How can we design and build an AI-powered solution to address a real-world energy problem?
Vibe coding is how today’s energy professionals use AI to build real tools fast. Students describe an energy problem and what they want their solution to do — AI writes the code. Students test it, refine it, and iterate until it works. The focus stays on energy knowledge and critical thinking, not programming syntax.
Students use AI to design and build real energy applications — no prior coding experience required. This unit connects career-technical energy curriculum to the AI tools reshaping the industry, giving students hands-on experience with the kind of problem-solving today’s energy workforce demands.
A 10–12 day unit built for high school CTE and community and technical college energy programs. Includes instructor guide, student handouts, daily lesson plans, and answer keys — aligned to US DOE Energy Literacy Standards and NGSS.
This module includes two key resources to get started: the Energy Problem Bank, which helps students define the real-world problem they’ll solve, and a full Unit Overview, outlining the scope and flow of the entire lesson. By the end of the unit, students will have designed, built, and tested a functional tool that addresses an authentic energy challenge, while building the AI literacy and technical confidence today’s workforce demands.
Today, you’re not a student — you’re a problem solver. Before writing a single line of code, you need to know what you’re actually trying to fix. This module challenges you to figure out what makes something an energy problem rather than just an energy topic — the kind that affects real people, systems, and communities, and deserves a real solution. You’ll explore energy problems across four dimensions and walk away with 1–2 problem ideas you’re ready to investigate further.
Strong solutions begin with strong understanding. Before any good engineer designs something, they dig deep — who’s affected, how it works, what’s already been tried, and why it’s still unsolved. Today, your job is to become the expert on your specific energy problem — not energy in general, your problem. What do you need to understand about it before you can design a solution? Using NotebookLM, you’ll research your problem, gather at least 4 pieces of evidence, and synthesize your findings in your own words — building toward being able to explain your problem to someone who’s never heard of it.
A problem statement like “people don’t have enough energy” sounds important, but it’s too big to design anything against — it’s not a problem, it’s a category. Today, you’ll zero in on the problem until it’s specific enough that we could actually build something for it. Students take what they’ve learned and sharpen their research into a clear, specific, research-grounded problem statement — one narrow enough that a real tool, product, or process could address it. From there, they define at least three criteria their solution needs to meet and three constraints it has to work within, setting the guardrails that will shape every design decision from here on out. By the end of the module, every student has a problem worth solving and the criteria to know if they’ve solved it.
Good solutions are designed, not guessed. Before you write a single prompt to an AI, you need to know exactly what goes into your energy solution, what it does with that input, and what comes out the other end — if you can’t describe that clearly, you don’t actually understand your solution yet. In this module, students think in systems, mapping their proposed solution as a complete Inputs → Process → Outputs model: at least three inputs, four process steps, two outputs, and one point where the whole thing could fail. That failure point matters as much as the rest — knowing where your system is fragile is what separates a real design from a guess. By the end of the module, every student can walk someone through exactly how their solution works, step by step, before they’ve written a line of code.
If you cannot explain it clearly, it is not ready to build. Students take their energy solution from a systems model to something they can actually see: a detailed, screen-by-screen wireframe with every input, button, and output labeled. This is where “think in systems” becomes “point to the button” — students blueprint their app until each screen shows exactly what a user sees, what they can click, and what happens next. The result is a blueprint precise enough to hand off — not a design idea, but a set of instructions detailed enough to become their first vibe coding prompt. By the end of the module, every student has a clear, complete picture of what their solution looks like and how each part connects to the next.
With their wireframe as a blueprint, students write their first detailed prompt and watch it become working code — then test it, find the gaps, and refine. This is the prompt-test-refine cycle: the core workflow of building with AI. Each round gets documented in the Prompt Log — what they asked for, what the AI produced, what they changed and why — with at least two full iterations recorded.
The lesson underneath: the AI generates code, but students generate the thinking. Success isn’t a perfect app on the first try — it’s a clear record of learning to direct a tool with precision.
Three days of building, breaking, fixing, and repeating. With their wireframe as blueprint and problem statement as compass, students develop a working prototype that tackles the energy problem they defined — building a first version, breaking it through real user testing, fixing what doesn’t hold up, and repeating the cycle until it does. By the end, their Prompt Log reaches at least six documented iterations, a full trail of the decisions that shaped the app.
The essential question drives every round: Am I actually solving the energy problem I set out to solve — and how do I know? Progress here isn’t measured by how polished the app looks, but by how directly it answers that question.
This is the moment students step up and defend their thinking. Presenting their energy solution prototypes, they don’t just show what they built — they explain the problem it solves, why their approach makes sense, and what they’d improve with more time or resources. That’s what engineers and technicians do: defend design decisions, not just display finished products.
Alongside the presentation, students reflect in writing on what they learned — about solving problems, collaborating with AI, and their own thinking process along the way. The essential question sets the bar: Can I clearly explain what problem I solved, how my solution works, and what I would do differently? This is Demo Day.
Instructor Materials is your home base for everything needed to teach Energy and Vibe Coding. Here you’ll find the Energy Problem Bank, a ready supply of vetted, real-world energy challenges for students who need a starting point, along with curated Research Resources to support the deep-dive work of Become the Expert. Assessments are aligned to each module’s outcomes, and Collaboration Structures and Thinking Protocols offer concrete ways to structure teamwork and guide student reasoning as students move from problem to prototype. Rounding it out is LMS Ready Content, so you can drop the unit directly into your course platform and spend your time facilitating, not building from scratch.
Student Resources is your one-stop shop for everything you need to work through Energy and Vibe Coding. Here you’ll find Student Handouts to guide you through each stage of the unit, plus a Vocabulary Library to keep key terms like criteria and constraints, systems models, and vibe coding at your fingertips. Student Games make practicing core concepts more hands-on, while Iteration Logs help you track how your energy solution evolves through each round of build, break, fix, and repeat. When you need it all in one place, All Resource Downloads has you covered.
Professional Development and Guidance is where you’ll find the support to teach Energy and Vibe Coding with confidence, no matter your starting comfort level with AI tools. From guides on vibe coding, NotebookLM, and debugging to a ready-made Prompt Library, you’ll have what you need to model each stage of the process for students. Resources on differentiation, stakeholder communication, and ethics and AI round out the toolkit, and the Chatbot Teacher Assistant is on hand whenever you need extra support along the way.
Supplementary Materials is the catch-all for everything that rounds out Energy and Vibe Coding. Here you’ll find adaptations of the unit for grades K-8, along with Implementation Pathways and a Pacing Guide to help you map the unit onto your own classroom timeline. CREATE webinars will be posted here as they’re hosted, and if there’s a resource you didn’t find elsewhere, chances are it lives in this module.
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CREATE expresses its gratitude to the Wisconsin Public Service SolarWise for Schools Program for sharing these Energy lesson plans with us.
To access additional Energy Education Resources, visit our Resources page.
