Skillup: Web App

Context

SkillUp is a gamified learning experience for novice industrial maintenance engineers with ≤18 months of experience.

These engineers needed a reliable way to understand their current capability and identify how to improve, while employers needed better skill development because capability gaps could contribute to higher MTTR, lower FTFR, repeat breakdowns and safety risk.

Problem

The core problem was not simply:

"Engineers need more training."

It was two deeper problems:

• Progress invisibility

• Engineers couldn't clearly see how their skills were developing.

Unclear paths to improvement
They didn't have a structured, personalized answer to:

"What should I learn next?"

The additional challenge was critical:

How do you use gamification in a safety-critical environment without letting engagement incentives undermine safe behavior?

My role

My role: Product Designer
I owned: Discovery, segmentation, persona development, product concept, gamification strategy, adaptive-learning logic, safety interaction design, UX and high-fidelity UI.
Collaborated with: Solo project — Product Designer (me).

Research & insights

The discovery phase was used to define three user segments and develop personas for novice maintenance engineers.

The first-principles analysis reduced the broad brief to two root problems:

Progress invisibility and Unclear paths to improvement.

This led to an important design insight:

The product shouldn't simply reward engineers for using the platform. It should reward evidence of actual competence and safe behavior.

Product opportunity

The opportunity became:

Create an adaptive skill-building system that tells engineers what they need to learn next while making verified competence, not engagement, the unit of progress.

That became the foundation for the mission system.

Design principles

1. Competence over clicks
Progress should be tied to verified outcomes rather than logins.

2. Safety before engagement
No gamification mechanic should bypass a safety prerequisite.

3. Private motivation over public comparison
Novice engineers shouldn't be publicly shamed in a hierarchical safety-critical workplace.

4. Adapt to demonstrated capability
The next learning activity should respond to what the engineer already knows.

5. Measure unintended consequences
The product needs guardrails to detect whether engagement mechanics are harming safety or learning quality.

Key decisions

Decision: Replace public leaderboards with Personal Bests + Team Goals
Alternative: Competitive leaderboard.
Why: Public comparison could create unhealthy pressure among novice engineers in a safety-critical environment.

Decision: Gate levels by verified outcomes
Alternative: XP based primarily on usage/activity.
Why: Usage isn't evidence of competence.

Decision: Use adaptive missions
Alternative: One fixed curriculum for everyone.
Why: Engineers with different skill gaps need different next steps.

Decision: Make PPE verification mandatory
Alternative: Treat safety as a separate training module.
Why: Safety must be a prerequisite to participation, not another optional achievement.

Decision: Define guardrail metrics before reward mechanics
Alternative: Design the gamification first and measure later.
Why: In a safety-critical product, the reward system itself is a potential source of risk.

System / architecture

The core adaptive system is the Instruct mission engine:

Safety prerequisite → Skill mastery → Task relevance → Mission type

The engine then determines whether the engineer receives:

Safety micro-mission → Targeted micro-mission → Stretch mission

This creates a feedback loop where demonstrated performance influences the next learning experience.

The wider system connects:

Skills → Missions → Verification → Progress → Analytics → Next mission

Final experience

The final high-fidelity experience includes:

• Missions dashboard

• XP and skill levels

• L1–L5 skill progression

• Competence badges with audit trails

• Personal Bests

• Team Goals

• Adaptive mission flows

• PPE verification

• Skill analytics

The result is a system designed around behavior change and competence development, rather than a collection of gamified screens.

Validation

The portfolio documents the discovery, product reasoning and high-fidelity design, but does not document live usability testing or deployment results.

Therefore, the validation framework is currently hypothesis-driven.

The most important validation questions would be:

• Does adaptive learning improve skill acquisition?

• Does hint dependency decrease?

• Does FTFR/MTTR improve?

• Does safety behavior remain stable or improve?

• Does the system unfairly disadvantage particular work schedules?

Impact

The project establishes a guardrail framework designed to make impact measurable:

• FTFR/MTTR should not worsen as engagement rises.

• Hint dependency should decrease as skill develops.

• Near-miss reporting should increase as a potential psychological-safety signal.

• Night-shift engineers should not be unfairly penalized.

What I learned

In safety-critical products, engagement is not the goal. The goal is better behavior and better outcomes - and the product needs guardrails to prove the difference.