DraftTrace records the writing process and attributes every character to its origin — typed, AI-assisted, cited, or uncited. Evidence an instructor can defend and a student can stand behind.
Native Canvas integration. A research project of the CoRAL Lab at Arizona State University.
Every major detector guesses “Is this AI?” from the finished text alone. The guess is unreliable, and it falls hardest on the writers least able to contest it.
false-positive rate of AI detectors on essays by non-native English writers — and it dropped once those writers used AI to polish their vocabulary.Liang et al., Stanford · Patterns (2023)
Three structural flaws follow: detectors infer from a finished artifact (trivially gamed by paraphrasers), they output a probability, not an audit trail, and they penalize the constrained while rewarding hidden AI.
Instead of inferring AI from the finished text, DraftTrace records the writing process — keystrokes, pastes, edits, and in-app AI assistance — and attributes every character to its real origin. The evidence is recorded, not inferred: an auditable trail, grounded in what the student actually did.
One submission, presented four ways — from a glance to the full replay. Everything an instructor sees is a reading of the same provenance record.
Composition at a glance — how much was typed, AI-assisted, cited, and uncited. The clean read you grade from.
The full text, colored character by character. You see exactly which sentences came from where — in the text itself.
Watch the draft get built, keystroke by keystroke. The moment a 400-word block appears at once is visible — not inferred.
Where this draft’s process sits against the rest of the class, per category. No competitor surfaces class-level process comparison.
DraftTrace summarizes each submission into a four-category advisory signal so an instructor can triage a class fast. Unlike a detector’s flag, it’s never the end of the conversation — it opens directly onto the evidence that justifies it.
Instructor-only and advisory: a reading of the record, never a standalone score a decision rests on. The evidence is.
A draft built largely with the sanctioned in-app assistant — and properly attributed — is a legitimate, well-disclosed submission. DraftTrace is the only system that can represent AI use as legitimate and attributed, distinct from hidden uncited paste.
DraftTrace is a research project of the CoRAL Lab at ASU, presented at the ACL Birds-of-a-Feather session “NLP for Education and Workforce Readiness,” July 2026. Two studies are underway: a ground-truth provenance benchmark against seven baseline detectors, and a comparative trust study with graduate-student graders.
Honest scope. DraftTrace reads only what happens in its own editor — text from an unmonitored source is captured as uncited paste, but its true origin is unknown. It does no web-scale matching, and the verdict layer is research-stage. The point isn’t to stop every workaround; it’s to raise the bar and make the record defensible.
DraftTrace is being evaluated with instructors in real courses. Watch the walkthrough, and if you’d like to follow the research or discuss a pilot, tell us.
Native Canvas integration (LTI). Presenting at ACL BOF — July 2026.