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FOR HIGHER-ED CTLs

AI quizzes your faculty can defend.

Built for the day a student appeals a grade. Every question carries the literal source paragraph it came from — so the instructor points at the reading, not at a black-box prompt.

Updated May 18, 2026 · Methodology verified on 15 questions across 3 disciplines · Full numbers below

Q1单项选择中等

QuizCraft 生成的题目和普通聊天模型写的题目有什么本质区别?

  1. 生成速度更快
  2. 每道题都锚定在源材料中的具体段落
  3. 支持判断题
  4. 可导出到 Microsoft Word

01

The problem on your campus right now

Faculty are using ChatGPT to generate quizzes. You know it. The committee knows it. Nobody's saying it out loud because nobody wants the conversation that comes next when a student appeals a grade and the instructor can't explain where a question came from.

You have three options:

  1. Ban it — futile, faculty will use it anyway, and you lose visibility.
  2. Wait for the LMS vendor to ship something — Canvas / Blackboard roadmaps are 18 months out, and what they ship will be black-box.
  3. Adopt a tool that bakes traceability in from day one.

QuizCraft is the third option.

02

What QuizCraft does differently

Every question QuizCraft generates is anchored to a specific paragraph in the source material the instructor uploaded. Not a vague topic match — the literal sentence the answer comes from, included with the question and exported alongside it.

  • The instructor reviews citations before publishing — catches the occasional bad question in seconds, not after a student complaint.
  • The exported QTI (Canvas / Blackboard / Moodle / Brightspace) includes citation metadata — so a year from now, an integrity review can trace any question back to its source.
  • No source material is ever used to train AI — written into the contract, not just the Privacy page.
03

Internally verified accuracy (n=15, mixed disciplines)

Citation retrievability
quote actually exists in source
100%
zero hallucinated quotes
Distractor quality
no “longest-answer-correct” tells
100%
Answer fully justified by cited quote alone
87%
remainder require minor inference like a real teacher would

Eval methodology and full numbers below. We re-run this eval before every prompt change.

04

What it's not

  • Not vendor-locked — instructor library stays exportable forever, even after the contract ends. If the pilot ends, we hand back every file and delete the rest.
  • Not a proctoring tool — no surveillance, no student data.
  • Not a black-box AI — every output is reviewable and editable by the instructor before students see it.
  • Not an LMS — works alongside Canvas / Moodle / Brightspace / Blackboard.
05

How CTL adoption typically works

Week 1: Pilot

  • 25-50 faculty get individual seats
  • We run a 60-minute CTL workshop (recorded; share with anyone)
  • Faculty start with their actual upcoming quizzes

Week 4: Check-in

  • Review faculty usage data with CTL director
  • Identify any integrity concerns or workflow gaps
  • Course-correct on prompts / training

Week 12: Expand or end

  • Most pilots expand to department-wide
  • If not, instructor library exports + we delete the data — clean exit
06

Pricing

Pilot
$59/seat/year × 25
$1,475/year
Initial faculty cohort
Department
$49/seat/year × 100
$4,900/year
Single dept rollout
Institution-wide
Custom
typically $35–$45/seat at 250+
Full faculty deployment

All tiers: invoiced annually, NET-30 terms, no individual credit cards required.

07

Why bother talking now (not next semester)

The window where “early adopter of responsible AI assessment” is a positioning win for a CTL is shrinking. By Fall 2026, every LMS vendor will be marketing AI features and the conversation shifts to “which vendor's AI is best”, not “is your CTL leading on this?”

A 25-seat pilot is a $1,475 budget line and a 4-week commitment to findings. It doesn't require IT approval (Google SSO only, no SAML needed for pilot), doesn't trigger procurement, and gives your CTL concrete data for the harder conversations coming next year.

08

Methodology — how the citation numbers are measured

We don't want you to take the 100% number on faith. Here is the exact procedure:

  1. Three hand-curated teaching passages (intro-college level) across macroeconomics, US history, and software engineering. Each ~300-400 words, dense enough to mirror real reading assignments.
  2. 5 questions generated per passage (MCQ + True/False + Fill-in-the-blank), totalling n=15 questions.
  3. Programmatic check #1 — quote retrievability.For each question, we look for the cited source quote in the original passage. Exact substring match, or fallback to >80% word overlap (for paraphrased citations).
  4. Programmatic check #2 — distractor balance.For MCQs, flags “correct answer is conspicuously the longest” (a notorious AI-quiz tell) and lazy distractors like “all of the above.”
  5. Claude judge — answer support.An independent Claude call evaluates whether the cited quote actually semantically justifies the answer. Strict: a teacher should be able to point at the quote and say “see, the answer is here.”

Results above are from the most recent methodology run. We're happy to share the full eval — script and all — by email, including which specific questions fell in the 13% “partial” bucket. Transparency on what we're NOT claiming matters more than the headline.

09

Want to see it on your material?

The fastest two paths:

  • Try it yourself in 60 seconds, no signup: usequizcraft.com/try — runs against a fixed sample reading; see citations populate inline.
  • Send us a PDF from one of your courses — we generate a quiz from your actual material and walk you through the citation flow in a 15-minute call. Email yaoyanweb@gmail.com.
10

Who's behind this

QuizCraft is built by Connor Yao, a solo engineer who's spent six months making AI quiz generation actually defensible — not just fast. Direct line: yaoyanweb@gmail.com. Every CTL inquiry gets a personal reply within 24 hours.

Talk to us about a pilot