DOI: 10.66106/yanjai.20250103 ISSN: 3081-1449

生成式AI驱动的问题链教学设计：基于认知科学洞见重构智能课堂(Generative AI-Driven Problem Chain Teaching Design：Reconstructing Intelligent Classrooms Based on Cognitive Science Insights)

周波澜 Bolan Zhou
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Abstract: In the era of educational digital transformation, the integration of generative artificial intelligence (AI) and instructional design has become a pivotal approach to advancing smart education. This paper takes cognitive science as the theoretical cornerstone and explores the innovative path of problem chain instructional design empowered by generative AI, aiming to address the limitations of traditional problem chain design such as subjectivity, staticity and poor adaptability. By integrating cognitive science theories including cognitive load theory and the zone of proximal development theory with the technical advantages of generative AI in semantic understanding and logical reasoning, this study constructs a dynamic and personalized problem chain design system, and verifies its practical application effect through interdisciplinary research methods. The research results show that generative AI can effectively optimize the hierarchical structure and situational adaptability of problem chains, and the combination of generative AI and cognitive science can promote students' deep thinking and improve the teaching efficiency of smart classrooms. This study enriches the theoretical system of educational technology and cognitive science integration, and provides a feasible practical scheme for the in-depth reconstruction of student-centered smart classrooms.

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