A Tutorial-Generating Method for Autonomous Online Learning

Generative AI has become the focus of the intelligent education field, especially in the generation of personalized learning resources. Current learning resource generation methods recommend customized courses based on learning styles and interests, improving learning efficiency. However, these methods cannot generate personalized tutorials according to learners' preferences, nor can they adjust tutorial content as moods or levels of knowledge change. Therefore, this study develops an intelligent tutorial-generating system (Self-GT) for self-aid learning, integrating cognitive computing and generative learning to capture learners' dynamic preferences. The critical components of Self-GT are the tutorial-generating model based on cyclic deep reinforcement learning (RL) and the multi-modal knowledge graph containing complex relationships. Specifically, the proposed RL model dynamically explores learners' preferences from the temporal dimension, enabling RL agents to express learning behavior characteristics accurately and generate personalized tutorials. Then, relying on the internal self-developed education base and external internet sources, a multi-modal knowledge graph with multiple self-defined relationships is designed to enhance the precision of tutorial generation. Finally, the experimental results indicate that the Self-GT performs well in generating tutorials and has been successfully applied in the generating tutorial for "Hospital Network Architecture Planning and Design."