Personalized education plan construction using neural networks

Abstract

In the paper, a personalized education planning system that utilizes neural networks and artificial intelligence to adapt learning paths for individual learners dynamically is presented. The system employs neural networks to analyze learner profiles, preferences, and real-time performance data, enabling the generation of tailored study plans. Neural networks are integral in predicting learner needs by analyzing past performance, learning style, and engagement patterns, allowing the system to recommend appropriate learning modules and optimal study schedules. Additionally, the system adjusts learning plans in real time, balancing cognitive load and ensuring personalized pacing to prevent learner fatigue. By incorporating these advanced mechanisms, the system provides content recommendations and schedules that evolve continuously as learners progress. The adaptive nature of the system is further enhanced through neural networks' ability to optimize long-term learning strategies, ensuring that the right balance between challenge and support is maintained. The proposed system can be seamlessly integrated with Learning Management Systems (LMS), offering a scalable solution for personalized education. The paper highlights the effectiveness of neural networks in creating efficient, learner-centered study paths and improving educational outcomes through data-driven adaptation.