COGENT: A Curriculum-oriented Framework for Generating Grade-appropriate Educational Content
This addresses the challenge of creating scalable, high-quality learning resources for educational contexts, though it appears incremental as it builds on existing generative AI methods with curriculum-specific adaptations.
The paper tackled the problem of generating educational content that aligns with curriculum standards and maintains grade-appropriate readability, particularly for STEM subjects, by proposing the COGENT framework, which resulted in passages comparable or superior to human references in evaluations.
While Generative AI has demonstrated strong potential and versatility in content generation, its application to educational contexts presents several challenges. Models often fail to align with curriculum standards and maintain grade-appropriate reading levels consistently. Furthermore, STEM education poses additional challenges in balancing scientific explanations with everyday language when introducing complex and abstract ideas and phenomena to younger students. In this work, we propose COGENT, a curriculum-oriented framework for generating grade-appropriate educational content. We incorporate three curriculum components (science concepts, core ideas, and learning objectives), control readability through length, vocabulary, and sentence complexity, and adopt a ``wonder-based'' approach to increase student engagement and interest. We conduct a multi-dimensional evaluation via both LLM-as-a-judge and human expert analysis. Experimental results show that COGENT consistently produces grade-appropriate passages that are comparable or superior to human references. Our work establishes a viable approach for scaling adaptive and high-quality learning resources.