Exploration of Blended Teaching Mode for "Introduction to Catalytic Science" Empowered by Artificial Intelligence

Liang Xinxin

Zhengzhou University of Light Industry

Abstract：

Higher education is actively exploring a student-centered transformation toward blended teaching. Advances in Artificial Intelligence (AI) technology provide new support for personalized instruction, resource optimization, and learning outcome assessment. This study focuses on an AI-enabled blended teaching model for the course Introduction to Catalytic Science. Technologies such as intelligent teaching assistants, personalized learning-path recommendations, and intelligent assessment and feedback are employed to create a multidimensional teaching environment integrating virtual and physical settings. A data-driven intelligent teaching system is developed to establish a closed loop of “data collection–intelligent analysis–targeted intervention–outcome assessment.” Practical results demonstrate that this model significantly improves teaching effectiveness: the experimental group’s average score increased by 18.6% compared with that of the traditional teaching group; the pre-class preparation completion rate rose to 92%; innovative proposals for comprehensive projects increased by 41%; and 92.3% of students rated their satisfaction with the teaching model as good or above. The study also proposes recommendations for optimizing human–computer interaction, recommendation algorithms, and online–offline integration, while identifying future directions such as generative AI and interdisciplinary collaborative platforms.

Key Words：

Artificial Intelligence; blended teaching; introduction to catalytic science