SharpenBox

Scarborough Health Network

May - Aug. 2026

A messenger for feedback from healthcare instructor to student

Design: Emily Chan

Tech: Kevin Shen, Nico To

Research: Angelica Roxas, Jana Kalbasi

Doctors are our experts

You trust them, through your understanding of their training and education

Only 1/4 medical residents reported
receiving regular feedback

POV you have to grade 10+ students live…

but it's physically impossible to be in 10 places at once

POV you have to grade 10+ students live…

but it's physically impossible to be in 10 places at once

As a result…

Healthcare grades change depending on variance

⤷ Mistakes are repeated and don't get corrected

⤷ Students graduate with gaps in their medical skills

My first time meeting the box:

When I first joined the team:

When I first joined the team:

  • software requirements

  • hardware

  • branding guidelines

  • software requirements

  • hardware

  • branding guidelines

were already set

were already set

So we have the AV equipment, and an ethics compliant AI model

But how is feedback going to move based on our user needs?

Future proofing with modifiable and reusable components in mind

(If you're interested in learning how SharpenBox was later reused for a virtual patient validation study ask me!)

Competitive Analysis

What does a video based feedback report look like?

Iterations

What is the breadth of the report?

Option 1

Birds eye view

Instructors are the main audience, receiving a topdown report following official medical procedure checklists of student performance

Option 2

Sectioned and automated

Instructors are the inbetween step, providing approval for student next steps. Students receive the top 3 actions to improve.

Key Decision

Video and text insights that are connected

Text insights with proof > segmented clips with subtitles

Browse through key insights, with a clickable timestamp linked as proof.

I advocated for this format of timestamps instead of the client's request for individually segmented clips. The output as a mixture of text and video would be quicker to catch the AI model's mistakes and fit within the 4 month deadline.

document style editing > guided builder

typing "#:##" automatically turns into a timestamp, "——" creates a new card.

Since the client's main priority for MVP was the feedback report, I chose this to keep editing capabilities easy to develop and customizeable.

Key Decision

Making it as asynchronous as possible

Instructors

Autoassign as a safeguard

Automatically analyze videos and assign feedback to students after a set time period. For instructors who are extra busy.

Students

Assigned to me > manual sharing

the instructor selects 3 aspects that instantly show up on a student's dashboard. Sending feedback to students is a highly encouraged step.

Final Designs

Introducing SharpenBox

Instructors

Interact with your student's performance straight from your inbox

Students

Proof of your performance sent straight to you

A messenger for feedback from instructor to student.

Reflection

Building for research funding

As a designer, I came into this project thinking about the simplicity of the product, and cutting down for the timeline first. I wasn't sure why so much tech was needed for an MVP.

At first I suggested simpler alternatives, like a text based meeting notetaker. I was constantly thinking about the impact vs. the effort.

However, with AI validation as a huge driver for the research team, a product for pitches would help shed communications and marketing onto the Research Institute for funding and publicity.

Curious and curiouser.

Curious and curiouser.