@inproceedings{wang-etal-2026-llm,
title = "{LLM}-{KT}: Enhancing Large Language Models with Knowledge Tracing via Multi-Level Plug-and-Play Alignment",
author = "Wang, Ziwei and
Zhou, Jie and
Chen, Qin and
Jiang, Bo and
Bai, Qingchun and
Dou, Liang and
He, Liang",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1781/",
pages = "35775--35792",
ISBN = "979-8-89176-395-1",
abstract = "Knowledge Tracing (KT) is a pivotal task in personalized education, aiming to predict students' future performance based on their historical interactions. While prior work has focused on learning behavioral sequences using question IDs or surface-level textual features, these methods often fail to capture complex behavioral patterns due to a lack of deep reasoning capabilities and world knowledge. To address this, we propose LLM-KT, a novel framework that integrates the reasoning power of Large Language Models (LLMs) with the sequential modeling strengths of traditional KT methods via multi-level plug-and-play alignment. Specifically, for task-level alignment, we design a plug-and-play instruction to leverage the rich knowledge and reasoning capacity of LLMs for the KT objective. For modality-level alignment, we introduce two mechanisms to integrate representations learned by traditional methods: (1) a Semantic History Projector that flexibly inserts compressed context embeddings into LLMs using question- and concept-specific tokens to capture long-term history; and (2) a Behavioral Dynamics Projector that enhances LLMs with sequential interaction patterns via a sequence adapter. Extensive experiments on four standard datasets demonstrate that LLM-KT achieves state-of-the-art performance, significantly outperforming over 20 competitive baselines."
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<abstract>Knowledge Tracing (KT) is a pivotal task in personalized education, aiming to predict students’ future performance based on their historical interactions. While prior work has focused on learning behavioral sequences using question IDs or surface-level textual features, these methods often fail to capture complex behavioral patterns due to a lack of deep reasoning capabilities and world knowledge. To address this, we propose LLM-KT, a novel framework that integrates the reasoning power of Large Language Models (LLMs) with the sequential modeling strengths of traditional KT methods via multi-level plug-and-play alignment. Specifically, for task-level alignment, we design a plug-and-play instruction to leverage the rich knowledge and reasoning capacity of LLMs for the KT objective. For modality-level alignment, we introduce two mechanisms to integrate representations learned by traditional methods: (1) a Semantic History Projector that flexibly inserts compressed context embeddings into LLMs using question- and concept-specific tokens to capture long-term history; and (2) a Behavioral Dynamics Projector that enhances LLMs with sequential interaction patterns via a sequence adapter. Extensive experiments on four standard datasets demonstrate that LLM-KT achieves state-of-the-art performance, significantly outperforming over 20 competitive baselines.</abstract>
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%0 Conference Proceedings
%T LLM-KT: Enhancing Large Language Models with Knowledge Tracing via Multi-Level Plug-and-Play Alignment
%A Wang, Ziwei
%A Zhou, Jie
%A Chen, Qin
%A Jiang, Bo
%A Bai, Qingchun
%A Dou, Liang
%A He, Liang
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F wang-etal-2026-llm
%X Knowledge Tracing (KT) is a pivotal task in personalized education, aiming to predict students’ future performance based on their historical interactions. While prior work has focused on learning behavioral sequences using question IDs or surface-level textual features, these methods often fail to capture complex behavioral patterns due to a lack of deep reasoning capabilities and world knowledge. To address this, we propose LLM-KT, a novel framework that integrates the reasoning power of Large Language Models (LLMs) with the sequential modeling strengths of traditional KT methods via multi-level plug-and-play alignment. Specifically, for task-level alignment, we design a plug-and-play instruction to leverage the rich knowledge and reasoning capacity of LLMs for the KT objective. For modality-level alignment, we introduce two mechanisms to integrate representations learned by traditional methods: (1) a Semantic History Projector that flexibly inserts compressed context embeddings into LLMs using question- and concept-specific tokens to capture long-term history; and (2) a Behavioral Dynamics Projector that enhances LLMs with sequential interaction patterns via a sequence adapter. Extensive experiments on four standard datasets demonstrate that LLM-KT achieves state-of-the-art performance, significantly outperforming over 20 competitive baselines.
%U https://aclanthology.org/2026.findings-acl.1781/
%P 35775-35792
Markdown (Informal)
[LLM-KT: Enhancing Large Language Models with Knowledge Tracing via Multi-Level Plug-and-Play Alignment](https://aclanthology.org/2026.findings-acl.1781/) (Wang et al., Findings 2026)
ACL
- Ziwei Wang, Jie Zhou, Qin Chen, Bo Jiang, Qingchun Bai, Liang Dou, and Liang He. 2026. LLM-KT: Enhancing Large Language Models with Knowledge Tracing via Multi-Level Plug-and-Play Alignment. In Findings of the Association for Computational Linguistics: ACL 2026, pages 35775–35792, San Diego, California, United States. Association for Computational Linguistics.