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Dashboards, learner profiles, and adaptive recommenders in AI tutoring platforms rely on behavioral features computed from interaction logs, such as persistence after an error, and present them as learner properties. Yet the platform that produced the log also decided which next action could occur. We present a feature-provenance audit: a four-step procedure and open-source toolkit. It classifies logged transitions by delivery regime, estimates within-learner associations by regime, and tests whether fixed-sequence label permutations reproduce each association. It also decomposes learner-level scores into regime exposure and residual behavior. In simulations with known ground truth, the audit recovered learner effects that conventional estimates missed when regime labels were correct, and it exposed its failure when labels were wrong or when the platform rerouted learners outside recorded regimes. Applied to 100,000 EdNet learners, the audit showed that 61% of post-error same-skill continuations occurred within item bundles answered and submitted together. Permutations reproduced the post-error association there. Repetition of the same question carried the only error-contingent signal, and about two thirds occurred in matched adaptive re-offers. Bundle exposure explained 75% of variance in a conventional persistence score, which did not improve retention prediction. The threshold-based warning had low sensitivity on held-out simulations. Counting bundles rather than responses as learning opportunities in a knowledge-tracing model changed learning-rate estimates while leaving predictive accuracy nearly unchanged. We derive reading rules for dashboard designers and a logging-design recommendation under which delivery regimes would be directly observable.

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