EDU 1.0

Benchmark data and evaluation pipeline for measuring the professional educational competence of foundation models on teacher-certification examinations from three countries.

The released question set contains 10,013 questions — 8,868 selected-response and 1,145 constructed-response — drawn from the Chinese NTCE, the U.S. Praxis and the Indian KVS recruitment examinations.

Country	Selected response	Constructed response	Total
China (NTCE)	3,660	1,028	4,688
India (KVS)	3,421	0	3,421
U.S. (Praxis)	1,787	117	1,904
Total	8,868	1,145	10,013

Setup

Two helper libraries are kept in separate repositories and are not installable from PyPI. Clone them and put them on PYTHONPATH before running anything here:

git clone https://github.com/kleeeeea/llm_common
git clone https://github.com/kleeeeea/parse_evaluation
export PYTHONPATH="$PWD/llm_common:$PWD/parse_evaluation:$PYTHONPATH"

llm_common supplies the per-model API configuration used by inference and scoring (llm_common.llm_infer.api_info.dataclass_); parse_evaluation supplies the shared response-type constants used by the scoring rules.

Remaining dependencies are ordinary packages:

pip install openai pandas

Set the API credentials as environment variables — see Configuration before your first run:

export INNOSPARK_API_KEY=...

Layout

dataset/            released questions, one <dataset>.jsonl + <dataset>.csv per source
input_datasets/     dataset loading, model registry, token pricing
inference/          run models over the questions
scoring/            LLM-as-judge scoring of constructed responses
monitor/            progress inspection for long-running jobs
config.py           paths and API endpoints

dataset/

Twenty datasets, each shipped as a JSON Lines file and an equivalent CSV. Every record is one question; the field set varies by examination family, but id, type, question and answer are always present. Structured values (options, sub_questions, …) are objects in the JSONL and JSON-encoded strings in the CSV.

Group	Datasets
China	s1, s2, s3, interview, shijian
U.S.	praxis, barrons, dummies, cliffs_ss, cliffs0511, kaplan, kaplan2017, ppst_cliffs, learningexpress, mometrix, math0061, allen, plt_selected_response, evaluation_records_plt_constructed_response
India	india

Usage

Run inference for one or more models over one or more datasets:

python inference/run.py --models qwen3.5-397b --datasets s1 s2 --workers 8

--limit caps the number of questions per dataset (useful for a smoke test), --force re-runs questions that already have a stored answer, and --disable_thinking turns off extended reasoning for models that support it.

Score constructed responses with a single judge, or with every configured judge:

python scoring/run_scoring.py --model qwen3.5-397b --dataset s2 --judge Kimi-K25
python scoring/run_all_judges.py --model qwen3.5-397b --dataset s2 --parallel

Selected-response questions are graded by exact match and need no judge.

Watch a long run:

python monitor/cli_progress.py

Configuration

config.py holds API endpoints and dataset paths. Two things to check before running:

• Credentials. API_KEY reads from INNOSPARK_API_KEY, but EXTRACT_API_KEY is currently a literal string in the file. Replace it with an environment lookup and rotate the key before publishing or sharing this repository — a committed key must be treated as compromised.
• Paths. DATA_ROOT points at the raw examination archive, which is not part of this release; only DATASET_DIR (the dataset/ directory above) is needed to reproduce inference and scoring.

Notes on the released set

The questions here are the ones that actually enter the reported results. Items excluded upstream by the quality blacklist are not included, and neither are 79 fill-in-the-blank items that were answered by the models but never entered any reported aggregate. Counts in this README therefore match the paper exactly rather than the larger raw collection.