Design and Application of Intelligent Assisted Learning System for Higher Education Students

Abstract

The Intelligent Assisted Learning System for Higher Education Students is an innovative educational platform designed to enhance the learning experience for college and university students. Leveraging artificial intelligence, machine learning, and data analytics, this system provides personalized learning pathways, adaptive tutoring, and real-time feedback to students based on their learning styles, preferences, and performance. By analyzing student interactions, engagement levels, and academic progress, the system dynamically adjusts course materials, assignments, and assessments to optimize learning outcomes. This paper explores the integration of Cooperative User Knowledge Modelling (CUKN) into Intelligent Assisted Learning Systems (IALS) tailored for higher education. Through a systematic investigation of CUKN's role within IALS, we elucidate its potential to enhance personalized learning experiences for students. By leveraging user data, collaborative filtering techniques, and adaptive learning strategies, CUKN empowers IALS to dynamically tailor content recommendations, learning paths, and collaborative tools to individual student needs, preferences, and proficiency levels. Simulation results suggest significant improvements in student engagement and performance, with metrics such as a 30% increase in learning efficacy and an 85% satisfaction rate with feedback mechanisms. While promising, challenges remain in refining algorithms and ensuring seamless integration into educational practices.