ACLASCollege/aclas-neuro-edu-demo

ACLAS Neuro-Edu Demo

A lightweight, zero-dependency demo of the Neuro-Edu Tiny-Cognition model — predict a student's absorption probability in three lines of Python. Optimized for rapid prototyping.

Quickstart

pip install numpy
python demo.py

from load_model import TinyCognition
model = TinyCognition("atc-v3.npz")
model.predict([0.6, 0.8, 0.7, 0.2, 0.5])  # focused student -> ~0.92
model.predict([0.9, 0.2, 0.3, 0.9, 0.1])  # struggling student -> ~0.29

Input features: [complexity, attention, skill_match, fatigue, prior_knowledge], each in [0, 1].

Files

• atc-v3.npz — trained weights (241 params)
• config.json — architecture config
• load_model.py — standalone NumPy loader
• demo.py — runnable demo with three student profiles

The full framework (multi-agent classroom simulation, evaluators, knowledge graph) lives in the GitHub repo; this demo ships only the tiny neural kernel.

Resources

• Core model: ACLASCollege/neuro-edu-core-v3
• GitHub: aclascollege/neuro-edu
• Live Demo: Interactive Neuro-Edu Dashboard

Citation

@techreport{aclas_neuro_edu_2026,
  title={Neuro-Edu: A Cognitive Science-Driven Framework for Large Language Model Alignment in Educational Sandbox Simulation},
  author={Atlanta College of Liberal Arts and Sciences},
  year={2026},
  institution={ACLAS College},
  doi={10.5281/zenodo.19743807},
  url={https://doi.org/10.5281/zenodo.19743807}
}