2026 ASEE Annual Conference & Exposition

This evidence-based practice full paper focuses on course design and evaluation for an undergraduate, project-oriented course on Wearables with AI Capabilities. The course follows a weekly format that begins with short lectures and transitions into team-based projects. It provides electrical and computer engineering students with a practical way to connect classroom concepts with hands-on design by developing health-focused wearable devices. These projects integrate sensors, microcontrollers with neural accelerators, and tiny machine learning tools while requiring students to evaluate trade-offs in power, computation, and communication across embedded, edge, and cloud systems.

From the educator’s perspective, the paper presents results from a structured faculty survey used as the main assessment method. The survey asked instructors who have taught the course to describe expected learning outcomes, what students learn most effectively in practice, how the course links to future careers or advanced study, remaining gaps in student preparation, and how instructor guidance balances with student independence. We also plan to capture student perspectives through an end-of-semester survey to complement the faculty feedback and evaluate perceived learning gains and course impact.

The outcomes include the ability to connect AI and embedded hardware, frame and evaluate solutions to sensing problems, understand sensor interconnects and data flows, gain broad exposure to the full pipeline, and build depth in one area, such as communication, sensor integration, or on-device AI. Instructors observe significant gains in practical system integration, microcontroller programming, debugging, teamwork, and a clearer understanding of the big picture from concept to prototype. Identified gaps include project management, product development topics such as enclosure and regulatory steps, stronger data analytics and validation beyond proof of concept, and professional documentation and reproducibility. Faculty emphasize the importance of balanced guidance, authentic projects, interdisciplinary teams, iterative feedback through meetings and peer reviews, and public demonstrations as key components of course success.

This submission addresses ECE Division topics related to curriculum design and assessment, project-based learning, and creative uses of technology in the ECE classroom, including artificial intelligence, machine learning, big data, and real-time analytics.