Hu Bihao, Zhu Jiayi, Pei Yiying, Gu Xiaoqing
School of Computer Science and Technology, East China Normal University, Shanghai, China.
Shanghai Institute of Artificial Intelligence for Education, East China Normal University, Shanghai, China.
NPJ Sci Learn. 2025 Feb 6;10(1):7. doi: 10.1038/s41539-025-00300-x.
The introduction of large language models (LLMs) may change future pedagogical practices. Current research mainly focuses on the use of LLMs to tutor students, while the exploration of LLMs' potential to assist teachers is limited. Taking high school mathematics as an example, we propose a method that utilizes LLMs to enhance the quality of teaching plans through guiding the LLM to simulate teacher-student interactions, generate teaching reflections, and subsequently direct the LLM to refine the teaching plan by integrating these teaching process and reflections. Human evaluation results show that this method significantly elevates the quality of the original teaching plans generated directly by LLM. The improved teaching plans are comparable to high-quality ones crafted by human teachers across various assessment dimensions and knowledge modules. This approach provides a pre-class rehearsal simulation and ideas for teaching plan refinement, offering practical evidence for the widespread application of LLMs in teaching preparation.
大语言模型（LLMs）的引入可能会改变未来的教学实践。当前的研究主要集中在使用大语言模型辅导学生，而对大语言模型协助教师潜力的探索有限。以高中数学为例，我们提出了一种方法，即通过引导大语言模型模拟师生互动、生成教学反思，随后指导大语言模型整合这些教学过程和反思来完善教学计划，从而利用大语言模型提高教学计划的质量。人工评估结果表明，该方法显著提升了大语言模型直接生成的原始教学计划的质量。改进后的教学计划在各个评估维度和知识模块上与人类教师精心设计的高质量教学计划相当。这种方法为教学计划的完善提供了课前预演模拟和思路，为大语言模型在教学准备中的广泛应用提供了实践依据。
