An AI-powered travel planning app consolidating itineraries, packing lists, and bookings using a supervised learning recommendation engine.
Role
UX Designer, 3-Person Team
Duration
Oct – Dec 2024
Course / Tools
INFO693 · Figma
02 · My Role
Led competitive analysis and a portion of user interviews on trip-planning pain points, sketched initial low-fidelity concepts, and built hi-fidelity prototypes for 3 of 6 core screens (teammates completed the rest). Defined the final end-to-end flow.
03 · Problem Framing
Existing productivity apps can't generate contextual, trip-specific suggestions; niche travel apps solve only a sliver of the problem.
04 · Key Decision: Explainable AI
Problem
A black-box AI model would undermine the app's core promise of reducing travel-planning anxiety.
Decision
Chose supervised learning over unsupervised/reinforcement approaches specifically because it keeps recommendations traceable to the inputs a user actually gave.
Grounded in Norman's principle that interactive systems need transparent feedback loops.
05 · Core Flow
Login→Home→Packing List (AI)→My Trips→Explore
06 · App Screens
The five core-flow screens, plus the trip itinerary — where the explainable-AI and offline-first decisions above actually show up in the UI.
01 · Login
Entry point into the app.
02 · Home
Upcoming trip and AI suggestions surfaced immediately, each with a visible reason.
03 · Packing List (AI)
Every AI suggestion discloses the inputs it came from — the explainable-AI decision made visible.
04 · My Trips
Every trip carries an offline badge — connectivity anxiety was the top interview finding.
05 · Explore
Rebuilt after heuristic evaluation flagged the original as visually busy — one filter row, one grid.
06 · Itinerary (Offline)
Full day-by-day plan cached locally — offline mode shipped as core, not a later add-on.
07 · Ethical Design Decisions
Minimal data collection with explicit consent, anonymized/secured training data, bias audits on diverse datasets, and user-facing feedback/error reporting.
08 · Research & Validation
Heuristic evaluation surfaced 3 issues: system status under poor connectivity, an ambiguous "Connections" menu label, and a visually busy Explore page. Concept validation interviews (4–5 participants) surfaced two themes, each confirmed by a second, independent signal (Figma comment threads).
Decision: Offline Support
Problem
Connectivity anxiety surfaced repeatedly in interviews.
Evidence
4–5 concept validation interviews plus independent Figma comment threads converged on the same concern.
Decision
Offline mode + local storage prioritized as core, not a later add-on.
Decision: Menu Clarity
Problem
"Connections" menu label caused confusion about what it contained.
Evidence
Confirmed independently by both interview feedback and Figma comment threads.
Decision
Label rewritten and menu restructured for clarity.
09 · Impact
Directional, not quantified: no task-based measurement was run. The strongest signal: two independent research methods converged on the same two problems, giving real confidence the fixes addressed genuine friction.
10 · Recommendations
True offline mode, inline error handling and validation, and accessibility improvements: contrast, readable fonts, and voice assistance.
11 · Reflection
Strongest asset was decision reasoning and ethical framing; with more time, the next step would be structured task-based usability testing to replace directional findings with measured outcomes.