Connecting Feedback to Choice: Understanding Educator Preferences in GenAI vs. Human-Created Lesson Plans in K-12 Education - A Comparative Analysis

This study compares lesson plans created by human curriculum designers versus two GenAI models (fine-tuned LLaMA-2-13b and customized GPT-4) in K-12 mathematics education, evaluating educator preferences across instructional components (warm-up, main task, cool-down) and grade levels. The mixed-methods analysis examines both quantitative preferences and qualitative feedback to identify strengths and limitations of AI-generated lesson plans compared to human-authored ones.

As generative AI (GenAI) models are increasingly explored for educational applications, understanding educator preferences for AI-generated lesson plans is critical for their effective integration into K-12 instruction. This exploratory study compares lesson plans authored by human curriculum designers, a fine-tuned LLaMA-2-13b model trained on K-12 content, and a customized GPT-4 model to evaluate their pedagogical quality across multiple instructional measures: warm-up activities, main tasks,

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