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Recently, there has been a national push to use machine learning (ML) andartificial intelligence (AI) to advance engineering techniques in alldisciplines ranging from advanced fracture mechanics in materials science tosoil and water quality testing in the civil and environmental engineeringfields. Using AI, specifically machine learning, engineers can automate anddecrease the processing or human labeling time while maintaining statisticalrepeatability via trained models and sensors. Edge Impulse has designed anopen-source TinyML-enabled Arduino education tool kit for engineeringdisciplines. This paper discusses the various applications and approachesengineering educators have taken to utilize ML toolkits in the classroom. Weprovide in-depth implementation guides and associated learning outcomes focusedon the Environmental Engineering Classroom. We discuss five specific examplesof four standard Environmental Engineering courses for freshman andjunior-level engineering. There are currently few programs in the nation thatutilize machine learning toolkits to prepare the next generation of ML andAI-educated engineers for industry and academic careers. This paper will guideeducators to design and implement ML/AI into engineering curricula (without aspecific AI or ML focus within the course) using simple, cheap, and open-sourcetools and technological aid from an online platform in collaboration with EdgeImpulse.

Utilizing Online and Open-Source Machine Learning Toolkits to Leverage the Future of Sustainable Engineering