AI-Native Design Engineering course for designers Your designs, working in the browser.
AI-Native Design Engineering is a 12-week live course for UX, UI and product designers. Direct an AI coding agent with real context, read and change the code it writes, test with people, and ship a working demo you can explain and modify on the spot.
- Length
- 12 weeks + setup week
- Time
- About 8 hrs a week
- Format
- Live, online or in person
- Credential
- Certificate, earned live
What you ship by the last week
- A working front-end demo at a public link
- A small component system, in Figma and in code
- The memory garden of context, decisions and design thinking the AI worked from
- Test findings from real people, and the fixes they led to
- Your first reusable AI skill, ready to take back to work
- A concise case study that shows your judgment, not just the output
What is AI-native design engineering?
AI-native design engineering is the practice of designing and building digital products with AI in the workflow from the start. The designer sets the direction, writes the context an AI coding agent works from, reviews and edits the code it generates, and stays accountable for the quality of the experience. It sits between product design and front-end engineering.
This course teaches that practice to working designers in 12 weeks, through one real project and a live build defense. The curriculum is co-developed with Happening.
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| Provider | UMO Design School, a program of UMO Design Foundation (founded 2000) |
|---|---|
| Curriculum partner | Happening, practicing design engineers whose AI-native DesignOps pipeline is the course backbone |
| Who it is for | Practicing UX, UI and product designers |
| Prerequisites | Design fundamentals. No coding experience required. |
| Length | 12 weeks, plus a self-paced setup week |
| Time | About 8 hours a week, 48 live studio hours in total |
| Format | Live cohort, online or in person in Hyderabad |
| Tools | Figma or Paper, an AI coding agent such as Claude Code, Codex or Cursor, GitHub, Vercel |
| Credential | UMO Certificate in AI-Native Design Engineering, earned through a live build defense |
| Fee | $729 or ₹39,000 in India |
| Next start | December 5, 2026 |
Why should designers learn to build with AI?
Because prompting is the easy part. AI can generate an interface in minutes. Teams still need someone who decides what is worth keeping, gives the agent the right context, checks what it built and can explain every choice. That person is AI-native, not AI-augmented. The augmented designer slowly lets the tool's judgment replace their own; the AI-native designer sets the context, makes the decisions and uses AI to carry them out faster. This course trains that judgment on one real project, start to finish.
Brief the AI like a lead
Turn user evidence into a brief and a memory garden the agent works from: your context, your decisions and how you design. Set up your own toolchain of skills and connectors.
Read and change the code
Learn enough HTML, CSS and React to follow what the AI writes, review its changes, make targeted edits and keep every version safe in Git.
Test it, then defend it
Watch people use your build, check keyboard and screen reader access, fix what breaks, and earn your certificate by explaining and changing your build live.
Who is this course for?
AI-Native Design Engineering is for UX, UI and product designers who already know how to design interfaces and want to build and test them as working products. You do not need to have coded before.
A good fit if
- You already work with user flows, visual hierarchy and interface design
- You want to stop handing off static screens and start testing real builds
- You use AI tools but don't yet trust what they produce
- You have never coded professionally, and that is fine
Start somewhere else if
- You are new to design. The AI-Native Product Design pathway starts with the foundations, then joins this course.
- You want to become a back-end or full-stack engineer
- You are looking for a quick tour of AI tools
What will you learn in 12 weeks?
You learn to frame a product problem, build the context an AI coding agent works from, set a visual direction and the foundations that keep AI output from looking generic, build and review a working front end, test it for usability and accessibility, and ship it. One project, six modules, twelve weeks. Bring a focused product problem. Every module moves the same project forward, and every module ends with something you can show. Faculty review your work at weeks 2, 6 and 10.
Week 0Setup week
Self-paced, before the first live session. Install the toolchain, set up your project folders, create your GitHub account and publish a one-page site, so week 1 starts with design work, not installs.
You leave with: a working setup and your first live link.
Weeks 1–2Frame the problem and build the context
Investigate a real friction, set a scope you can finish, find the single blockage point, and build the memory garden an AI agent needs to do useful work.
You leave with: a one-page brief, testable requirements and a memory garden: context.md, DESIGN.md and a glossary. Milestone review 1.
Weeks 3–4Map the experience and learn how the browser thinks
Map the whole product as an IA, then walk one User Interaction Flow as the person, asking what they need on each screen right now. Learn how pages, components, props and sample data fit together, enough to direct a build and spot when the AI gets it wrong.
You leave with: an IA, a User Interaction Flow, a full inventory of screen states, and a page you edited by hand.
Weeks 5–6Set the visual direction and design without the AI slop
Write a brand noun list and commit to a direction. Then set the foundations before AI explores: tokens, a depth system, accessibility rules and three to five seed components the AI builds from, so nothing looks generic.
You leave with: tokens, seed components and a design-thinking file, with matching components in Figma and in code. Milestone review 2.
Weeks 7–8Build the main flow with AI and stay in control
Connect your AI coding agent to your memory garden and design files, then direct it through the core flow. Review each change before you accept it, commit often, and handle the loading, empty, error and success states most demos skip.
You leave with: a working main flow on sample data, with a clean version history in GitHub.
Weeks 9–10Test with people and fix what breaks
Run sessions with real users. Check keyboard access, screen reader basics, responsive layouts and the interactions that matter most, then prioritize and ship the fixes.
You leave with: a findings report, an accessibility check against WCAG 2.2 AA for your core flow, and a revised build. Milestone review 3.
Weeks 11–12Ship it, document it, defend it
Publish your demo, write down its technical limits honestly, write a backend handover note, and turn a routine you repeated into your first reusable AI skill. Finish with your live build defense.
You leave with: a public demo link, a case study, your first skill and your certificate.
Tools you will use
- Figma or Paper
- An AI coding agent, such as Claude Code, Codex or Cursor
- Skills, connectors and MCPs
- GitHub
- Vercel
- Browser developer tools
We review the toolchain before every cohort. The methods carry across tools, so you are not locked into one vendor.
Example project scopes
- A clinic check-in flow that shortens the wait at the front desk
- A volunteer sign-up tool for a community event
- A simpler bill-splitting flow for shared households
Examples only. You bring your own problem, and faculty help you size it.
How do you earn the certificate?
You defend it. Anyone can show an AI-generated screen. In your final week you walk a faculty panel through your build, answer questions about any part of it, then make a change they request, live, in about 20 minutes. Pass, and you earn the UMO Certificate in AI-Native Design Engineering.
That is the proof hiring managers are now looking for: you understand what you shipped.
What the panel assesses
- A clear problem, backed by evidence
- Visual and interaction quality, consistent with your system
- A working flow that handles real states, not just the happy path
- Accessibility of the core flow
- Changes made because of what testing showed
- Your ability to explain and modify the build yourself
- Simplicity, with no feature that lacks a real friction behind it
- At least one reusable AI skill, built and tested
How is the course taught?
Live demonstrations, guided build sessions and peer critique, all applied to your own project. Online and in-person cohorts follow the same outcomes and the same assessment standard.
- Online
- Two 2-hour live studios a week, plus about 4 hours of practice
- In person · Hyderabad
- One 4-hour studio a week, plus about 4 hours of practice
What does the course not cover?
- It is front-end work. Your project runs on sample or mock data.
- It does not cover production back ends, sign-in systems or live AI integrations. Those need further engineering.
- It will not turn you into a software engineer in 12 weeks. It will make you a designer who can build, review and explain a working product.
- It does not guarantee a job. It gives you assessed work and a case study you can stand behind.
What add-on modules are available?
You can add Portfolio and Career Practice after the core, or take a foundation module first if you have gaps in visual design or research. A Designing AI Experiences elective is in development.
Portfolio and Career Practice
Turn your project into a portfolio story, present your design and code decisions, and practice for design interviews. Taken straight after the core.
Foundation modules
Gaps in visual design or research? Take Design Essentials or UX Research and Interaction before you join the core. See the pathway
Designing AI Experiences
Patterns for products that have AI inside them: trust, uncertainty, user control and failure states. Join the interest list to hear when it opens.
Who teaches the course?
Practicing designers and design engineers from UMO and Happening lead every studio and sit on the defense panel. Faculty for each cohort are named here before applications close.
Rohan Sridhar
CXO & Co-Founder, Happening Design
Kaladhar Bapu
AI native design strategist
Swastik Bose
Design Engineer, Happening Design
How much does it cost, and when does it start?
The core course costs 729, or 39,000 in India, and the next cohort starts December 5, 2026. Apply with a short note about your design experience and the problem you want to work on. We reply within 3 working days. Not sure yet? Come to a free info session and ask faculty anything.
- Cohort starts
- December 5, 2026
- Apply by
- November 30, 2026
- Cohort size
- 20 seats
- Info session
- Nov 21, 8.30PM IST/10AM EST
AI-Native Design Engineering
Core course · 12 weeks + setup week
- 48 hours of live studio time with faculty
- Three milestone reviews and peer critique
- Live build defense and certificate
- Setup guide and supported toolchain
Add Portfolio and Career Practice for ₹5,000. Tool costs during the course are up to [amount] a month, listed in full before you pay.
Apply for the next cohort Save a seat at the info sessionFrequently asked questions
What is a design engineer?
A design engineer is a designer who can also build. They design the experience, then turn it into working front-end code, increasingly by directing AI coding agents. Teams value them because fewer decisions are lost in handoff and ideas can be tested as real products, not static screens.
What does AI-native mean?
AI-native means AI is part of your workflow from the start, while the judgment stays yours. An AI-augmented designer uses AI tools and slowly lets the tools' decisions replace their own. An AI-native designer sets the context, makes the decisions and uses AI to carry them out faster. This course trains the AI-native way of working.
Do I need to know how to code?
No. You need solid design fundamentals: user flows, visual hierarchy and interface design. You learn to read, edit and explain code through guided practice, starting in the setup week. By the end you can follow what the AI writes and change it with intent.
How is this different from a vibe coding course or a UX bootcamp?
A vibe coding course teaches you to generate apps fast. A UX bootcamp teaches design but stops at the prototype. This course connects the two: you design, build with AI, test with people and then prove you understand the build in a live defense before you earn the certificate.
Do I have to pay for AI tools?
Some tools need a paid plan. We list every cost, up to [amount] a month, before you enroll. If a paid AI assistant is not an option for you, a supplied code-review exercise covers the same learning outcome.
Is this a course on designing AI features?
It is a course on building products with AI in your workflow. Your project can include a simulated AI interaction. Designing AI features in depth is the focus of the Designing AI Experiences module, now in development.
What happens after the course?
You keep your demo, code, case study and first AI skill. Your build is a working prototype, which is what UMO's Design Pitch asks entrants to bring, and selected projects may be invited to show at UXINDIA.
I'm new to design. Can I still join?
Yes, through the AI-Native Product Design pathway. It starts with two foundation modules, Design Essentials and UX Research and Interaction, then joins this course and ends with Portfolio and Career Practice. It runs for 24 weeks.
Bring your design judgment into the build.
Questions? team@umo.design
