Context-Aware AI Trip Planner

This project explores how AI can shape product decisions, not just generate content.I designed and built a web-based AI trip planner that adapts its interface based on user context and behavior, instead of relying on static dashboards or predefined templates.The goal was to reduce planning friction while maintaining clarity and user control.

Most trip planners treat planning as a configuration task.
Users are asked to:
1. Set preferences upfront
2. Choose templates
3. Manually adjust rigid itineraries

In reality, travel planning is ambiguous and contextual.
People often start with vague intent and refine as they go.

The experience starts with a single natural-language input:
‍“Three days in Paris.”

‍From this input, the system infers trip context and dynamically adapts the interface as users interact.

Instead of one fixed dashboard, different users see different structures based on:
Trip duration
Interaction patterns
Emerging priorities

The interface evolves with the user.

Progressive disclosure
The system avoids upfront configuration and reveals information gradually.

‍Behavior-driven UI
Layouts adapt based on interaction instead of predefined personas.

‍Designing with restraint
In an AI workflow, adding features is easy.
The harder—and more important—decision was choosing what to remove.

For every feature, I asked:
“Does this improve decision-making, or add noise?”

The result is a lightweight, adaptive planning experience that:
Reduces upfront effort
Responds to user context
Prioritizes clarity over feature abundance

More importantly, this project reflects how I approach AI-powered design:
AI as a collaborator that amplifies judgment—not a shortcut for thinking.