摘要
Abstract
[Objective]Smart water management has emerged as one of the core fields in securing water supply and optimizing water treatment by integrating big data,the internet of things,and intelligent control.As rapid development of artificial intelligence(AI)technology and its wide application in water engineering,developing smart water practical competencies for non-computer specialty students has become a key priority in the pedagogical reform of environmental engineering practical training.However,practical training of interdisciplinary undergraduate students in smart water management faces several significant challenges,including insufficient foundational programming skills among students,a mismatch between AI course content and the professional needs,and limited teaching resources and experimental platforms,which hinder the depth and breadth of practical learning.[Methods]This paper systematically addressed these challenges,specifically the issues of low motivation,high cognitive load,and the theory-practice gap.From the perspective of cognitive load and constructivist learning theories,a core teaching model based on the"perception-deconstruction-iteration"framework was proposed.[Results]Using the typical case of"water supply pipeline leakage location",this paper outlined the implementation approach of the model in terms of teaching objectives,content,and processes.The application value and potential for wider dissemination of the model were discussed from the perspectives of theoretical self-consistency,design precision,and transferability.[Conclusion]This paper provides a comprehensive solution that bridges the theoretical and practical aspects of implementing in the domain of water management education.关键词
智慧水务/跨学科教育/人工智能实践教学模式/感知-解构-迭代/案例示范Key words
smart water/interdisciplinary education/artificial intelligence/practice and teaching mode/perception-deconstruction-iteration/case demonstration分类
社会科学
