Patrícia Martins.
EDTECH•AI•MOBILE
Aprova
A mobile-first, AI-powered platform for Brazil’s university entrance exam, built around access, feedback, and study confidence.
PROJECT OVERVIEW
The product had to make high-stakes exam preparation feel more accessible, practical, and possible.
Brazil’s ENEM exam — the national standardized test used for university entrance — plays a major role in access to higher education, but many students preparing for it do so with limited resources, inconsistent feedback, and smartphone‑only access. The project focused on designing a platform that could respond to those conditions more realistically.
ROLE
End-to-end: research → tested prototype
CONTEXT
Digital Design capstone project
FOCUS
AI-powered access, feedback & study confidence
TOOLS
Figma & FigJam
RESEARCH
The research pointed to one clear constraint: the product had to work for motivated students studying under limited conditions.
Desk research across government data, academic publications, and EdTech market reports revealed recurring patterns: students often studied exclusively on smartphones, had inconsistent internet access, lacked regular essay feedback, and relied heavily on passive study methods. The product had to support progress within those limits rather than assume ideal learning conditions.
CONNECTIVITY PARADOX
89% own smartphones, but only 41% have consistent broadband
FEEDBACK GAP
Public schools average 1 teacher per 28 students, limiting guidance.
LOW SELF-EFFICACY
62% show low academic self-efficacy despite high motivation.
MOBILE-FIRST REALITY
78% study exclusively via smartphone (small screens: 4-5 inches)
KEY FINDINGS
COMPETITIVE ANALYSIS
No platform combined ENEM specificity, intuitive UX, genuine freemium access, and mobile-first design in one place.
Brazilian AI-based platforms tended to be too complex, while international products with better usability were too generic for ENEM preparation. The opportunity was to design something more specific, more accessible, and easier to use consistently on mobile.
DESIGN APPROACH
I approached it as an accessibility problem shaped by context, not as a feature race.
The central question became: How could the platform help students study more effectively without assuming stable internet, large screens, strong prior confidence, or teacher support? That pushed the work toward mobile-first flows, clearer learning structure, supportive feedback, and AI features that removed friction instead of adding complexity.
KEY DESIGN DECISIONS
01
The platform was designed for smartphones first because that was the main study environment.
Every screen was structured for mobile use before anything else. This respected the reality that many users studied only on smartphones and helped reduce cognitive load for students with lower digital confidence.
02
Onboarding collected the context needed for adaptation without making setup feel overwhelming.
The first-use flow gathered personal, socioeconomic, and study-habit information to build a more relevant learning profile. That allowed recommendations and difficulty to adapt over time while keeping the initial experience understandable.
03
The dashboard showed what mattered first, then revealed detail only when students needed it.
Overview cards highlighted progress and weak areas at a glance, while deeper breakdowns stayed available on demand. This kept the interface useful on small screens without flattening everything into one layer.
04
The platform separated learning into three modes that matched how students actually prepare.
Study organized lessons and reading materials, Practice handled adaptive questions, and Writing focused on essay feedback based on ENEM competencies. The structure made the platform easier to navigate while connecting each activity to a clear purpose.
05
AI was used to guide learning, give feedback, and offer support — not to dominate the experience.
Aprova used predictive personalization for difficulty adjustment, automated writing feedback tied to ENEM evaluation criteria, and a contextual assistant for navigation and study support. The goal was to make AI feel practical and helpful rather than intimidating.
06
Progress needed to be visible because motivation alone was not enough.
Points, levels, and badges helped compensate for the lack of consistent feedback many students experienced in traditional schooling. Visible progress systems were designed to reinforce effort, continuity, and a stronger sense of capability.
VALIDATION
Early testing showed the concept was understandable, but readability still needed work.
Usability testing with two target users showed that the platform’s purpose was clear, the separation between Study, Practice, and Writing felt intuitive, and the achievement system encouraged engagement. The most important issue uncovered was readability: font sizes felt too small on 4–5 inch screens, especially in low-light conditions.
NEXT STEPS
-
Increase base font sizes.
-
Implement responsive scaling and test usability across screen sizes.
-
Test contrast in varied lighting.
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Expand testing to 4–6 users.
RESULTS
The project turned research on educational inequality into a concrete, testable product direction.
Aprova turned research on educational inequality into a concrete product direction, shaping a mobile‑first experience around real study conditions and access barriers. The concept used AI in a focused way and early testing made the next round of improvements clear.
01
Mobile-first design was grounded in real study behavior.
02
Access barriers were translated into clear product decisions
03
AI worked as practical support, not a gimmick.
04
Testing validated the concept and exposed readability issues.
KEY INSIGHT
Designing for access means designing for the conditions people actually have.
Inclusive design isn't just about removing cost barriers. It means designing for real conditions — small screens, inconsistent internet, limited feedback, and students who need the platform to be clear enough to use consistently without support.