  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.
