Product is the operating field
Learners practice users, outcomes, scope, constraints, tradeoffs, briefs, acceptance criteria, and decision quality.
Courses
Three pathways for product judgment in an AI-native world.
KLD courses teach learners to make product decisions that can be seen, explained, reviewed, and improved. The catalogue begins with Product x Design x AI, then expands the same learning model into marketing and engineering.
Every course produces artifacts that can be reviewed.Every pathway keeps AI use transparent and human-led.Every overview names the learner role, evidence standard, and capstone direction.
Shared architecture
The pathway names change; the institute standard stays consistent.
Specialist craft raises the bar
Each pathway adds a discipline standard: design quality, marketing proof, or engineering literacy.
AI accelerates the work
AI expands options, critique, drafting, prototyping, synthesis, and documentation while human judgment stays visible.
Pathway catalogue
Compare the active course with the two planned pathways that inherit the same KLD method.
Active pathwayWeek 0 + 24-week semester path
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.
Learner role
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.
Evidence standard
- Observation before generation
- Critique before acceptance
- Revision before showcase
Artifact trail
Screen noticing boardVisual foundations mapAI output critique sheetProduct and design brief
Coming soon previewPlanned week-based semester path
Product x Marketing x AI
Marketing judgment for products worth explaining well.
AI makes it easy to publish more. Strong product marketing still depends on sharper value judgment, cleaner proof, better timing, useful creative range, and disciplined experiments that reveal what the market is actually teaching.
Learner role
The intended graduate shape is an AI-native product marketer with product-owner judgment: someone who can understand the product, name the audience situation, make a credible promise, test the market, and keep AI-assisted marketing anchored in proof.
Evidence standard
- Market context before messaging
- Proof before persuasion
- Learning before volume
Artifact trail
Audience and value mapPositioning and proof mapOffer and objection briefLanding-page message system
Coming soon previewPlanned week-based semester path
Product x Engineering x AI
Technical judgment for product people working with AI.
AI can generate code, but responsible product work still needs clear requirements, technical literacy, risk awareness, review habits, and launch judgment. This pathway helps product-minded learners guide AI-assisted builds with enough discipline to know what deserves trust.
Learner role
The intended graduate shape is an AI-native product owner with engineering literacy: someone who can define build intent, understand technical consequences, collaborate with engineers, and use AI implementation tools without losing responsibility for quality.
Evidence standard
- Requirements before code
- Traceability before trust
- Quality before release
Artifact trail
Product requirements briefTechnical concept mapAI-assisted build logQA, risk, and edge-case checklist