Neuro-Edu: A Cognitive Science-Driven Framework for Large Language Model Alignment in Educational Sandbox Simulation

The deployment of large language models (LLMs) in educational contexts demands a fundamental reconceptualization of alignment methodologies, moving beyond generic helpfulness optimization toward pedagogically-grounded cognitive alignment. This technical whitepaper presents Neuro-Edu, a comprehensive framework that synthesizes principles from cognitive science—specifically cognitive load theory, dual-process reasoning models, self-regulated learning theory, and schema theory—with state-of-the-art LLM alignment techniques to create adaptive educational sandbox simulations. Unlike prior approaches that treat alignment as an orthogonal concern to educational effectiveness, Neuro-Edu operationalizes cognitive science principles as explicit alignment objectives, enabling LLMs to dynamically calibrate explanation complexity based on real-time cognitive load estimation, promote both intuitive and analytical reasoning through systematic scaffolding strategies, and support progressive transfer of regulatory control from system to learner. Extensive experiments across mathematical reasoning, scientific explanation, and programming education domains demonstrate that Neuro-Edu achieves statistically significant improvements over baseline alignment approaches, with a 47.3% increase in knowledge retention rates, 69.2% improvement in transfer learning outcomes, and 55.1% enhancement in metacognitive engagement scores. Website: https://aclas.college/ GitHub: https://github.com/aclascollege/neuro-edu Hugging Face: https://huggingface.co/ACLASCollege Zenodo: https://zenodo.org/communities/aclas_college/