Pet Embedder
An AI-powered tool to get more pets adopted.
Role: Lead UX designer, Visual Designer, and User Researcher for the Computational Outreach Research Lab at Virginia Tech
Developer and Lab Director: Ivan Hernanddez
Scope: 17 weeks
Tools: Figma and Qualtircs
Processes: User Research (Interviews & Surveys), Requirement Gathering, Wireframing, Lo-Fi and Hi-Fi Prototyping, Affinity Mapping, Visual Design, Usability Testing, Task Flows
In his research, Dr. Hernandez found that the most significant factor for potential adopters when searching for a pet is morphology. In other words, how a pet looks is the driving factor behind their search for a pet.
Pet Embedder is a tool powered by machine learning that streamlines the search for a pet in two steps:
Drag and drop a photo of a dog or cat that resembles the desired breed.
Scroll through the top morphological matches in pet shelters near the user. The closest matches will be shown at the top.
Visual Design, Wireframing, and User Research
At our first meeting, l gathered requirements from Ivan and started revising the visual design of the system to comply with W3C/WCAG and ADA web accessibility standards. After introducing design thinking to the team, I encouraged the lab to start with user research before making design decisions.
I explained the value of non-judgmental user research that seeks to understand the problem-identification phase of design thinking.
For this project, we used user interviews and surveys as our user research.
As Ivan laid out the goals of the project and required features, I began iterating through wireframes for the home page.
Usability Testing and User Research
As the lead UX designer, I have introduced a user-centered design approach to the lab and prioritized inclusivity at every step of the product development process.
Pet Embedder is currently in its first phase of usability testing with a focus on the user’s impressions and perceived usefulness of the product’s basic features and how well they’re integrated.