Introduction:
Planning a trip today is fragmented and overwhelming; users have to jump between multiple platforms for inspiration, budgeting, and itinerary creation. There is no simple way to translate personal preferences into a structured, personalized travel plan.
ZenTravel explores how AI can simplify this process by turning user inputs into meaningful, end-to-end travel experiences.
Travel planning involves too many decisions across disconnected tools, making it time-consuming and cognitively heavy. Users struggle to convert vague ideas like “relaxing trip” or “adventure travel” into clear, actionable plans. This project aims to reduce that friction by using AI to interpret intent and generate personalized journeys.
AI Tools:
Stitch by Google
Google AI Studio
Concept: ZenTravel — AI Travel Planner
ZenTravel was imagined as a personal AI travel companion, not just a tool that generates itineraries, but one that understands how people feel about travel.
The core idea was simple:
Can we turn personal preferences into a travel plan that actually feels like your trip?
Instead of overwhelming users with open-ended prompts, ZenTravel focuses on:
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whom you’re traveling with
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How do you like to spend
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What kind of experiences are you drawn to
These inputs act as signals to generate a personalized itinerary, moving from generic recommendations to something more intentional and relevant.
Design Direction
The experience blends two distinct influences:
1. Premium travel aesthetic
Inspired by high-end travel platforms, the interface is designed to feel:
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calm
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aspirational
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immersive
The goal was to create a sense of escape even before the journey begins, making planning feel like part of the experience, not a task.
2. Language shaped by Gen Z & social behavior
Instead of a formal or robotic tone, the interaction explores:
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casual phrasing
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relatable prompts
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light Gen Z slang
This makes the experience feel more human, conversational, and current, especially for younger users who are used to interacting with digital products more informally.
Underlying Thought
Most travel tools focus on logistics. ZenTravel focuses on personal feelings.
- It’s less about: “Where should you go?”
- And more about: “What kind of experience are you looking for and how can AI shape that into a journey?”
Design Process
Concept UI — Early Generated Exploration
The initial screens were generated using Stitch, translating prompts into visual directions. This phase focused on quickly exploring layouts, content structures, and interaction patterns rather than refining details. It helped validate how user preferences and emotional cues could shape the overall travel experience.
Final UI — Refined Experience
The final interface builds on the early concepts, evolving into a more cohesive and premium experience. Visual design, copy, and interactions were refined to create a calm, immersive journey that feels personal and intuitive. The focus was on simplifying decision-making while maintaining a strong emotional connection to the idea of travel.
→ Explore the interactive prototype
→ View app
Project BTS Video
A glimpse into the making of ZenTravel, capturing how ideas evolved through prompts, AI-generated iterations, and design refinements. This video highlights the workflow between Stitch and Google AI Studio, showing how concepts quickly moved from rough exploration to functional interfaces. It reflects the real process of designing with AI, iterative, fast, and constantly evolving.
My learnings
Working on ZenTravel shifted my perspective from designing static screens to shaping dynamic, AI-driven experiences.
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AI is a powerful accelerator, not a replacement
It speeds up exploration, but strong product thinking is still essential to guide meaningful outcomes.
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Prompts are the new design inputs
The quality of output is directly tied to how clearly the intent/emotions are defined through prompts.
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Designing for AI requires thinking beyond UI
It’s about structuring inputs, guiding outputs, and designing interactions around uncertainty.
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Iteration matters more than perfection
The real value comes from quickly testing ideas, learning, and refining rather than aiming for a perfect first output.