Exploring an Artificial Intelligence – Driven Teaching Model for the Environmental Engineering Microbiology Course.

Artificial intelligence (AI) technology, with its powerful capabilities in data analysis, intelligent interaction, and personalized learning support, is creating new opportunities for the reform of education and teaching. Through the integration of online and offline blended teaching methods, this study utilizes a learning platform to analyze multi-source student learning data, assess knowledge mastery, and dynamically generate personalized learning paths. Abstract concepts are visualized via 3D modeling and dynamic simulation to enhance students' comprehension of microbiological knowledge. Knowledge mapping is employed to systematically organize course concepts and establish dynamic connections, aiding students in navigating complex and abstract knowledge structures. By leveraging an interactive learning platform, a multi-evaluation system incorporating dynamic assessment, teacher feedback, and student self-evaluation is established. This system evaluates learning outcomes through automated grading and intelligent analysis, while also delivering adaptive teaching resources tailored to individual student differences, so as to meet personalized learning need and stimulate students' interest and motivation. This study offers innovative insights for the curriculum reform of Environmental Engineering Microbiology in the context of emerging engineering education. [ABSTRACT FROM AUTHOR]