Bitbo - Bitcoin Stats & Data
With user-centered approach, the goals was to create an intuitive interface for effortless financial management while incorporating gamification.
Context
Teachers in both public and private schools face a major challenge: adapting assignments for neurodivergent students (such as those with ADHD, autism, dyslexia) in a fair, personalized, and feasible way.
The problem?
Most of these educators don’t have access to medical reports or formal training in inclusive education. This often leads to long extra hours and uncertainty when designing fair assessments.
💭 How can we support teachers in adapting learning activities — without sensitive data — and still ensure quality and fairness?
35%
75%
84%
Who was involved
PM and developers from the squad
Inclusion and neurodiversity experts
Educational consultants
AI specialists
Myself + another designer
Our process
Benchmarking + understanding the flow
User map and wireframes
Clickable prototype
Usability testing
Soft launch
Performance tracking metrics
Research + Competitive Benchmarking
We were aiming to map the teacher’s journey and pain points, then conducted a benchmarking analysis to learn from direct and indirect competitors. But we faced a issue…
📌 Legal constraint: data sensitivity
Ideally, we'd use reports like Anamnesis and IEP (Individualized Education Plan) — but those are sensitive documents under data protection laws (like LGPD/GDPR).
⚠️ So how do we build a reliable solution without them?
The solution: Design Sprint! Building a Path Forward
We led a 2-day Design Sprint with:
Inclusion and accessibility specialists
AI engineers and researchers
Our goal: Enable teachers to guide the AI based on their lived experience with students — no medical diagnosis required.
🛠️ Result: a complete user flow, validated by the PM (business rules), devs (technical feasibility), and inclusion experts (content quality).
From flow to prototype
We created high-fidelity wireframes based on the approved flow
Then, we designed wireflows including success and failure scenarios
Ensured alignment across design, dev, and PM
And finally, delivered an interactive prototype for testing
🧪 We tested it with real users!
This process was crucial to collect valuable feedback, make usability improvements, and prepare for rollout.
After usability testing, small adjustments were made and the product was prepared for handoff. To further ensure the reliability of the adaptations generated by the solution, we invited a select group of schools to test our product, releasing it to small groups (soft launch) and closely monitoring the results.
Launch & Results
To support continuous improvement based on user behavior, we actively monitored real usage.
To measure success, we used:
🎯 Mouseflow (heatmaps and interaction patterns)
📊 Metabase (usage data tracking)
💬 Typeform (CSAT and user feedback)
Real-world impact
🚀 Scaled from 50 to 600+ schools within months
👥 Reached 100,000+ students
📈 Continuous improvement guided by real data
Key takeaways
✅ Designing for different realities: I deepened my understanding of how various neurodivergences affect learning and how digital products can — and should — adapt to meet these needs.
✅ Scalability with purpose: With a solid user-focused foundation, we created a product that not only worked — it scaled fast, with impact.
✅ Responsible AI Design: Working with AI requires careful testing, iteration, and ethical thinking. Good design = usability + accountability.
Let’s connect if you're passionate about designing tech that matters 💬🙂
