Exploration of an AIGC-Driven Intelligent Course Design and Automated Teaching Resource Generation Model

With the rapid development of artificial intelligence-generated content (AIGC), intelligent course design and automated teaching resource generation have emerged as transformative advancements in educational technology. Traditional instructional design methodologies rely heavily on manual efforts, often suffering from inefficiencies, inconsistencies, and a lack of personalized adaptation to diverse learner needs. Furthermore, existing automated teaching models predominantly utilize static rule-based approaches, limiting their ability to dynamically adjust instructional content based on real-time learner feedback. To address these challenges, this study proposes a novel AIGC-driven intelligent course design framework, integrating reinforcement-based adaptive learning strategies and automated content generation techniques. The proposed model formalizes the teaching process as Cognitive-Adaptive Teaching Strategy (CATS), leveraging a reinforcement learning-based instructional policy to dynamically optimize teaching strategies. A probabilistic learner state estimation module is introduced, ensuring personalized adaptation by continuously updating the knowledge representation based on real-time performance metrics. By integrating structured knowledge graphs and domain-specific inference mechanisms, the model effectively enhances pedagogical flexibility while maintaining instructional coherence. Experimental results demonstrate that the proposed approach significantly improves learning efficiency, optimizes content adaptation, and fosters personalized educational experiences compared to conventional methodologies. This research contributes to the evolution of AI-driven education by establishing a robust, adaptive framework for intelligent course design and automated teaching resource generation.