Abstract This article provides an in-depth introduction to the AI-assisted Chem Score pre-assessment system,specifically designed for chemical experiment teaching.Taking the kinetics experiment of esterification reaction of ethyl acetate in physical chemistry as an example,the system builds a database from historical data and integrates key feature parameters during the experimental process.It uses a machine learning algorithm based on random forests to precisely identify,automatically score,and visually display data during the experiment.The Chem Score system standardizes the scoring of experimental data,providing students with immediate feedback and personalized learning path planning.Additionally,the system's built-in experimental database and data analysis functionalities support visual presentation and in-depth analysis of data,providing scientific support for teaching improvements and showcasing the broad prospects of smart education in chemical experiment teaching.
WANG Jin-Long, CHEN Xiao-Yu, YAN Pan, LIN Jia-Hui, ZHAO Yan-Xi, BI Xue-Qin, GUO Yan-Bing. Research on the Application of Artificial Intelligence-Assisted Chem Score System in Chemistry Experimental Teaching[J]. Chinese Journal of Chemical Education, 2025, 46(16): 110-115.
