AI Empowers Iterative Classic Experiments: Construction of a New Model of “Ternary Collaboration” in Microbiology Experimental Teaching

The rapid development of artificial intelligence (AI) profoundly reshapes knowledge acquisition, presenting new challenges and opportunities for traditional microbiological experimental teaching. Although students can quickly access information through AI tools, this may weaken their critical thinking and error-analysis abilities. However, AI’s advantages in data analysis, simulation, etc., offer innovative teaching approaches. This paper constructs and explores a “teacher guidance—AI empowerment—student inquiry” collaborative mechanism to optimize undergraduate microbiological experimental teaching. Through specific scenarios, it elaborates on AI’s functional positioning in each experimental teaching link. It constructs an inquiry-based learning model by leveraging AI to optimize experimental schemes, preset errors, and handle other scenarios. Meanwhile, it analyzes the transformation of teachers’ roles with the assistance of AI and proposes in-depth teaching strategies to prevent superficial experimental cognition caused by AI. The new paradigm aims to transform AI from a mere knowledge-retrieval tool into a cognitive scaffold that promotes scientific thinking and enhances experimental skills. Ultimately, this aims to achieve a positive educational ecological cycle in which teachers provide intelligent guidance, AI offers precise assistance, and students engage in active inquiry.