Recently, there has been a national push to use machine learning (ML) and artificial intelligence (AI) to advance engineering techniques in all disciplines ranging from advanced fracture mechanics in materials science to soil and water quality testing in the civil and environmental engineering fields. Using AI, specifically machine learning, engineers can automate and decrease the processing or human labeling time while maintaining statistical repeatability via trained models and sensors. Edge Impulse has designed an open-source TinyML-enabled Arduino education tool kit for engineering disciplines. This paper discusses the various applications and approaches engineering educators have taken to utilize ML toolkits in the classroom. We provide in-depth implementation guides and associated learning outcomes focused on the Environmental Engineering Classroom. We discuss five specific examples of four standard Environmental Engineering courses for freshman and junior-level engineering. There are currently few programs in the nation that utilize machine learning toolkits to prepare the next generation of ML and AI-educated engineers for industry and academic careers. This paper will guide educators to design and implement ML/AI into engineering curricula (without a specific AI or ML focus within the course) using simple, cheap, and open-source tools and technological aid from an online platform in collaboration with Edge Impulse.

翻译：近期，美国全国范围内正推动运用机器学习（ML）与人工智能（AI）技术提升各工程学科的技术水平，涵盖从材料科学中的高级断裂力学到土木与环境工程领域的土壤与水质检测。借助AI（尤其是机器学习），工程师可通过训练模型与传感器实现流程自动化，减少人工处理或标注时间，同时保持统计学可重复性。Edge Impulse公司专为工程学科设计了一套基于开源TinyML技术的Arduino教育工具包。本文探讨了工程教育工作者在课堂中运用机器学习工具包的各种应用场景与教学方法，并提供以环境工程课堂为重点的深度实施指南及相关学习成效评估。我们具体讨论了面向大学新生与三年级工程学生的四门标准环境工程课程中的五个实例。目前全美仅有少数项目采用机器学习工具包来培养下一代具备ML与AI素养的工程人才，为其工业界与学术界职业发展做准备。本文旨在指导教育工作者借助简单、廉价的开源工具，以及Edge Impulse协作提供的在线平台技术支持，在工程课程中（无需专门开设AI或ML课程）设计与实施ML/AI教学。