Blindspot

The problem

Teacher grading and feedback workload is a documented crisis, not a hunch — a third of US teachers have seriously considered leaving the profession over grading burden alone, and the research is clear on why specific, timely feedback breaks down: it simply doesn't scale to an entire class by hand.

But the deeper, less-discussed problem is a blind spot baked into grading itself, AI-assisted or not: grading checks answers, not reasoning. A 2026 study on AI tutoring identified this directly — nearly all misconception-detection research (academic and commercial) is triggered by a wrong answer, because that's the only signal current systems know how to use. When a student's answer happens to be correct, the system — and often the teacher, moving fast through a stack of papers — has no way to see that the method behind it was invalid and will fail on the next problem.

Inspiration

Most education software looks for misconceptions after a student gets an answer wrong. But a correct answer can conceal a more dangerous problem: a student may have used a rule that happened to work once and will fail on the next problem.

For example, a student can simplify 16/64 to 1/4 by crossing out the two sixes. The final answer is correct, but the operation is not. A conventional grader passes it; the student leaves believing that digit deletion is fraction reduction. Blindspot was built to find that hidden misunderstanding before it becomes visible as a wrong answer.

What it does

Blindspot reviews a student's worked fraction-simplification solution, not just its final answer. It determines whether the displayed method is mathematically valid in general.

When the final answer is correct but the method is fragile, Blindspot:

1. identifies the exact invalid operation in a diff-style review;
2. explains the valid underlying rule in educator-facing language;
3. generates a same-concept problem with different numbers; and
4. deterministically replays the flawed method to prove it now gives the wrong result.

Teachers can submit a new worked solution, keep it in a local review queue, or use Class review to inspect a group of students who share the same hidden misconception.

How we built it

Blindspot is a Next.js App Router application written in TypeScript and styled with Tailwind CSS. It uses the official OpenAI JavaScript SDK and the Responses API with GPT-5.6.

The review route uses system-level instructions plus Zod-backed Structured Outputs to return a schema-safe validity result. The stress-test route asks GPT-5.6 for an adversarial candidate, then validates it with deterministic fraction arithmetic before rendering it. The model proposes; the verification layer decides whether there is proof.

Challenges we ran into

The main challenge was avoiding a persuasive-looking but unproven AI explanation. A generated counterexample is only useful if the student's exact flawed operation actually breaks on it. We added a deterministic verification gate and reject live candidates that do not differ from the true reduced fraction.

We also had to make the product understandable in seconds. The interface now leads with the causal chain—correct answer, method review, counterexample—rather than hiding the most important proof behind a dashboard.

Accomplishments that we're proud of

• Built a feedback workflow for misconceptions hidden behind correct answers.
• Made the core claim testable: every displayed stress test is verified by deterministic arithmetic.
• Combined free-form AI review with strict structured outputs and local mathematical checks.
• Delivered an educator-facing class review that turns individual findings into a reteach group.

What we learned

Correctness and understanding are different signals. A model can make feedback feel intelligent, but trust comes from constraining the model with a verification layer that users can understand.

We also learned that the strongest education interaction is often a counterfactual: not merely "this method is wrong," but "apply it once more and watch exactly where it breaks."

What's next for Blindspot

We are extending the reasoning stress-test engine beyond digit cancellation to additional fraction misconceptions, then to algebra, proportional reasoning, and introductory programming. The longer-term goal is a teacher review layer that surfaces fragile understanding across a class before it turns into failure—while retaining evidence for every intervention.

Built With

• codex
• gpt-5.6
• nextjs
• openai
• react