Application of Large Language Models in Automated Question Generation: A Case Study on ChatGLM's Structured Questions for National Teacher Certification Exams
For educators and test developers, this work demonstrates the potential of LLMs in automated question generation but is incremental as it applies existing methods to a new domain.
This study tested ChatGLM's ability to generate structured questions for National Teacher Certification Exams, finding that the generated questions were highly similar to real exam questions in rationality, scientificity, and practicality, though further optimization is needed for multi-criteria generation.
This study delves into the application potential of the large language models (LLMs) ChatGLM in the automatic generation of structured questions for National Teacher Certification Exams (NTCE). Through meticulously designed prompt engineering, we guided ChatGLM to generate a series of simulated questions and conducted a comprehensive comparison with questions recollected from past examinees. To ensure the objectivity and professionalism of the evaluation, we invited experts in the field of education to assess these questions and their scoring criteria. The research results indicate that the questions generated by ChatGLM exhibit a high level of rationality, scientificity, and practicality similar to those of the real exam questions across most evaluation criteria, demonstrating the model's accuracy and reliability in question generation. Nevertheless, the study also reveals limitations in the model's consideration of various rating criteria when generating questions, suggesting the need for further optimization and adjustment. This research not only validates the application potential of ChatGLM in the field of educational assessment but also provides crucial empirical support for the development of more efficient and intelligent educational automated generation systems in the future.