Fine-Tuning Large Language Models for Educational Support: Leveraging Gagne's Nine Events of Instruction for Lesson Planning

Effective lesson planning is crucial in education process, serving as the cornerstone for high-quality teaching and the cultivation of a conducive learning atmosphere. This study investigates how large language models (LLMs) can enhance teacher preparation by incorporating them with Gagne's Nine Events of Instruction, especially in the field of mathematics education in compulsory education. It investigates two distinct methodologies: the development of Chain of Thought (CoT) prompts to direct LLMs in generating content that aligns with instructional events, and the application of fine-tuning approaches like Low-Rank Adaptation (LoRA) to enhance model performance. This research starts with creating a comprehensive dataset based on math curriculum standards and Gagne's instructional events. The first method involves crafting CoT-optimized prompts to generate detailed, logically coherent responses from LLMs, improving their ability to create educationally relevant content. The second method uses specialized datasets to fine-tune open-source models, enhancing their educational content generation and analysis capabilities. This study contributes to the evolving dialogue on the integration of AI in education, illustrating innovative strategies for leveraging LLMs to bolster teaching and learning processes.