Good tutoring adapts to the individual: it tracks what a learner knows, notices why they go wrong, and asks the next question that will help most.
Most deployed tutoring tools instead serve fixed item banks and treat a wrong answer as a single bit of signal.
We present CoLearn, an interactive, agentic tutor that supports an iterative tutoring loop: the learner practises, and the system builds an evidence-grounded memory of the learner's mastery and misconceptions.
This memory is updated as evidence accumulates and is used to generate the next personalised question.
CoLearn has three components:
- (i) a persistent learner-state memory that updates per-topic mastery with a soft-evidence variant of Bayesian Knowledge Tracing, where a large language model acts as a continuous observation function;
- (ii) adaptive question generation that targets the learner's weakest topic and recurring misconceptions;
- (iii) an evidence view that makes personalisation visible and testable through live progress visualisation and blind A/B comparison.
In blind A/B evaluation, questions conditioned on this memory are preferred over non-personalised ones 68-69% of the time, and in persona simulations with hidden ground-truth mastery the agent's belief converges toward the learner's true mastery.