๐ค AI Summary
Existing lesson plans struggle to accommodate the diverse needs of students across different regions and lack effective tools to support teachers in rapid adaptation. This work proposes an interactive, large language modelโbased adaptive lesson planning system that, for the first time, enables automatic transformation of lesson plans across varied regional teaching contexts. By inputting student profiles, teachers receive structured, context-aware lesson plans accompanied by explanatory justifications and a human-AI collaborative iterative refinement mechanism. Evaluation through a user study with nine teachers and expert assessments from three domain specialists demonstrates that the system seamlessly integrates into teachersโ workflows, significantly enhancing the efficiency of lesson plan adaptation while offering an interpretable and actionable technical pathway toward greater educational equity.
๐ Abstract
Due to educational inequality, high-quality lesson plans often mismatch the needs of disparate educational contexts. Teachers typically modify existing lesson plans to fit new contexts, but current tools instead focus on generating content from scratch, creating additional workload. Moreover, a critical gap remains in supporting teachers to quickly adapt to new learning profiles. To bridge these gaps, we present AdaPT, a system leverages LLMs to support transformation of existing lesson plans for cross-regional and differentiated instruction. AdaPT features an interactive interface that allows teachers to input student profiles, offers structured lesson representation, provides explanations for lesson-plan transformations, automatically adapts lesson content for new contexts, and supports iterative, teacher-in-the-loop refinement. We evaluated AdaPT through a user study with 9 teachers and an expert evaluation with 3 specialists. Results show that AdaPT supports workflows of teachers and offers a promising pathway toward promoting educational equity.