Innovative Application of Knowledge Map and Large AI Models in the Teaching of Transition State Theory in Physical Chemistry
ZHANG Xin1**, ZHANG Shi-Long1, FENG Hai-Song1, XU Xiang-Yu1, YU Le2, LIN Chang-Gen1, LI Le-Yuan1, CHEN Xing-Quan1
1. College of Chemistry,Beijing University of Chemical Technology,Beijing 100029,China; 2. College of Chemical Engineering,Beijing University of Chemical Technology,Beijing 100029,China
Abstract This study explores a teaching mode for physical chemistry that integrates knowledge map with AI large models,aiming to enhance students' understanding and application ability of transition state theory.The course adopts a comprehensive design of “pre-classb-in-class-post-class”.Before class,students use knowledge map and AI large models to preview fundamental concepts.During class,the alkaline hydrolysis of ethyl acetate is used as an example,where computational chemistry software simulates the reaction mechanism,vividly presenting the dynamic reaction process.The teacher explains the theory and guides the operations.After class,students reinforce their knowledge through summarization and training with knowledge map and AI large models.The initial teaching practice indicates that 90% of students can successfully complete the course content,though they face difficulties in software operation and interacting with AI large models.While this mode relies heavily on technology,it significantly improves students' understanding and application ability of transition state theory,stimulates learning interests and innovative thinking,and has the potential to serve as a mode for teaching in this field.
ZHANG Xin, ZHANG Shi-Long, FENG Hai-Song, XU Xiang-Yu, YU Le, LIN Chang-Gen, LI Le-Yuan, CHEN Xing-Quan. Innovative Application of Knowledge Map and Large AI Models in the Teaching of Transition State Theory in Physical Chemistry[J]. Chinese Journal of Chemical Education, 2025, 46(18): 42-48.
