Research on Algorithm Design of Intelligent Teaching System Based on Students' Cognitive Models

The intelligent transformation of educational technology faces the challenge that traditional teaching is difficult to dynamically adapt to individual cognitive differences. The current mainstream intelligent teaching system generally relies on static knowledge mapping and shallow behavioral analysis, and lacks in-depth modeling capability for the evolution of students' cognitive state. With the development of multimodal sensing devices and cognitive computing theory, it is possible to construct dynamic cognitive models that integrate physiological, behavioral and psychological data. In this study, we explore the dynamic knowledge tracking method based on cross-modal time-series characterization and design a strategy optimization mechanism with knowledge constraints, aiming at the development of an intelligent teaching system with a closed loop of cognitive perception and decision-making, and providing theoretical foundations and technical support for the digital transformation of education.