Prism: Making Student Thinking Visible

Prism uses AI to analyze student reasoning, reveal hidden misconceptions, and help teachers personalize instruction — turning every response into a window into understanding.

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Inspiration

Every teacher knows the quiet dread of an exit ticket: 30 answers, five minutes before the next class, and the same useless signal every time — a score.

“18 of 30 got it right” tells you how many were wrong. It never tells you how they were thinking.

The moment that started Prism was a fraction question. Ask a class whether ( \frac{4}{5} ) or ( \frac{5}{6} ) is bigger, and a huge number will confidently say:

“They’re equal — both are one piece away from a whole.”

That’s not a careless mistake. It’s a coherent, well-structured wrong idea — the student is reasoning, just from a rule that quietly breaks. A grade throws that reasoning away. A teacher who could see it would know exactly what to do next.

I wanted to build the instrument that makes that thinking visible — not another quiz generator, not an AI grader, but something that answers the question teachers actually ask:

“How is my class thinking right now?”

What It Does

Prism turns a two-minute written answer into a live map of how a class reasons.

  1. The teacher describes the lesson — subject, grade, learning objectives, and optionally curriculum context, pasted slides/notes, and what a previous assessment revealed.

  2. An Assessment Design Agent reasons about the lesson before writing anything. It works out:

  • the core concepts;
  • the specific ways real students in that grade misunderstand them, classified into seven pedagogical archetypes;
  • a single hinge question;
  • a “convince a skeptic” explanation prompt where each wrong answer reveals a different way of thinking.
  1. Students join by QR code or a six-letter code. They:
  • answer the multiple-choice hinge question;
  • write 2–4 sentences of reasoning;
  • indicate how sure they are;
  • optionally answer: “What made you think this?”
  1. An analysis engine reads every explanation — not for correctness, but for structure. It:
  • charitably parses answers across typos, slang, and other languages;
  • extracts verbatim claims;
  • rates the depth of reasoning;
  • matches reasoning against the lesson’s misconception set;
  • abstains when the evidence is genuinely ambiguous.
  1. The teacher dashboard becomes an observatory, featuring:
  • Learning Landscape — a constellation of the lesson’s concepts, glowing according to how the class understands each one;
  • Teacher Insight — three grounded paragraphs:
    • What does my class understand?
    • What is confusing them?
    • What should I do next?
  • a concrete five-minute teaching move;
  • reasoning-pattern clusters, each containing:
    • students’ own words;
    • a per-student thinking profile:
      • understands X;
      • connects Y incorrectly;
      • recommended next step.

The guiding principle appears at the bottom of every dashboard:

“Prism sorts. You diagnose.”

Prism never claims to know a student’s mind. It surfaces patterns with the evidence attached and leaves the judgment to the teacher.

How We Built It

Frontend & App

  • Next.js 16
    • App Router
    • after() for background analysis
  • React 19
  • Tailwind CSS v4
  • Deployed on Vercel

Data & Authentication

  • Supabase
    • PostgreSQL
    • Authentication
    • Row-Level Security (RLS)

Teachers receive owner-scoped isolation through RLS:

owner_id = auth.uid()

Built With

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