Project Overview
Project Goals
- Reduce search time for AI assistants by showcasing AI tools based on the educator's preference.
- Implement personalized search capabilities allowing educators to find tools quickly and efficiently.
- Follow Marvel AI’s branding guidelines for typography, color palette, & icons to keep within Marvel AI's brand standards.
- Increase interactivity for favorited tools & recently used tools so educators can customize the tools they use most.
Challenges
- Problem: Educators struggle to find relevant AI tools due to poor categorization and lack of personalization.
- Impact: Reduced platform adoption and inefficiencies in teaching workflows.
- User Feedback: Highlighted needs for curated content, personalized search, and quicker access to tools.
Solutions
- Key Features: Intuitive search bar with autocomplete, categorized sections (Date range & Sort by), trending assistants, advanced filters, interactive favorites & recently used.
- Design Decisions: Prioritized Favorite tools & recently used assistants, used filter buttons for better navigation, & ensured responsive design for mobile optimization.
Process
- Research: Analyzed discovery workflows of platforms like Khanmigo and Duolingo.
- Wireframing: Drafted low-fidelity layouts to test navigation and categorization.
- Prototyping: Developed interactive prototypes and conducted usability tests with educators.
- UI Design: Created high-fidelity mockups aligned with Marvel AI’s branding guidelines.
Key Results
- Usability Improvement: 90% of educators found tools faster in usability tests.
- Engagement Boost: 75% increase in assistant cards clicked during prototype testing.
- Adoption Potential: Positive feedback from 85% of testers on discoverability and relevance.
Impact
- User Testing: Positive feedback on improved discoverability and navigation.
- Design Alignment: Consistent with Marvel AI’s branding and user experience goals.
- Anticipated Impact: Increased engagement with assistants and better resource allocation for administrators.
Design Question
How might we design an intuitive, centralized discovery system that curates and recommends teaching assistants tailored to educators’ specific goals, enabling them to efficiently find & adopt tools that enhance their teaching & student learning outcomes?
