Provenance, Not Detection:
The TeacherAware Position

👤 Aaron Kushner
📅 2026 · 🏷 Writing Integrity · AI Provenance · Policy

Developed with: Claude (Anthropic)

The author declares no competing financial interest or funding from any commercial AI tool provider.

The point

Cheating was never impossible. It was difficult and risky — a ghostwriter cost money, a copied paper could be caught.
AI reversed the incentive. Easy, instant, undetectable — while the grade at stake stayed the same. Rational students respond rationally.
Detection cannot fix an incentive problem. It guesses at finished pages and loses the arms race — even its own vendors concede it.
TeacherAware re-reverses the reversal. Mandatory digital provenance-at-creation makes honest work the cheap path again — and cheating exponentially more difficult than it is today, with a simple convenient UI, pre-addressed privacy concerns, and a product that can grow into a full suite of AI-powered learning and teaching systems.

1. The detection dead end

Two-thirds of American students use AI for schoolwork. The tools built to catch them — humanizers, drip-typers, autotypers, agentic browsers that type for you — have crossed the threshold where "did this text come from AI?" is no longer operationally answerable. Even the vendors selling detection concede the race is a dead end: every improvement in the detector funds an improvement in the evader. Meanwhile the false positives land on honest students, disproportionately multilingual writers, and every accusation without evidence corrodes a classroom.

Schools have responded by retreating — oral exams, pen-and-paper, no take-home writing. That retreat abandons the slow, iterative practice that take-home work exists to build, and it abandons AI literacy in the same stroke, two years before those students need it professionally.

2. Change the question

A finished document cannot testify about its own origins. The composition process can. TeacherAware moves the question from "does this text look AI-generated?" — a judgment about a static artifact, losing — to "here is exactly how this text came to exist" — a record of a process, winnable, because the record is made while the process happens.

Students compose inside a sanctioned workspace. Every keystroke, paste, and citation placement is hashed into a tamper-evident chain as it happens; the running chain root is witnessed by the server during composition, so a session fabricated afterward cannot reproduce the roots the witness already stamped. At submission the server independently replays the whole chain. The work arrives carrying its own evidence.

3. The threat ladder, and the defense that climbs it

Each rung of cheating tooling meets a specific, mostly deterministic countermeasure:

Attack	Defense
Paste AI text / "humanized" text	The chain records the paste as a paste, with source classification. No detector needed — the event is simply visible.
Autotyper injects keystrokes while the student steps away	Deterministic absence detection: text produced while nobody is on camera is flagged, localized to the exact seconds it happened.
Transcribing from a phone or second device	Gaze awareness: eyes sustained off-screen while text is produced. Calibrated to each student's own rest position in the opening seconds of a session, so a low camera or a tilted head is baseline, not evidence.
AI assistant open in another window	Whole-screen capture (window/tab shares are rejected) with a two-stage review: stage 1 sees the AI window; stage 2 judges whether it was actually used — typed query, rendered response — and whether what it produced is about this assignment.
Human-cadence drip tools (the irreducible)	Keystroke-rhythm analysis today; a stylometric backstop on the roadmap — comparing the submission against the student's own corpus of previously proctored work, the one signal a perfect drip can't forge.

The architecture is an escalation ladder, not a dragnet: a cheap deterministic pass watches every session locally; the expensive AI review runs only on the moments the cheap pass flagged. A clean session never calls a model at all — which is both a cost property (a ten-hour essay costs the same as a ten-minute one) and a values property (nobody re-reviews a student who did nothing).

4. The privacy objection, answered structurally

Proctoring software has a deserved reputation problem. The answer is not a privacy policy; it is architecture that makes the abuses impossible:

Webcam frames are background-blurred at capture — the bedroom behind the student is never recorded.
Frames live encrypted in memory under a non-extractable session key, hash-sealed into the ledger. On a clean pass they are destroyed. Nobody — vendor included — can re-watch a clean session, because it no longer exists.
The permanent record is a frame-free timeline of events: "eyes off-screen 10s at 2:14 PM," never video.
Students under 13 never use the webcam. The screen tier alone still defeats most of the ladder.
The system opens by telling the student how to stay clean — a close-your-AI-windows nudge before capture ever starts. Advisory posture throughout: every output is evidence for a human teacher, never a verdict.

5. What this restores

With provenance in place, the teacher can assign take-home writing again — the assignment the AI era supposedly killed. The teacher can also sanction AI where it belongs: the same environment that records unauthorized AI use can host an authorized, constitutionally-constrained writing collaborator, used in the open, on the record. Integrity infrastructure and AI-literacy infrastructure turn out to be the same infrastructure.

This is not a proposal. The workspace, the chain, the citation library, the escalating proctor, and the teacher dashboard are running code as of July 2026 — the companion spec sheet inventories every shipped feature, and the go-to-market brief maps who it's for and how it reaches them.