# COGENT: A Curriculum-oriented Framework for Generating Grade-appropriate Educational Content

arXiv: https://arxiv.org/abs/2506.09367 · HTML: https://mlsanity.com/paper/2506.09367/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 42 references that are in the map.
- First posted on arXiv 11 Jun 2025.

## Builds on (most cited first, 8 of 8)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Gemma 2: Improving Open Language Models at a Practical Size](https://mlsanity.com/paper/2408.00118/) (2024)
- [Evaluation of Text Generation: A Survey](https://mlsanity.com/paper/2006.14799/) (2020)
- [Large Language Models for Education: A Survey and Outlook](https://mlsanity.com/paper/2403.18105/) (2024)
- [Branch-Solve-Merge Improves Large Language Model Evaluation and Generation](https://mlsanity.com/paper/2310.15123/) (2023)
- [Generating and Evaluating Tests for K-12 Students with Language Model Simulations: A Case Study on Sentence Reading Efficiency](https://mlsanity.com/paper/2310.06837/) (2023)
- [Scaffolding Language Learning via Multi-modal Tutoring Systems with Pedagogical Instructions](https://mlsanity.com/paper/2404.03429/) (2024)
- [DnA-Eval: Enhancing Large Language Model Evaluation through Decomposition and Aggregation](https://mlsanity.com/paper/2405.15329/) (2024)

## Built on by (most cited first, 2 of 2)

- [The Imperfect Learner: Incorporating Developmental Trajectories in Memory-based Student Simulation](https://mlsanity.com/paper/2511.05903/) (2025)
- [PustakAI: Curriculum-Aligned and Interactive Textbooks Using Large Language Models](https://mlsanity.com/paper/2511.10002/) (2025)

## Abstract (arXiv)

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.