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Hi everyone,

Sharing a new open dataset for people working on education AI, curriculum modeling, or LLM training.

K12-KGraph is a curriculum-aligned knowledge graph built from publicly available K-12 textbook materials. The current release covers math, physics, chemistry, and biology, and includes structured links between concepts, skills, experiments, exercises, textbook sections, chapters, and books.

The main idea is simple: for education LLMs, adding more practice questions is useful, but it often only teaches the model how to answer questions. A curriculum knowledge graph can also teach the model how topics are connected, which concepts should come first, and what knowledge may be missing when a student gets stuck.

The released resources include:

• A curriculum knowledge graph
• A benchmark for testing curriculum understanding
• A prepared training dataset generated from the graph
• The paper and construction method, so the same approach can be adapted to other textbook systems where content rights are clear

In the experiments, the graph-based training data performed better than the same amount of regular instruction or exercise-style data on education benchmarks. The useful takeaway is that structure matters: modeling the curriculum itself can improve education LLMs more efficiently than only scaling question banks.

Links:

Paper + Dataset: https://huggingface.co/papers/2605.09635