CoLearn: An Agentic Tutor that Learns its Learner in a Human--AI Co-Learning Loop

2026-09-17

AI Summary

CoLearn系统通过迭代学习环路、贝叶斯知识追踪及自适应问题生成，解决个性化辅导问题，提高学习效率。

Abstract

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; and (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.

Problem

• tutoring
• learner adaptation
• personalized learning
• interactive AI

Innovation

• interactive agentic tutor
• evidence-grounded memory
• soft-evidence Bayesian Knowledge Tracing
• adaptive question generation
• personalisation visualization