Selected work · Robotics, technical education, and curriculum design
Robotics appears in my background in more than one form: as something I taught, as a vehicle for student research and product development, and later as a subject for technical curriculum and certification design.
Those experiences matter to my broader portfolio because they show the same instructional problem from different sides. A student needs enough structure to experiment without being handed the answer. An industry-facing curriculum needs enough rigor to mean something beyond the classroom. In both cases, the design challenge is to connect concepts to decisions, performance, and evidence.
Teaching robotics through research and product development
In 2017, while working with EIC New Pathway in Shanghai, I led a bilingual robotics research and biomedical-sensor product-development course for students ages 12–18. The course culminated in a product launch rather than a conventional final paper or exam.
That changed the instructional sequence. Students had to move from a question or problem into research, technical design, iteration, communication, and presentation. The product-development frame made abstract concepts consequential: choices about sensors, design, feasibility, and explanation affected whether the final concept made sense as a whole.
The experience also required translating technical ideas across language, age, and prior-experience differences—an early version of a challenge I continue to work on in professional learning: preserving complexity while making the path through it usable.
Designing technical curriculum for certification
Later, through education and consulting work with Spark Growth / The Righting Desk, I helped coordinate the ROOTS Education online technical-certification program. My work included certification research, preparation of a Florida CAPE application, and design of an industry exam focused on collaborative robotics for applications in advanced manufacturing, aeronautics, and biomedical research.
Assessment design changed the question from “What should learners know?” to “What evidence would show that a learner can use this knowledge appropriately?” That requires defining performance expectations, mapping content to outcomes, distinguishing foundational knowledge from applied judgment, and building an assessment that measures the intended competency rather than test-taking skill alone.
What the two contexts had in common
The student course and the certification work were separated by audience and purpose, but they shared a design logic:
- Start with a meaningful technical problem or capability.
- Break it into teachable and assessable decisions.
- Give learners opportunities to apply the method rather than only describe it.
- Use outputs—prototypes, explanations, assessments, or performance evidence—to make understanding visible.
- Connect the learning to the context in which the skill will actually be used.
Why this belongs in a curriculum portfolio
My professional work is often described in terms of curriculum, research, data, and systems rather than “STEM education,” but robotics makes the underlying method easy to see. The content can be technical, the learner can be young or experienced, and the setting can be a classroom or a certification program. The design question remains: what does someone need to understand, practice, and demonstrate in order to use the skill responsibly?
That question now informs my work across research writing, analytics, professional learning, AI-responsive curriculum, and reusable learning tools.
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