From Bayesian Knowledge Tracing to Verifiable Educational AI: A Design-Science Case Study in Cryptographic Attestation, Teacher Oversight, and EU AI Act Evidence Readiness

v0.3 artifact-backed working draft (17 May 2026) of From Bayesian Knowledge Tracing to Verifiable Educational AI. This design-science working paper uses MATx / matx-hack and EATF as a case study for representing adaptive educational AI events as canonical, hash-chained, replayable evidence. The paper focuses on Bayesian Knowledge Tracing state updates, task recommendations, teacher-facing explanations, and teacher approval or override events. Version v0.3 adds a self-contained architecture figure, an explicit signature/RFC 3161 timestamp integration path, executable replay and tamper-test results, package-size and verification-latency measurements, and a short teacher/auditor review protocol. The artifact package remains synthetic and hash-chain-only: it contains no real student data and makes no claim of production cryptographic assurance. The central claim is narrow: cryptographic attestation cannot prove pedagogical validity, fairness, privacy compliance, or legal conformity, but it can provide an integrity and replayability layer that makes selected governance claims independently checkable.