Prediction Is Not Simulation: Process Contracts for Auditing and Repairing Knowledge-Tracing Models

Knowledge-tracing models are selected as next-response predictors but reused as simulators for instructional decisions. Predictive fidelity on logged data does not identify how a model changes state under a controlled curriculum. This paper introduces process contracts as a second validation axis. They specify directional or practically null responses to matched trajectory transformations. The matched audit swaps early concept blocks while fixing the learner prefix, item-response multiset, exposure dose, delayed suffix, and probes. A contract passes only when its whole 95% interval lies on the prescribed side of zero or inside a predeclared ±0.01 band. On ASSISTments 2009 and a four-regime simulator, process contracts expose transition semantics that vary with architecture, seed, and logging policy and that test AUC does not determine. In a fresh 600-checkpoint matrix, the oracle and DKT pass all 24 endpoint contracts, whereas SAKT passes 16. Four of its eight failures occur at a mean AUC of at least 0.85. The paper also introduces Process-Contrastive Regularization (PCR), a hinge penalty added to the factual cross-entropy. The penalty pushes matched learner-level effects past a margin or into the band and uses item identities that the audit never scores. The promotion gate requires a matched test-AUC loss of at most 0.01 and strictly fewer failed contracts than the matched baseline. PCR passes this gate in an exploratory single-seed pilot (from 4/16 to 1/16) and for SAKT across fresh seeds under three logging policies. The gate refuses PCR trained against a misspecified contract.