Exploring the potential of LLM to enhance teaching plans through teaching simulation - Bihao Hu; Jiayi Zhu; Yiying Pei; Xiaoqing Gu (npj Science of Learning)
Abstract 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.