Proctorseal
A submission-provenance layer that records participating tools, student disclosures, source segments, transformations, signatures, policy versions, review, correction, and academic decisions.
Institutions need to evaluate student use of AI without treating probabilistic detection as proof. Research confirms dominant detection infrastructure, institutional disablements after false-positive concerns, and a standard for signed content provenance, while no product was found that issues per-submission receipts mapping disclosed AI-assisted portions to participating tools. Proctorseal captures provenance only when a compatible tool or student supplies it. It cannot reconstruct undeclared assistance, prove that a human authored unsigned text, identify who typed, establish originality, or decide misconduct. A signature verifies issuer and retained manifest integrity, not the truth of every claim. Academic policy, accessibility, privacy, student response, and human review remain authoritative.
The academic integrity, teaching and learning, assessment, educational technology, or institutional policy leader seeking evidence beyond AI-detection scores.
Verifiable provenance is useful precisely because it must admit incomplete coverage and avoid turning absence into guilt.
Academic integrity and educational technology leaders have a clear evidence and policy problem.
The standard and detection backlash create a feasible alternative, but provenance systems are not newly possible.
The institutional buyer, detection false-positive problem, signed-provenance standard, and per-submission gap are concrete.
Provenance coverage depends on participating tools and workflow adoption, managed integration weakens leverage, the standard does not prove claim truth, and incumbents can add manifests.
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