From Diagnosis to Generalization: A Cognitive Approach to Data Selection for Educational LLMs
Yuxiang Guo, Yan Zhuang, Qi Liu, Zhenya Huang, Xianquan Wang, Liyang He, Jiatong Li, Rui Li
Abstract
Specializing Large Language Models for educational domains is a key frontier in creating personalized learning tools. The central challenge is not data scarcity but its abundance: efficiently selecting a curated data subset from vast corpora to enhance specialized skills and foster generalization, without degrading existing abilities. Existing data selection paradigms, relying on superficial semantic similarity or model training dynamics, often lack a principled framework to identify data that promotes true cognitive growth. Our work proposes a paradigm shift from leveraging indirect proxies of learning value, such as semantic similarity and training dynamics, towards a framework that performs a direct, cognitive-level modeling of the learner
BibTeX
@inproceedings{aaai2026_fromdiagnosistog,
title = {From Diagnosis to Generalization: A Cognitive Approach to Data Selection for Educational LLMs},
author = {Yuxiang Guo and Yan Zhuang and Qi Liu and Zhenya Huang and Xianquan Wang and Liyang He and Jiatong Li and Rui Li and Shijin Wang},
booktitle = {AAAI 2026},
year = {2026}
}