Teach from a visible problem
Each lesson begins with a clear design or product judgment problem, then gives learners a concrete screen, prompt, template, or artifact to work from.
Product x Design x AI
Learn to judge product experience before AI accelerates it.
AI can produce interface options in seconds. It cannot decide what is clear, trustworthy, usable, accessible, or worth building. This course teaches the judgment behind better product experience, then shows learners how to use AI without surrendering that judgment.
ActiveWeek 0 + 24-week semester path
Photo by UX Indonesia on Unsplash
Why this course matters
The flagship KLD pathway establishes the institute method: product ownership as the operating role, design as the quality discipline, and AI as a practice partner that expands options without replacing judgment.
Built for
For ambitious beginners, creative operators, founders, marketers, and early product people who need the confidence to judge product experiences with clarity.
What learners produce
Learners produce observation records, critique sheets, prompt logs, screen direction notes, acceptance criteria, revision evidence, and a finished product owner case story.
KLD standard
A studio pathway for judgment, not just tool fluency.
The intended graduate shape is an AI-native product owner with design-specialist capability: someone who can read a screen, explain a product decision, work with AI, and still own the quality of the outcome.
Studio rhythm
Practice in the open
Learners build through observation, AI-supported option generation, critique, revision, and written explanation.
Review the evidence trail
Artifacts are reviewed for clarity, evidence, accessibility, product logic, and the learner’s ability to explain the decision.
Evidence standard
Observation before generation
Learners record what they noticed before asking AI for help, so the artifact shows human attention rather than tool dependence.
Critique before acceptance
Every AI-supported direction is checked against design vocabulary, user task, product outcome, accessibility, and evidence quality.
Revision before showcase
Work is revised into briefs, flows, acceptance criteria, and case-story notes that make decisions reviewable by another person.
Learning arc
A guided semester from orientation to evidence.
Weeks 0-4: Guided entry
Set up the learning workspace, learn how designers read screens, and complete the first AI-assisted critique without skipping human observation.
Weeks 5-8: Visual foundations
Study hierarchy, spacing, type, layout, contrast, and visual attention through guided screen analysis and studio boards.
Weeks 9-12: Product framing
Connect interface choices to user tasks, context, product promises, business constraints, and the outcomes a product owner must protect.
Weeks 13-16: AI generation
Use AI to generate options, critique alternatives, improve UX writing, document assumptions, and make tool-assisted work inspectable.
Weeks 17-20: Product decisions
Turn observations into decisions: flow direction, screen states, acceptance criteria, review notes, and tradeoff explanations.
Weeks 21-24: Capstone evidence
Package the evidence trail into a product owner case story with before-and-after reasoning, critique, revision, and team-ready explanation.
Learner outcomes
- Read interfaces with the vocabulary of product owners, UX designers, and visual design reviewers.
- Use AI to widen options while keeping critique, source judgment, and final decisions human-led.
- Write briefs, acceptance criteria, feedback notes, and tradeoff explanations that a team can act on.
- Build a portfolio case story that shows how the learner noticed, judged, revised, and decided.
Artifact set
Screen noticing boardVisual foundations mapAI output critique sheetProduct and design briefPortfolio-ready case story
Capstone
A polished product/design case story: the original problem, AI-supported options, critique, revision, tradeoffs, and final decision.
Course promise