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arXiv:2610.08703v1 Announce Type: new Abstract: In K-12 mathematics tutoring, student-tutor dialogue provides rich evidence of learners' problem-solving processes and sources of difficulty. Learning...
arXiv:2609.13824v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to evaluate the responses of other language models. This approach, known as LLM-as-a-Judge, is faste...
arXiv:2607. 01247v1 Announce Type: cross Abstract: Open-ended mathematics exams are valuable because they assess reasoning, proof construction, algorithmic thinking, and communication of intermediate steps.
arXiv:2606. 06546v1 Announce Type: new Abstract: Evaluating large language models (LLMs) for education requires measuring how models teach, not only what they know.
TrussTutor-Bench is a benchmark of 200 verified two-dimensional truss problems with automatically generated student solutions, designed to evaluate large language models on solution accuracy, sycophancy, and error detection. Thirteen LLMs were tested, showing accuracy ranging from 64.5% to 97.0%, near-perfect error detection (90.4%–100%), and varying degrees of sycophancy (1.6%–50.0% of correct solutions rejected when a student doubted them). The benchmark highlights wide performance differences among models, especially in handling student doubt, and offers a verified, extensible standard for assessing technical reliability before deploying LLMs in educational settings.
The paper reports that fine‑tuned small language models (SLMs) can outperform large frontier models in annotating grammar concepts from learner–tutor transcripts. By training a 0.8B Qwen3.5 model on teacher‑generated data and embedding the annotation rules into adapter weights, the authors deploy an efficient end‑to‑end grammar mastery tracker that matches or exceeds the performance of 4B reference models and GPT‑5.4/5.6 on two human‑curated benchmarks. The lightweight model also cuts serving costs by about 16× and boosts learner engagement and key business metrics in an online experiment.