Circuits, code, and independent builds
Arduino setup, kit labs, serial debugging, daily reflections, and an approved Arduino-based project.
FOR EDUCATORS
Take the ideas.
Build your version.
Use a single lab, borrow the engineering routines, or follow the broader course arc. Start with what your students know and the equipment you already have.
A practical place to start
- Choose the learning goal.
Start with one input, one output, and a behavior students can explain. Add machine learning when the task calls for classification or recognition.
- Check the actual hardware.
Confirm board versions, voltage requirements, sensors, libraries, and available compute. The class adapted reference-kit instructions to its own expansion boards.
- Make thinking visible.
Use a daily notebook, a working demonstration, and a short explanation of the code or circuit. Keep a record of failures and revisions.
- Build toward independence.
Move from a guided lab to an open-ended build, then a capstone proposal, prototype milestones, testing, and presentation.
FROM THE DAILY AGENDAS
A year on the workbench.
This sequence is drawn from the 2025–2026 pages of the course landing-page PDF. Older 2022–2023 pages remain part of the earlier course archive. The agendas document the classroom rhythm; they are not an exhaustive record of every lab.
Fabrication, Raspberry Pi, and displays
Soldering and project work lead into Raspberry Pi software and LED-matrix activities. December agendas include sensor readings displayed on the matrix.
Learning with JetBot
Robot setup, basic motion, teleoperation, image collection, Colab model training, collision avoidance, and line/road following.
TinyML onboarding
XIAO ESP32S3 Sense checks for blink, microphone, camera, and Wi-Fi; SenseCraft tools and adaptation to the available expansion board.
Capstone development
Brainstorming, proposals and presentations, team formation, repeated prototype milestones, final testing, and demonstrations.
Additional curriculum strands confirmed by the instructor include room-condition classification, audio/keyword recognition, IMU-based motion classification, and edge language/vision-language models. These are described in the expanded syllabus; not every activity appears in the daily agendas.
FOUR HABITS THAT TRAVEL
Teach the engineering,
not just the device.
Ask students to explain the complete system
What are the inputs? What does the program or model do with them? What changes in the physical world? Where could the result be wrong?
Use evidence to improve a prototype
Record test conditions, observed behavior, and changes. For an AI system, test examples beyond the ones used to train it. For a mechanism, test repeatability.
Make time for iteration and communication
The course uses daily reflections and repeated milestones. The final narrative explains design changes, technical decisions, results, and future improvements.
Keep building safe and responsible
Match power and logic levels to the equipment; disconnect power before rewiring. Supervise soldering and moving mechanisms. Collect only the image, audio, or sensor data needed for the task, with appropriate permission.
REUSE & CREDITS
Made to be adapted.
You may download and adapt David McLoda’s original teaching materials with attribution. Please credit the original author, link to this site, and identify your changes. This permission does not extend to third-party publications, software, or student work.
Adapted from AI & Mechatronics (AIM), David McLoda, School of Science and Engineering, Dallas ISD. Source: robotics.mcloda.com. Changes made by the adapting educator.
Student project summaries are anonymous. Original narratives and identifying photographs are not distributed here. Course equipment and software names belong to their respective owners; references do not imply endorsement.