Personalized learning pathways with generative adversarial networks in intelligent education management

The increasing adoption of intelligent education management systems has created a demand for personalized learning pathways that cater to diverse student needs and learning styles. Traditional methods of personalizing education often rely on rule-based or static algorithms, which fail to adapt to dynamic student progress and evolving educational requirements. This paper introduces a novel framework using Generative Adversarial Networks (GANs) to design personalized learning pathways. By leveraging the generative capabilities of GANs, the proposed model creates adaptive learning strategies based on real-time student data, including performance metrics, behavioral patterns, and engagement levels. The framework consists of a generator that proposes potential learning pathways and a discriminator that evaluates these pathways against desired educational outcomes. Experimental results demonstrate the model’s ability to improve student engagement and learning outcomes, offering a scalable solution for intelligent education management. This research highlights the potential of GANs in advancing personalized education through adaptive, data-driven pathways.