# TutorBench: A Benchmark To Assess Tutoring Capabilities Of Large Language Models

arXiv: https://arxiv.org/abs/2510.02663 · HTML: https://mlsanity.com/paper/2510.02663/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 3 Oct 2025.

## Builds on (most cited first, 11 of 11)

- [OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems](https://mlsanity.com/paper/2402.14008/) (2024)
- [Humanity's Last Exam](https://mlsanity.com/paper/2501.14249/) (2025)
- [HealthBench: Evaluating Large Language Models Towards Improved Human Health](https://mlsanity.com/paper/2505.08775/) (2025)
- [FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI](https://mlsanity.com/paper/2411.04872/) (2024)
- [Unifying AI Tutor Evaluation: An Evaluation Taxonomy for Pedagogical Ability Assessment of LLM-Powered AI Tutors](https://mlsanity.com/paper/2412.09416/) (2024)
- [Training LLM-based Tutors to Improve Student Learning Outcomes in Dialogues](https://mlsanity.com/paper/2503.06424/) (2025)
- [AutoTutor meets Large Language Models: A Language Model Tutor with Rich Pedagogy and Guardrails](https://mlsanity.com/paper/2402.09216/) (2024)
- [Evaluating the Effectiveness of LLMs in Introductory Computer Science Education: A Semester-Long Field Study](https://mlsanity.com/paper/2404.13414/) (2024)
- [Beyond Final Answers: Evaluating Large Language Models for Math Tutoring](https://mlsanity.com/paper/2503.16460/) (2025)
- [GPT-4 as a Homework Tutor can Improve Student Engagement and Learning Outcomes](https://mlsanity.com/paper/2409.15981/) (2024)
- [How Students (Really) Use ChatGPT: Uncovering Experiences Among Undergraduate Students](https://mlsanity.com/paper/2505.24126/) (2025)

## Built on by (most cited first, 11 of 11)

- [The Missing Evaluation Axis: What 10,000 Student Submissions Reveal About AI Tutor Effectiveness](https://mlsanity.com/paper/2605.05648/) (2026)
- [TeachBench: A Syllabus-Grounded Framework for Evaluating Teaching Ability in Large Language Models](https://mlsanity.com/paper/2601.21375/) (2026)
- [Application-Driven Pedagogical Knowledge Optimization of Open-Source LLMs via Reinforcement Learning and Supervised Fine-Tuning](https://mlsanity.com/paper/2604.06385/) (2026)
- [Are Agents Ready to Teach? A Multi-Stage Benchmark for Real-World Teaching Workflows](https://mlsanity.com/paper/2605.14322/) (2026)
- [Elmes*: Automated Construction of Fine-Grained Evaluation Rubrics for Large Language Models in Long-Tail Educational Scenarios](https://mlsanity.com/paper/2606.06546/) (2026)
- [CSTutorBench: Benchmarking Small Language Models as Tutors for Block-Based Programming](https://mlsanity.com/paper/2607.05571/) (2026)
- [Knowledge Distillation for Automated AI Tutor Evaluation](https://mlsanity.com/paper/2607.10647/) (2026)
- [Auditable Release Control for Pedagogical Leakage in LLM Tutors](https://mlsanity.com/paper/2608.00515/) (2026)
- [OmniEdu: Open Foundation Models for Learning and Teaching](https://mlsanity.com/paper/2609.23088/) (2026)
- [StudentBench: AI and human tutoring yield equivalent GRE learning gains](https://mlsanity.com/paper/2609.28470/) (2026)
- [Sherpa: Teaching LLMs to Teach Adaptively](https://mlsanity.com/paper/2610.08778/) (2026)

## Abstract (arXiv)

As students increasingly adopt large language models (LLMs) as learning aids, it is crucial to build models that are adept at handling the nuances of tutoring: they need to identify the core needs of students, be adaptive, provide personalized guidance, and be accurate. To this end, we introduce TutorBench, a dataset and evaluation benchmark designed to rigorously evaluate the core tutoring skills of LLMs. The dataset comprises 1,490 samples curated by human experts, focused on high-school and AP-level curricula. The samples are drawn from three common tutoring tasks: (i) generating adaptive explanations tailored to a student's confusion, (ii) providing actionable feedback on a student's work, and (iii) promoting active learning through effective hint generation. To account for the inherent complexity of tutoring, samples are accompanied by sample-specific rubrics which are used to judge model responses during evaluation. TutorBench uses a reliable and fine-grained automatic evaluation method that uses an LLM-judge and the sample-specific rubrics. We evaluate 16 frontier LLMs on TutorBench and present a detailed analysis of their performance and behavior. Our results show that none of the frontier LLMs achieve a score of greater than 56%, showing a large room for improvement. We find that LLMs fall short in exhibiting the full range of tutoring skills needed to guide, diagnose, and support students effectively, with all the frontier models achieving less than a 60% pass rate on rubric criteria related to these skills. We also find that different model families exhibit varied strengths and limitations: the Claude models outperform others in supporting active learning, while they lag behind in the other two use cases. By releasing TutorBench, we provide a comprehensive and unsaturated benchmark to guide the development of the next-generation of AI tutors.