Trace
UXL Design-a-thon Competition
Trace is a desktop plugin that rebuilds trust in digital art by verifying the creative process through action tracking. Built in 36 hours, placed in finals, and awarded Best User Research at UXL Laurier, our solution gives artists flexible control over transparency with a 2-tier system and seamless OS integration. I led user research, user flows, and UX/UI design prototyping for the creator experience.
Problem
As AI-generated art becomes increasingly sophisticated, digital artists face a growing crisis of authenticity. Including insights from Reddit discussions, many creatives find their work mistakenly flagged as AI-generated, while AI art is often passed off as original.
Solution
A desktop plugin that verifies digital art by tracking real creative actions instead of screenshots, giving artists flexible transparency control through a lightweight, workflow-friendly interface.
Results
Placed in finals and awarded Best User Research.
RESEARCH QUESTION
In an era where AI blurs the lines of artistic authorship, we asked:
How might we use technology to rebuild trust and deepen connections between digital artists and global audiences in an era where authenticity is constantly in question?
FEATURE DEMO
Real-Time Action Tracker
Continuously logs creative actions like clicks, keystrokes, and tool usage with timestamps. Screen recording is optional.
Seamless OS Bar Access
An always-on desktop icon lets creators track real-time status, with changing icons representing different states.
Intuitive Recording Controls
Dropdown menu lets users manage recordings, mark timestamps, and adjust settings mid-creation.
DESIGN PROCESS
My design process started with secondary research and community insights to deeply understand the trust gap in digital art. I mapped user flows, ideated flexible verification features, and prototyped a seamless tool that integrates directly into artists’ workflows. If given more time, my next step would be to test the prototype with real digital artists to validate usability and refine the system further.
RESEARCH & IDEATION
Our research revealed a growing trust issue in digital art: 43% of people in our survey couldn’t confidently distinguish AI-generated art from human-made work, leading to real harm for artists whose creations are misidentified or dismissed. To address this, we brainstormed features that could capture and verify the creative process without disrupting the artist’s flow.
I mapped these ideas onto a journey map to visualize how verification might fit naturally into an artist’s workflow. Drawing from tools I use daily, like Photoshop’s History panel, Blender’s Scene Collection, and passive screen recording software, I aimed to design a system that felt familiar, seamless, and respectful of artist autonomy.
ITERATION
I mapped out the artist’s journey and found early concepts too rigid for real-world use. I refined the flow with optional screen recording and tiered transparency, giving artists more control over how their work is verified.
WHAT DID I LEARN?
Our team was awarded Best User Research at UXL Wander, recognized for the depth of our investigation into artists’ needs and AI-usage transparency. While we originally aimed for a top 3 placement, the experience helped us sharpen our problem-solving approach: redefining our solution around actionable tracking, designing a clearer 3-tier transparency model, and strengthening our user flows through mentorship from industry designers. Ultimately, the project delivered a validated concept and a significantly stronger understanding of how to design for real-world feasibility.