Education / AI
Hoperise
An AI-powered learning companion that helps users start new skills with clarity and confidence through personalized roadmaps and real mentors.
- Timeline
- 3 months
- My Role
- UX Research, Design, and Prototyping
- Team
- One other designer; I led the core flows and structure
- Tools
- Figma · Miro · Trello
Overview
- Hoperise is an AI-powered learning companion designed to help users start new skills with clarity and confidence. It generates personalized roadmaps, guides learners through each step with an AI mentor, and connects them to real educators when deeper guidance is needed. The goal: reduce overwhelm and make skill-building structured, achievable, and motivating.
- I contributed end-to-end across design and research: UX research, user flows, interaction design, UI design, prototyping, and design system creation.
Problem Space
People who want to start learning a new skill often struggle with too much information, unclear paths, trouble finding trustworthy mentors, and fading motivation. Key problems:
- Overwhelming amounts of information when starting a new skill
- Unclear learning paths and progression
- Difficulty finding trustworthy mentors
- Losing motivation due to lack of structure
Key Observation Insights
Our research surfaced:
- Learners often abandon courses when they don't understand the next step
- Manual onboarding process is time-consuming and inconsistent
- Mentors struggle to track learner progress without a unified system
Competitive Analysis
We analyzed Masterclass, Skillshare, Coursera, Codecademy, and Maven. Findings:
- Existing platforms excel at course delivery but lack personalization
- Mentor access is limited or requires premium pricing
- UI is often overwhelming with too many choices
- Lack of clear roadmaps for beginners
- Limited community or peer support features
Key Research Insights
- Beginners need clear, structured roadmaps tailored to their goals
- Human mentorship is essential for motivation and deeper learning
- AI guidance should simplify, not overwhelm
- Platform must work across diverse skill categories
- Social proof and learning community matter for engagement
Challenges & Solutions
Challenge
Designing roadmaps as flexible, reusable components that could adapt to any skill.
Solution
We created a modular roadmap system that worked for 'UI Design Basics' to 'Learn Python' to 'Improve Public Speaking.' This required iterative testing to ensure clarity and consistency without losing personalization.
Challenge
Balancing AI guidance with human mentorship without overwhelming users.
Solution
We introduced the features gradually. Onboarding starts with AI, and mentors come in once learners are comfortable. That staged approach kept the cognitive load down.
Outcomes
- Reduced confusion and cognitive load for new learners
- Guided learners step-by-step through their chosen skill
- Made roadmap navigation intuitive and flexible
- Let AI and human mentorship work together without friction
- Created a scalable structure that works for diverse skills
Future Improvements
- Reduce some features to avoid overwhelming first-time users
- Simplify certain flows to make the learning journey even more straightforward
- Introduce 'beginner' vs 'advanced' modes for different user types
- Add more peer-to-peer learning features and community challenges
- Expand AI mentor capabilities with more specialized guidance