2026 ASEE Annual Conference & Exposition

Just as previous generations have been responsible for teaching engineering students to use charts, calculators, and computers, we now have the responsibility to teach our students to leverage generative AI in their respective fields. The availability and power of the tools currently available substantially increases the breadth of engineering ability that today’s engineering students have access to. Toward providing this enhanced ability, in the context of a bioinstrumentation class we develop a laboratory experience to explore strategies for empowering students to effectively leverage genAI tools to *augment* rather than *replace* their knowledge and engineering skills. In this SHARE, we explore administering a lab in which we attempt to introduce students to (1) the epistemological aspects of interacting with genAI, (2) psychological, societal, and performance concerns to consider when working with AI, and (3) the skills needed to work with genAI in a productive workflow. Upon successful completion of this lab, students should achieve the following learning objectives: (i) evaluate AI-generated outputs for accuracy, (ii) utilize genAI for filter design, (iii) understand ethics of genAI-use in biomedical and engineering applications, (iv) apply engineering knowledge to implement test cases to validate results, and (v) articulate the role of genAI as a tool in the engineering design process. In this lab, students will act as biomedical engineers tasked with designing filters for several common biosignals. They will use genAI as a tool to generate filter circuit designs. Their role will be to provide the AI with precise engineering specifications, interpret its output, and critically evaluate the proposed designs for both accuracy and adequacy for their particular design needs. The collaborative genAI lab is composed of an introduction to biosignals, filters, and collaborative engineering with AI, a pre-lab review of relevant content, colLABorative filter design with AI, and an analysis and classroom discussion of the lab outcomes. This process should illustrate how AI is not a replacement for the erudite engineer, but a powerful collaborative tool. If successful, this SHARE may have broad usefulness across engineering disciplines, but we note that the access to and sanctioned use of generative AI tools varies across universities.