What We Built
NumPath is an AI math tutor for children with dyscalculia. At its core is an adaptive engine that picks the next problem for each student based on their Bayesian Knowledge Tracing (BKT) mastery estimate. In this post I'll walk through a problem we had — and solved — in the rule-based phase: classified mistakes were being logged but completely ignored by the selection engine.
The fix was a 60-line change across two files. The research implication is significant.
The Problem: a diagnostic signal going nowhere
Our MistakeClassifier already tagged every wrong answer with a structured code — BORROW_SKIP when a student adds instead of subtracts with borrowing, DIGIT_REVERSAL when they write 51 for 15, MAGNITUDE_MISJUDGE when they pick the smaller number as larger. These MistakeEvent records were hitting the database on every incorrect attempt.
