SATLens

Inspiration

For us personally, we've been studying for the SAT and struggled to find adequate resources and information to study effectively. We created SATLens because we knew future students would face the same problem, and we wanted to build a solution that could make SAT preparation more personalized and effective.

What it does

SATLens analyzes practice data to identify weaknesses and recurring mistake patterns. It explains the issue, recommends a strategy, and generates a targeted question to test whether the student improves.

How we built it

We built SATLens with Next.js, TypeScript, and Tailwind CSS. The app analyzes structured practice data including accuracy, skills, subskills, answer choices, and time spent, then turns those patterns into personalized insights.

Challenges we ran into

Our biggest challenge was preventing generic or incorrect diagnoses. We focused on making feedback evidence-based instead of guessing why a student got something wrong.

Accomplishments that we're proud of

We built a complete personalized learning loop that goes beyond tracking scores: Data → Diagnosis → Explanation → Targeted Question → Progress

What we learned

We learned that effective AI education isn't just about generating answers. The real value comes from identifying patterns in how a student learns and makes mistakes.

What's next for SATLens

We want to support larger datasets, improve diagnostic accuracy, create more adaptive practice, and explore integrations with supported SAT practice platforms.