The paper introduces Universal Textual Teaching (UTT), a parameter‑update‑free framework that distills knowledge gaps between a Teacher and Student large language model into a reusable natural‑language artifact called a Primer. UTT identifies representative gap cases, then iteratively refines the Primer through a cycle of student attempts, prompter‑generated teaching instructions, teacher demonstrations, and synthesizer consolidation. Experiments on math (Omni‑MATH‑2) and code generation (KernelBench) show significant accuracy gains, outperforming prompt engineering and parameter‑based knowledge distillation, and the Primer generalizes across different Teacher‑Student pairs.
By Zhanyi Lu, Huan Wang
SMRC is a new method that aligns large language models with student reasoning for mathematical error correction. It treats student reasoning as a multi‑step decision problem and uses Monte Carlo Tree Search to find optimal correction paths, while a breadth‑first search guided by the model generates reward signals that are back‑propagated to supervise intermediate steps. The authors also introduce the MSEB benchmark of 158 high‑school math problems and a dual evaluation protocol focusing on solution accuracy and correct‑step retention, showing that SMRC outperforms existing methods on several datasets.
By Biaojie Zeng, Min Zhang, Juan Zhou, Fengrui Liu, Ruiyang Huang, Yu Song, Xin Lin
Stepwise-Querying is a method that extracts step-by-step explanations from a black-box teacher model without needing its internal chain-of-thought. By independently querying the teacher for intermediate derivations, validity checks, and corrections for each step, the gathered explanations are used to fine-tune smaller student models. Experiments on mathematics and code benchmarks show that this approach outperforms three baselines in 14 of 16 comparisons, and the introduced Stepwise Explanation Information Gain metric helps diagnose the usefulness of intermediate reasoning.
By Dicong Yu, Wenhua Wang, Tian Wang
arXiv:2607. 13103v1 Announce Type: cross Abstract: Knowledge tracing (KT) aims to predict students' future performance by modeling their evolving knowledge states from historical interactions.
By Duantengchuan Li, Yingqian Bi, Jinsong Chen, Rui Zhang, Mingwen Tong
arXiv:2608.21668v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly used to simulate students at different mastery levels. These simulations can generate synthetic training...
By Yuan An, Emily Wang, Benjamin Wang, Ruhma Hashmi
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