Core DRL Framework for Learning Paths

The proposed system integrates cutting-edge AI techniques to create a dynamic and responsive learning environment:

It utilizes Natural Language Processing (NLP) to analyze educational content, building dynamic knowledge graphs. Student data—from learning platforms (LMS) to physiological sensors (EEG, eye-tracking) and IoT devices on training equipment—is collected for comprehensive student ability portraits. A Deep Reinforcement Learning (DRL) algorithm, combining deep Q-networks and reinforcement learning feedback, continuously optimizes the learning path. This allows for real-time adjustments, such as recommending compensatory paths if a student's efficiency drops by 30%, balancing knowledge mastery, learning efficiency, and interest retention.

Quantifiable Gains in Learning Outcomes

Experimental results consistently demonstrate the DRL algorithm's superior performance across multiple key indicators when compared to traditional rule-based and collaborative filtering methods. Students exhibit higher final grades, increased satisfaction, and more efficient learning processes.

Transforming Vocational & General Education

The DRL-based personalized learning system offers a powerful solution to the challenges faced by modern education, particularly in vocational training where practical relevance and adaptability are crucial. It provides a robust framework for enhancing both student outcomes and engagement.

The findings pave the way for a more individualized, efficient, and engaging educational experience. By dynamically adjusting to each student's needs, AI can significantly bridge the gap between learning content and industry demands, fostering continuous improvement and higher student satisfaction.