Low-Fidelity Prototype in Figma

As a part of course work, I created user stories, personas, wireframes and a low-fidelity prototype in Figma for a fictional app. I chose to create an application for museum attendees to select and book exhibits before going to the museum, with an emphasis on allow users to peruse options that fit their needs through application of filters.

Identifying the User

Initial research and audits of museum websites identified 4 key user pain points:

  • Users want:

    • an overview of details regarding estimated time to go through exhibit, age appropriateness and if there were interactive opportunities

    • ability to schedule entrance time to avoid long lines or wait times

    • ability to purchase tickets prior to going to ease entrance

    • Easy access to general museum information including accessibility and parking

Through this process, I was able to create a user persona named, Rebecca.

Storyboarding

Big picture and close-up story boards identified the use of the application in the user’s daily flow and began to shape how a user would flow through the app. For the purposes of this project, I focused on:

  • Creating an easy way for busy professionals to book exhibits based on their needs (i.e. children, strollers, sensory friendly, etc.)

  • Allowing users to select tickets and entry times to plan their visit to the museum

Wire Framing

Paper Wire Frames

Digital Wire Frames

From there, I completed initial sketches on paper and then moved into Figma to build the concepts, focusing on creating an intuitive user flow.

Built low-fidelity prototype with connections from Figma.

Once the wire frames were roughly solidified, I incorporated the interactions users would take to flow through the app. I then tested this with prospective users using a moderated usability study and iterated on my designs using the feedback from these users.

This design is now ready to move to a high-fidelity prototype design with the incorporation of visual and interactive elements.

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Mockups and Prototype for a Mobile App

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Low-Fidelity Clinical Decision Making Tree