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.

翻译：由于教育不平等现象的存在，优质教案往往难以适配不同教育情境的需求。教师通常需要修改现有教案以适应新情境，然而当前工具多侧重于从零开始生成内容，反而增加了额外工作量。更关键的是，目前缺乏支持教师快速适应新型学情画像的工具。为填补这些空白，我们提出AdaPT系统——一种利用大语言模型支持跨区域与差异化教学中现有教案转换的系统。AdaPT具备交互式界面，支持教师输入学生画像，提供结构化教案表征与教案转换过程的解释说明，可自动适配教案内容至新情境，并支持教师参与的迭代式调优。通过包含9名教师的用户研究与3名专家的评估实验，结果表明AdaPT能够有效支撑教师工作流程，为促进教育公平提供了一条可行路径。