The paper introduces an interpretable, difficulty-aware knowledge tracing framework for tutor‑student dialogues that leverages large language models. It explicitly models student ability and question difficulty at each turn, using the original question text and the next tutor‑posed task to estimate knowledge state. By integrating Item Response Theory, the framework translates LLM outputs into cognitive‑theory‑based parameters, achieving superior performance over existing baselines while producing interpretable predictions.
By Shuyan Huang, Alexander Scarlatos, Jaewook Lee, Andrew Lan
The paper introduces Knowledge Tracing Leveraging Problem‑Solving Process (KT‑PSP), a method that incorporates students’ problem‑solving steps to model mathematical proficiency more comprehensively than traditional knowledge tracing. It presents the KT‑PSP‑25 dataset and a new framework, StatusKT, which uses a teacher‑student‑teacher LLM pipeline to extract proficiency indicators, generate responses, and evaluate mastery. Experiments show that StatusKT improves prediction accuracy and offers interpretable explanations by explicitly modeling proficiency.
By Jungyang Park, Suho Kang, Jaewoo Park, Jaehong Kim, Jaewoo Shin, Seonjoon Park, Youngjae Yu
arXiv:2509.05346v3 Announce Type: replace
Abstract: While large language models (LLMs) are increasingly being adopted to support personalized learning, there remains limited understanding of how thei...
By Bo Yuan, Jiazi Hu
The paper explores using semantic text embeddings to automatically build educational taxonomies by clustering math question embeddings. It compares these embedding‑based taxonomies to existing Common Core State Standards (CCSS) taxonomies in two ASSISTments datasets, finding substantial but incomplete overlap. When applied to Deep Knowledge Tracing, the new taxonomies improve predictive performance by about 3‑5 AUC percentage points.
By Kia Karbasi, Kaveh Karbasi, Gregory Pottie
arXiv:2610.19532v1 Announce Type: cross
Abstract: Existing student models in educational applications largely focus on student responses to questions. However, as tutoring becomes more accessible at...
By Zhangqi Duan, Simon Woodhead, Maya Miller-Vedam, Andrew Lan
The paper introduces TIZSKT, a zero‑shot cross‑domain knowledge tracing model that separates behavioral sequence modeling from domain‑local historical evidence construction. It uses text as an index via a frozen text encoder and unsupervised per‑domain projection to build question addresses, then aggregates a student’s past outcomes into a question‑specific historical correctness summary. Experiments on three public datasets show that TIZSKT outperforms baselines in mean AUC and accuracy, with ablations confirming the importance of text addressing and cross‑segment continuation.
By Yin Yi, Haiping Ma, Siyu Song, Ziwen Wang, Shangshang Yang