Research on AI-driven personalized learning path planning and effectiveness under dual-system teaching mode
Year: 2025
Type: conference-paper
Abstract: With the rapid development of AI technology, personalized learning path planning is becoming an important research field. Dual system teaching models have great influence on the learning effect of students. Therefore, how to optimize the learning process of students through the personalized learning path is a great challenge. A Deep Reinforcement Learning (DRL) algorithm for personalized learning path planning is proposed. Based on deep Q-networks and reinforcement learning feedback mechanisms, the algorithm achieves adaptive optimization. The experimental results show that the algorithm based on the DRL is superior to the traditional method in learning performance, learning efficiency and satisfaction. Compared with traditional path planning algorithms based on rules and collaborative filtering, the proposed algorithm improves learning performance by 20%. Moreover, it has obvious advantages in learning motivation and efficiency. This paper provides a new way of thinking to intelligent education system, especially dual-system teaching model, and it is of great practical significance.
Author: Ling Chen
Institution: Taizhou Vocational and Technical College
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
OpenAlex ID: W4413985849