Abstract
Persistent challenges in Environmental Microbiology teaching include the mismatch between networked knowledge structures and linear instruction, insufficient support for open-ended engineering scenarios, the gap between blended formats and deep learning outcomes, and weak process-oriented assessment evidence. Guided by Outcome-Based Education (OBE), the course team constructed a four-dimensional curriculum graph integrating ability, problem, knowledge, and resource dimensions, and developed a progressive teaching model of graph-guided learning, intelligent inquiry, and data-driven evaluation. This article reports the design and implementation of a teacher–student–AI collaborative blended smart course and presents a descriptive evaluation of one implementation cycle. The course was implemented over a 16-week semester with 60 third-year environmental engineering undergraduates at Jiangsu University; the previous cohort, taught by the same instructors with conventional blended teaching, served as a historical reference. Data were collected from platform interaction records, blueprint-matched final examination questions, and a piloted questionnaire (58 valid responses; Cronbach’s α = 0.89), and were analyzed with descriptive statistics, independent samples t-tests, and effect sizes; classroom interaction was summarized descriptively because repeated student session observations were clustered within students. Using the module on biological nitrogen and phosphorus removal in wastewater treatment as an example, the article illustrates differentiated pre-class preparation, in-class problem-chain inquiry with AI-assisted scheme design, and post-class learning profiling. Compared with the historical cohort, the implementation cohort showed a descriptively higher classroom interaction rate (90.3% vs. 70.5%), a 15.2-percentage-point higher mean score rate on examination questions targeting microbial regulation (95% CI [10.2, 20.2]; Cohen’s d = 1.12), and positive student perceptions (91.4% reported that knowledge graphs supported systematic cognition and 84.5% reported increased confidence in analyzing engineering problems). The documented course architecture and implementation pathway may provide a practical reference for the smart transformation of core environmental engineering courses.