The results support truncated CoT auditing as a lightweight process-level diagnostic for answer-driven reasoning in math tutoring explanations and support truncated CoT auditing as a lightweight process-level diagnostic for answer-driven reasoning in math tutoring explanations.
Programming tutors should support learners'own explanations rather than immediately providing model answers. We present ExplainRoute, a pre-deployment audit framework for non-answer-giving programming tutors. Given a code line and a learner explanation, it estimates the explanation state and selects one of two bounded...
Y. Gai, Ying-Ying Zhang, Xue-Fei Huang· 0 citations
Qr-Hint is presented, a system designed to support debugging for both students and educators that identifies the smallest syntactic edit to a wrong query to make it semantically equivalent to a correct one.
Zhou-Yi Jiang, Yi-Hao Hu, Amir Gilad et al.· Proceedings of the VLDB Endo...· 0 citations
Effective LLM tutoring depends on correctly identifying the specific error in a student's reasoning before generating feedback. We study this problem in propositional-logic proof tutoring, where student actions can be checked against formal inference rules. We introduce a verifier-grounded architecture that separates d...
Tahreem Yasir, A. Mody, Xioayi Tian et al.· 0 citations
EvalAI grades theory answers on meaning rather than wording, using Sentence-BERT to measure how semantically similar a student's response is to a lecturer-provided reference answer, showing that semantic grading of this quality can be delivered within a deployable web application without specialised machine learning in...
E. Mgbeahuruike, Chris-Esezobor Ejodamen, Nelson-Nwanoneze Samuel et al.· British journal of computer,...· 0 citations
A deployed tutoring system that enforces answer-withholding as a per-turn, machine-checkable contract, and a method for tuning that withholding against evidence is reported, offering the measure, diagnose, and fix loop as a reusable recipe for any LLM agent that must refuse a capability it has.
Paired runs of the same question at matched reasoning horizons across 198 GPQA Diamond and 500 MMLU-Pro questions are evaluated, finding that a tight deadline can favor lower effort or a concise instruction, whereas allowing high effort to finish can recover higher final accuracy.
Francesca Carlon, Vincent Ginis, A. Algaba· 0 citations
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