K12-KGraph: A Curriculum-Aligned Knowledge Graph for Benchmarking and Training Educational LLMs

Large language models (LLMs) are increasingly deployed in K–12 education, yet existing benchmarks such as C-Eval, CMMLU, GaokaoBench, and EduEval measure only whether a model can answer an exam question, i.e., factual recall. Effective educational AI further requires curriculum cognition: the structured understanding of how knowledge is organized and visually presented, including prerequisite chains, concept taxonomies, experiment–concept links, and pedagogical sequencing. Curriculum cognition is neither probed by current benchmarks nor explicitly taught by current instruction-tuning data. To close this gap, we introduce K12-KGraph, a curriculum-aligned knowledge graph extracted from the official People’s Education Press textbooks, covering mathematics, physics, chemistry, and biology across primary, middle, and high school, with nine node types (Book, Chapter, Section,Concept, Skill, Experiment, Exercise, Figure, VisualElement) and fourteen relation types spanning both curriculum structure and visual grounding.

Introduction. Large language models (LLMs) have become strikingly proficient at answering K–12 exam questions. On benchmarks such as C-Eval [9], CMMLU [14], GaokaoBench [33], and EduEval [21], frontier models now rival, and sometimes surpass, top human students, fueling rapid interest in LLM-powered tutoring and exam preparation [11]. Taken at face value, this progress suggests that educational AI is close to being solved. Yet anyone who has tried to build a real tutoring product knows that answering a question is only a small fraction of what a good teacher actually does, and it is precisely the larger fraction that today’s benchmarks leave untested. The untested part is what we call curriculum cognition: the structured understanding of why a topic must be learned before another, how a laboratory experiment connects to a theoretical concept, and where in the textbook each idea actually lives.

Discussion / Conclusion. We introduced K12-KGraph, a curriculum-aligned knowledge graph built from official Chinese K–12 textbooks, together with K12-Bench (for evaluating curriculum cognition) and K12-Train (for KG-guided SFT). Experiments show that (1) current LLMs lack robust curriculum cognition despite strong factual recall (46% EM for a strong open-source model on K12-Bench); (2) KG-guided synthesis is remarkably sample-efficient across both textual and multimodal settings: under a matched budget of approximately 2,300 samples, K12-Train-Text outperforms equally sized subsets of eight mainstream SFT corpora on GaokaoBench and EduEval, while K12-Train-Full outperforms substantially larger general-purpose datasets on three multimodal educational benchmarks; and (3) textual and visual supervision are complementary: K12-Train-Full consistently outperforms both K12-Train-Text and K12-Train-MM across the three multimodal educational benchmarks. Together, these results highlight the potential of curriculum-grounded data for building more capable educational language and vision-language models.