Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.30828

TutorCaseBench: A Case-Grounded Benchmark for Evaluating LLM Tutors in Supporting Students with Challenging Behaviors

Yeon Su Park·Chanyoung Park·Tae Soo Kim·Seungju Kim·Hyunwoo Kim·Daho Jung·Jinsung Yoon·Juho Kim
View original source ↗Read source document ↗Export metadata ↓

Abstract

LLMs are increasingly used as AI tutors, yet existing benchmarks overlook the challenging behaviors common among students with learning and behavioral difficulties. We introduce TutorCaseBench, a benchmark that grounds AI-tutor evaluation in qualitative case studies from pedagogy, educational psychology, and special education. Our pipeline converts documented student categories into case-specific scenarios, tutor-student dialogues, and rubrics grounded in source evidence. The benchmark contains 923 dialogues across five challenge types, evaluated through open-ended response generation (1,560 items) and multiple-choice selection (2,537 items). Across 11 state-of-the-art LLMs, even the strongest model satisfies a rubric criterion in only 55.8% of open-ended cases, while models are better at selecting appropriate strategies than generating situated responses. These results reveal a gap between intervention recognition and context-specific adaptation, especially for open-weight models. All data are publicly available at https://github.com/kixlab/TutorCaseBench.

Explore related subjects

cs.CLcs.HC

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yeon Su Park, Chanyoung Park, Tae Soo Kim, Seungju Kim, Hyunwoo Kim, Daho Jung, Jinsung Yoon, Juho Kim. 2026-10-22. TutorCaseBench: A Case-Grounded Benchmark for Evaluating LLM Tutors in Supporting Students with Challenging Behaviors. https://arxiv.org/abs/2610.30828

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Kongzi: A Historical Large Language Model with Fact Enhancement

Large Language Models (LLMs) can exhibit factual inaccuracies during long-chain reasoning or under misleading prompts, limiting their reliability in historical analysis. We propose Kongzi, a language model for historical understanding and reasoning that combines continued pre-training on historical corpora, supervised fine-tuning, and fact-enhanced reinforcement learning. Its factuality reward compares entities and relations extracted from generated responses with historical references through knowledge-graph similarity. We evaluate 0.5B and 7B models on historical question answering and counterfactual reasoning, using reported ratings from three LLM judges, and on CMMLU-Chinese-History. Kongzi-7B receives the highest historical-accuracy ratings among the compared models under all three judges. Reinforcement learning improves the mean answer scores of both model sizes and their CMMLU-Chinese-History accuracy, although Kongzi-7B remains below the strongest reported baselines on that benchmark. These descriptive results support further study of factuality rewards for historical language models.

cs.CL↗
arXiv2026

ShIOEnv: A Command Evaluation Environment for Grammar-Constrained Synthesis and Execution Behavior Modeling

We introduce ShIOEnv, a Gymnasium-compatible Bash environment for synthesizing execution-annotated shell input traces. Datasets containing such traces are useful for training models to perform execution-free shell simulation, which in turn is useful for deploying honeypots for cyber deception. Existing datasets, such as NL2CMD and other Bash command corpora, do not include complete execution artifacts (stdout, stderr, exit status, and changes to the system's state) associated with a command's execution. ShIOEnv synthesizes shell inputs, executes them in a controlled system context, and records stdout, stderr, exit status, and structured filesystem and environment patches. Unconstrained (random) generation of shell inputs (command and arguments) results in multiple instances of arguments that are mostly redundant or result in execution errors. We introduce grammar-constrained synthesis to generate syntactically valid arguments with high input irreducibility, which we estimate through self-ablative contrasts of execution artifacts. We prepare 1.6M samples using grammar-constrained and unconstrained synthesis and collect their execution artifacts for 86 Linux/Bash utilities. Using this dataset, we train small language models (CodeT5) and evaluate their accuracy in predicting the actual execution behavior of attacker-sourced Bash commands. These models outperform commonly used rule-based simulation (Cowrie) and prompted large language models by up to 14.2 pp in output edit similarity (O-ES). We enhance the NL2CMD dataset by constructing 500K samples executable in a grounded system context along with their complete execution artifacts. We empirically demonstrate that models trained on grammar-constrained samples outperform those trained on NL2CMD samples by 22.5 pp in O-ES. ShIOEnv, command grammars, and 2.1M samples used in our primary evaluations are released for use by other researchers.

cs.CL↗
arXiv2026

How Well Does Mathematical Reasoning Transfer Across Languages in English-Centric LLMs?

Recent advances in Reinforcement Post-Training (RPT) have substantially improved the mathematical reasoning capabilities of large language models (LLMs), but it remains unclear how well such gains transfer across languages. In this work, we study cross-lingual mathematical reasoning transfer in English-centric LLMs under controlled multilingual evaluation. We first evaluate a range of English-centric reasoning models on multilingual mathematical reasoning benchmarks and find that transfer is highly variable across initial model choice, target language, and training paradigm. Through controlled comparative experiments, we further find that stronger English-centric initialization does not necessarily lead to stronger relative transfer efficiency, even when it yields higher multilingual reasoning accuracy. Finally, we show that the largest improvement comes from moving beyond English-only supervision: adding a single parallel language yields a substantial gain, while further gains from additional languages are smaller but consistent. Overall, our results highlight the need to evaluate multilingual mathematical reasoning directly, using both absolute performance and relative transfer-efficiency metrics.

cs.CL↗