Model Reconstruction and Divergent Thinking from the Perspective of College Entrance Examination Proposition. Physics Bulletin, 2010(3): 4.

Xiang L, Li G, Li H, 2024, A Reliability Code Automatic Generation Method Based on Knowledge Graphs and GPT Models. Chinese Journal of Computational Mechanics, 41(2): 217–225.

Liu J, 2023, Exploring Guidance Methods in the Process of Creative Thinking and Design Thinking. National Common Language Teaching and Research, 2023(7): 1–3.

Yang M, Shi L, Su Z, et al., 2024, Research on User Personalized Demand Service Matching Model Based on AIGC. Packaging Engineering, 45(20): 109–119 + 182.

Lan H, 2020, Research on Personalized Cross-Language Query Expansion Based on User Interest Models. Information Systems Engineering, 2020(3): 3.

Xiao F, 2024, “Speech Acts” in Human-Machine Collaboration in the Era of Large Models. Jianghan Forum, 2024(10): 49–56.

Li F, Xiong Z, 2017, Core Literacy-Oriented Information Technology Curriculum: The “Data and Computing” Module. China Educational Technology, 2017(1): 32–37.

Li S, 2024, Reflections on the Reform of Electromagnetic Fields and Waves Teaching Assisted by the Large Language Model ChatGLM. Creative Education Studies, 12(5): 5.

Editorial Department, 2018, The Ministry of Education Issues the General High School Curriculum Plan and Curriculum Standards (2017 Edition). China Ethnic Education, 2018(2): 3.

Su Q, 2023, Analysis of Instructional Design Strategies for High School Information Technology Courses from the Perspective of Core Literacy. Computer Campus, 2023: 6957–6958.