COGENT: A Curriculum-oriented Framework for Generating Grade-appropriate Educational Content
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916790500589568 |
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| author | Liu, Zhengyuan Yin, Stella Xin Goh, Dion Hoe-Lian Chen, Nancy F. |
| author_facet | Liu, Zhengyuan Yin, Stella Xin Goh, Dion Hoe-Lian Chen, Nancy F. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_09367 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | COGENT: A Curriculum-oriented Framework for Generating Grade-appropriate Educational Content Liu, Zhengyuan Yin, Stella Xin Goh, Dion Hoe-Lian Chen, Nancy F. Computation and Language Artificial Intelligence 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. |
| title | COGENT: A Curriculum-oriented Framework for Generating Grade-appropriate Educational Content |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2506.09367 |