Developing a Nonlinear Learning Agent Model Driven by Generative Artificial Intelligence
Dan Zhao, Yuan Lin · 2025
This study is dedicated to exploring the application potential of Generative Artificial Intelligence (AIGC) in non-linear learning, aiming to construct innovative agent models that optimize learning experiences and adapt to the complexity and individual differences in learning. The theory of non-linear learning emphasizes dynamic essence and personalized needs, with AIGC technology providing technical support through its content generation and data analysis capabilities. In terms of model construction, this study designs three core learning paradigms: self-directed, hybrid, and collaborative learning, constructing a diversified non-linear learning ecosystem. The self-directed paradigm realizes personalized learning support, the hybrid paradigm promotes the integration of online and offline learning, and the collaborative paradigm enhances team collaboration and knowledge construction quality. In terms of model implementation, a reinforcement learning framework is adopted to construct a Markov Decision Process (MDP) model, introducing personalized dynamic learning paths. By improving the Q-Learning algorithm, intelligent learning paths are generated to achieve deep knowledge correlation learning.