Generative Artificial Intelligence Empowering the Reform and Practice of Pharmacology Experiment Teaching

Abstract

Traditional pharmacology experiment teaching suffers from insufficient student thinking training, lack of personalized guidance, and limited hands-on opportunities. This paper explores the reform practice of integrating generative artificial intelligence (GAI) into the entire process of experimental teaching. Based on the principle of "teacher-led, AI-assisted, student-centered", a "three-stage" teaching model is constructed: AI-assisted pre-class preparation and question generation, in-class human-machine dialogue for analyzing abnormal results, and post-class AI-collaborative data interpretation and report writing. A controlled experiment involving undergraduate clinical medicine students showed that the experimental group significantly outperformed the control group in theoretical test scores, depth of analysis in experimental reports, and ability to independently design validation protocols (P<0.05). Questionnaire surveys indicated that GAI effectively lowers the threshold for asking questions and promotes the development of critical thinking and scientific reasoning skills. Standardized usage strategies are proposed to address issues of information accuracy and over-reliance. The study demonstrates that generative artificial intelligence can serve as an effective thinking scaffold for pharmacology experiment teaching, and the resulting human-machine collaboration model may provide a reference for the reform of medical laboratory courses.