AEye is a mobile learning concept that helps users distinguish AI-generated content from real-world media through educational modules and repeat-use game loops.
UX Problem
AI literacy tools are often too technical or not engaging enough for repeat use.
Users need low-friction practice that reinforces pattern recognition over time.
Feature discoverability and action hierarchy can break learning momentum.
My Role
Conducted user interviews to uncover misconceptions and learning friction.
Synthesized findings into personas and structured journeys.
Designed core learning flows, feedback loops, and social features.
Led usability iterations to improve discoverability and navigation clarity.
UX Strategy
Designed daily challenge loops for habit formation.
Added progression systems with points, levels, and milestones.
Used local and global leaderboards to reinforce motivation.
Repositioned social actions after discoverability testing feedback.
Execution
Low-fidelity sketches and wireframes
Iterative usability testing cycles
High-fidelity interactive Figma prototype
Documented rationale behind interaction decisions
Outcome
Improved navigation clarity and feature discoverability across learning and social flows, demonstrating end-to-end UX ownership from research to prototype delivery.
