Scaffolding Middle-School Mathematics Curricula With Large Language Models

Despite well-designed curriculum materials, teachers often face challenges implementing them due to diverse classroom needs. This paper investigates whether large language models (LLMs) can support middle school math teachers by helping create high-quality curriculum scaffolds, which we define as the adaptations and supplements teachers employ to ensure all students can access and engage with the curriculum. Through cognitive task analysis with expert teachers, we identify a three-stage process for curriculum scaffolding: observation, strategy formulation and implementation. We incorporate these insights into three LLM approaches to create warmup tasks that activate students' background knowledge. The best-performing approach provides the model with the original curriculum materials and an expert-informed prompt; this approach generates warmups that are rated significantly higher than those created by expert teachers in terms of alignment to learning objectives, accessibility to students working below grade level and teacher preference. This research demonstrates the potential of LLMs to support teachers in creating effective scaffolds and provides a methodology for developing artificial intelligence-driven educational tools.