@inproceedings{he-etal-2026-colearn,
title = "{C}o{L}earn: An Agentic Tutor that Learns its Learner in a Human{--}{AI} Co-Learning Loop",
author = "He, Kailai and
Wu, Zhihao and
Zhang, Linhai and
Zhao, Runcong and
He, Yulan and
Li, Jiazheng",
editor = "Agrawal, Sweta and
Abad, Alberto",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-demos.6/",
pages = "56--65",
ISBN = "979-8-89176-461-3",
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 ob- servation function; (ii) adaptive question generation that targets the learner{'}s weakest topic and recurring misconceptions; and (iii) an evi- dence 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."
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<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 ob- servation function; (ii) adaptive question generation that targets the learner’s weakest topic and recurring misconceptions; and (iii) an evi- dence 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.</abstract>
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%0 Conference Proceedings
%T CoLearn: An Agentic Tutor that Learns its Learner in a Human–AI Co-Learning Loop
%A He, Kailai
%A Wu, Zhihao
%A Zhang, Linhai
%A Zhao, Runcong
%A He, Yulan
%A Li, Jiazheng
%Y Agrawal, Sweta
%Y Abad, Alberto
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-461-3
%F he-etal-2026-colearn
%X 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 ob- servation function; (ii) adaptive question generation that targets the learner’s weakest topic and recurring misconceptions; and (iii) an evi- dence 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.
%U https://aclanthology.org/2026.emnlp-demos.6/
%P 56-65
Markdown (Informal)
[CoLearn: An Agentic Tutor that Learns its Learner in a Human–AI Co-Learning Loop](https://aclanthology.org/2026.emnlp-demos.6/) (He et al., EMNLP 2026)
ACL