The AI Authorship Trap: A Zero-Trust Competence Attestation Protocol for Generative-AI Education
Keywords:
Generative artificial intelligence, competency assessment, zero-trust architecture, verifiable credentials, academic integrity, learning analyticsAbstract
Generative artificial intelligence (GenAI) makes finished assignments increasingly unreliable as stand-alone evidence of what a learner can independently do. The resulting AI authorship trap arises when an institution treats evidence about the origin of a submission as a substitute for evidence about the learner's competence. AI-text detectors cannot resolve this problem reliably, while ordinary digital credentials typically certify an award rather than the evidentiary process that justified it. This design-science study develops the Competence Attestation Protocol (CAP), a zero-trust, privacy-conscious architecture that binds an assisted learning artifact to an authenticated learner, a fresh independent challenge, a delayed transfer task, accountable assessment, and a revocable competence claim. CAP separates provenance, assessment validity, and credential authenticity; specifies typed evidence records, challenge nonces, event invalidation, and issuance guards; and integrates established learning-platform, provenance, and credential standards without assuming that cryptographic integrity proves knowledge. Analytical threat walkthroughs assess replay, forgery, artifact tampering, unacknowledged AI use, and compromised assessment. A reproducible, abstract state-machine exploration reached 24 distinct states and checked all 312 state-event transitions across 13 event types; its four structural invariants held in this model. These are protocol-consistency findings, not student-learning effects or production security guarantees. The paper presents implementable interfaces, failure boundaries, equity safeguards, and a preregisterable evaluation design. Its central contribution is an alternative to detecting who wrote an answer: verifying what a learner can demonstrate when assistance is removed.
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