Conscious: Connection > Consumption
Designing an AI-driven wardrobe assistant that turns fast fashion habits into sustainable style routines.
Role
UX Researcher & Sole Prototyper
Team
Solo project
Timeline
June 2024 – August 2024
Skills
Flutter, Gemini API, Python, Figma, UX Research
NOTICE: This case study’s visuals are currently being updated.
Background & Context
The rise of fast fashion has turned self-expression into overconsumption.
People are buying 60% more clothing than they did two decades ago, only to keep them for half as long. I joined UCSB’s Research Mentorship Program wanting to explore where AI and UX could shift that behavior, and reimagine how design could orient people towards wanting less, to consume less.
As an avid fashion lover, I wanted to explore how UX design solutions could shift fashion from an industry of overconsumption back to a way of self-expression.
The insight that shaped everything downstream: the habit that matters most doesn’t happen when you’re reflecting on your closet—it happens right before you buy something new.
Solution
Designing an ambient purchase intercept that builds a wardrobe worth keeping
I built Conscious’s high-fidelity prototype in Figma, later connected to Gemini’s multimodal API for closet detection.
01. Digital Wardrobe Upload
Users upload photos of their garments, automatically categorized and sorted by Gemini's computer vision — building the closet everything else gets measured against.
02. Detection & Trigger
Any cart or checkout, on any retailer, is caught by the browser extension. iPhone 14 Pro+ surfaces an ambient Live Activity; every other device gets a standard push notification.
03. Emotional Check-In
Tapping in opens a short modal that requires a real response before moving forward — not something you can swipe away like the trigger itself.
04. Closet-Match, or Treat Yourself
If something comparable is already owned, its real $/wear sets the bar. If there's nothing honest to compare it to, the flow stays light instead of forcing the math.
05. Decision & Impact
Buy, wishlist, or skip — each choice updates a running dashboard of what's been saved, skipped, and kept in rotation.
Problem
The habit that matters most happens before checkout.
By the time you’re organizing your closet, the purchase has already happened. The moment that actually shapes a sustainable wardrobe is the one right before you buy—not guilt afterward, just a beat of reflection. I wanted to design something that interrupts that impulse, so what you keep buying starts building a wardrobe that’s more sustainable, and more distinctly yours.
Research
Current platforms make discovering your style difficult…
Cognitive Overload
Managing your wardrobe, finding outfits, and tracking purchases all live in separate, disjointed tools.
Lack of Personalization
Existing recommendation systems often miss personal context — users' values, cultural preferences, or motivations for shopping.
Trend-Driven Systems
Most apps push new products instead of encouraging users to rewear what they already own.
Reflection Comes Too Late
Every existing tool waits until after checkout to ask you to reflect. None of them show up in the moment that actually decides what you own.
How might we help users develop a personal, lasting relationship with their clothes—using AI and UX to make sustainability intuitive?
The Ideation Process!
Using the pain points gathered, I sketched early flows focused on reconnection—how users could rediscover what they already owned.
Sketched low-fidelity wireframes to explore flows for digital wardrobe upload, AI outfit recommendations, and style onboarding—including an early chat-based stylist concept. Testing kept surfacing the same gap: reflection only worked if it showed up right at the moment of buying, not after. That pushed the design toward an ambient, cross-retailer purchase intercept instead of a stylist interface.
Core User Flow
The full flow, mapped in FigJam — drag to pan and scroll to zoom.
Usability Testing
Three rounds of testing with 12 participants, surveyed against 70+ people.
I conducted three rounds of usability testing to refine both flow and tone. Metrics measured: task success, error rate, and perceived helpfulness. This round of testing ran against the original prototype—wardrobe upload, stylist chat, and an adaptive homepage—before the design shifted toward the purchase-intercept direction above, which hasn’t been through usability testing yet.
Average Usability Score (out of 5)
3.62
Round 1
3.95
Round 2
4.48
Round 3
User Insights
Clarify before recommending
Users preferred when the AI asked clarifying questions before suggesting outfits.
Favorites build attachment
A "Favorites" section increased attachment to clothing items.
Helpfulness resonated most with non-fashion-savvy users
Participants who self-identified as not fashion-savvy rated the app most helpful — average usability score of 4.48/5 after revisions.
Takeaways
My takeaways
01.
Simplicity is persuasive
Creative solutions can still be possible with simple, scalable designs.
02.
Conversational UX matters
The AI’s tone directly shapes user trust and satisfaction.
03.
Designing for sustainability is designing for emotion
People keep what they feel connected to.
Outcomes
What Conscious proved out
After three rounds of revisions, the original prototype reached an average usability score of 4.48 / 5, with the strongest response coming from users who didn’t consider themselves fashion-savvy— exactly the audience the project set out to reach. The purchase-intercept direction is the next thing to put in front of users. Built and tested as part of UCSB’s Research Mentorship Program.