Innovative Integration of Computer Network Technology in Modern Educational Systems

A novel educational system has been developed using the Radial Basis Function (RBF) algorithm to address the limitations of traditional classroom environments, which often rely on standardized material and fixed teaching methods. This research assesses recent advancements in interactive intelligent education systems by combining Artificial Intelligence (AI) with interactive teaching methods. A model based on cognitive functions is constructed using the RBF algorithm to personalize instructional approaches and foster a self-directed learning platform. The research methodology involves a comprehensive literature review, developing an Education Intelligent System (EIS) using AI-driven cognitive modelling, and the implementation of the RBF algorithm within a neural network architecture. The system's effectiveness is evaluated through empirical methods, including extensive data analysis and continuous refinement based on student performance feedback. Additionally, a network topology model is designed to enhance the system's adaptability for different roles within the educational framework. The results show significant improvements in instructional effectiveness, learner engagement, and personalized learning experiences. This research demonstrates that AI and interactive technologies can revolutionize conventional educational methods, enhance learner proficiency, and cultivate a more dynamic and engaging learning environment. Future work aims to improve the system's user interface and analyze larger datasets to refine the AI algorithms further.