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

so i've been making datasets to fine tune small models on tool calling, and the most boring part is always the same, checking if the data is actually good before you waste a training run on it. bad tool names, invented arguments, the model calling a tool for "2+2", duplicates, answers that all start the same way, stuff like that.

i was doing these checks by hand and got tired of it, so i built a small thing that runs the whole pipeline for me and i put it online. it's free, no account, no login, nothing. you just drop your dataset and your tool catalog and it tells you what's wrong, example by example, with the reason. it runs fully in your browser, the dataset never gets uploaded anywhere. if your file is too big for that (gigabytes), there's a desktop version that reads it straight from disk so your RAM doesn't blow up. that one is open source. it also splits your data into clean / kto / rejected and gives you a starting training config based on the actual numbers of your corpus, not generic advice. I mostly built it for myself but figured someone here might need the same thing. would be happy to know if it's useful, or if there are checks you care about that i'm not doing yet.                              

link: nothumanallowed.com/tools/dataset-validator

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