2025
In a five-week NYC EdTech Hackathon, I contributed to the design of GradeSnap, an AI-assisted grading tool built to integrate with Google Classroom. Placed 5th overall; featured on the NYC EdTech Hackathon website.
Our goal was to explore how AI could reduce grading burden while preserving professional judgment.
Team: Cross-functional (engineering, UX, PM)
Role: Design Research & Interaction Design
Timeline: February–March 2025
Framing the Opportunity
Grading Is a Systemic Strain
Teachers report working ~54 hours per week.
Writing assignments, while critical, are among the most time-intensive tasks.
In interviews, one teacher shared:
“Sometimes I spend four hours grading just one class. I love teaching, but I’m exhausted.”
Existing tools organize assignments, but don’t meaningfully support feedback workflows.
We asked:
How might AI assist teachers without replacing their judgment?
Research Sprint
Understanding Workflow & Friction
In Week 1, we conducted:
5 semi-structured interviews with K–12 teachers
Competitive analysis of Gradescope, Crowdmark, and GradeMate
We mapped grading workflows and tensions:
Feedback fatigue
Cognitive load during rubric scoring
Weak Google Classroom integration across existing tools
Translating Insight Into Concept
GradeSnap
We prototyped an AI-assisted grading flow:
Sync assignment from Google Classroom
AI generates rubric-aligned draft feedback
Teacher reviews, edits, approves
Scores + feedback return in one click
The teacher remains the decision-maker.
AI functions as cognitive support.
Final Product
What This Reinforced
Speed doesn’t eliminate rigor.
AI tools must respect professional expertise
Systems thinking applies even in compressed timelines
This project reinforced my interest in designing tools that reduce cognitive load without removing human agency.
