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home / papers / 2026 / LLM-KT: Enhancing Large Language Models with Knowledge Tracing via Multi-Level Plug-and-Play Alignment

LLM-KT: Enhancing Large Language Models with Knowledge Tracing via Multi-Level Plug-and-Play Alignment

Paper · 2026 · Ziwei Wang, Jie Zhou, Qin Chen et al.

Summary

Knowledge Tracing (KT) estimates whether a learner will solve the next exercise from their earlier answers. Classic KT models handle sequences of question and concept IDs well but have no grasp of meaning. LLMs bring world knowledge and reasoning, but they treat IDs as arbitrary numbers and struggle with long textual histories. The authors propose LLM-KT, which combines the two through multi-level plug-and-play alignment.

At the task level, KT becomes a Yes/No generation problem. An instruction template contains special question and concept tokens, and their embeddings are swapped for learned vectors. At the modality level, two components feed those vectors:

  • a Semantic History Projector, which compresses question and concept texts into compact embeddings via a context adapter;
  • a Behavioral Dynamics Projector, which maps interaction representations from traditional KT models (DKT, AKT) into the LLM's space.

The two signals are fused for each token. With a LoRA-tuned LLaMA2 backbone, the framework sets a new state of the art on four public benchmarks against more than 20 baselines. This shows a practical way to merge LLMs with established educational sequence models.

Key points

  • The authors motivate the projectors with two observations: student histories can exceed 200 questions at roughly 77 tokens each, and plain fine-tuning of LLaMA (or prompting GPT-4o) gives limited gains.
  • Initializing the special tokens with DKT or AKT embeddings far outperforms randomly initialized fine-tuned token IDs (LLM-FT TokenID), with AKT gaining over 15 ACC points on Nips2020. DKT and AKT perform almost the same (average ACC 0.8295 vs 0.8302).
  • Among context encoders (BERT, all-mpnet-base-v2 / MPNET, LLaMA2), LLaMA2 is best on Assist2009 and Nips2020 (average AUC 0.8581, ACC 0.7865 vs BERT's 0.7659 ACC). The authors still recommend MPNET as a cost-effective choice of BERT size.
  • The way context and sequence vectors are fused (concatenation, averaging, addition) matters little; addition scores slightly highest.
  • Stated limitations include higher compute cost and latency than lightweight KT models, reliance on good question texts, noise in very long sequences, and weaker handling of unseen questions.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓East China Normal UniversityShanghai · China50 works · 2,174 citations

Also on the byline: Shanghai Open University.

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksInformation Systems · 1,437 worksComputer Science Applications · 102 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,320 works

Cite

Cite as 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. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.1781

@inproceedings{wang2026llm,
  title = {{LLM-KT: Enhancing Large Language Models with Knowledge Tracing via Multi-Level Plug-and-Play Alignment}},
  author = {Ziwei Wang, Jie Zhou, Qin Chen, Bo Jiang, Qingchun Bai, Liang Dou and Liang He},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1781},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1781}
}

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