K12-KGraph: A Curriculum-Aligned Knowledge Graph for Benchmarking and Training Educational LLMs
Hao Liang, Qihan Lin, Zhaoyang Han, Xiaochen Ma, Zhen Hao Wong, Meiyi Qiang, Linzhuang Sun, Wentao Zhang

TL;DR
K12-KGraph introduces a curriculum-aligned knowledge graph from textbooks to improve educational AI, providing benchmarks and training data for curriculum cognition in K-12 subjects.
Contribution
The paper presents a novel curriculum-aligned knowledge graph, benchmark, and training corpus derived from textbooks, enhancing curriculum understanding in educational large language models.
Findings
Existing models show limited curriculum understanding, with Gemini-3-Flash at 57% exact match.
K12-Train outperforms other instruction-tuning datasets on GaokaoBench and EduEval.
Curriculum-structured supervision is highly sample-efficient for educational LLM tuning.
Abstract
Large language models (LLMs) are increasingly used in K-12 education, yet existing benchmarks such as C-Eval, CMMLU, GaokaoBench, and EduEval mainly evaluate factual recall through exam-style question answering. Effective educational AI additionally requires curriculum cognition: understanding how knowledge is structured through prerequisite chains, concept taxonomies, experiment-concept links, and pedagogical sequencing. To address this gap, we introduce K12-KGraph, a curriculum-aligned knowledge graph extracted from official People's Education Press textbooks across mathematics, physics, chemistry, and biology from primary to high school. The graph contains seven node types (Concept, Skill, Experiment, Exercise, Section, Chapter, Book) and nine relation types covering taxonomy, prerequisite, association, verification, assessment, location, and order. Based on this graph, we construct…
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