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

• URL: http://arxiv.org/abs/2510.02663v1
• Date: Fri, 03 Oct 2025 01:41:09 GMT
• Title: TutorBench: A Benchmark To Assess Tutoring Capabilities Of Large Language Models
• Authors: Rakshith S Srinivasa, Zora Che, Chen Bo Calvin Zhang, Diego Mares, Ernesto Hernandez, Jayeon Park, Dean Lee, Guillermo Mangialardi, Charmaine Ng, Ed-Yeremai Hernandez Cardona, Anisha Gunjal, Yunzhong He, Bing Liu, Chen Xing,
• Abstract summary: TutorBench is a dataset and evaluation benchmark designed to rigorously evaluate the core tutoring skills of large language models (LLMs)<n>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.<n>We evaluate 16 frontier LLMs on TutorBench and present a detailed analysis of their performance and behavior.
• Score: 10.963195858672627
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• One Model to Critique Them All: Rewarding Agentic Tool-Use via Efficient Reasoning [54.580646706013965]
  Reward models (RMs) play a critical role in aligning large language models with human preferences.<n>We introduce ToolRM, a family of lightweight generative RMs tailored for general tool-use scenarios.<n>To build these models, we propose a novel pipeline that constructs pairwise preference data using rule-based scoring and multidimensional sampling.
  arXiv  Detail & Related papers  (2025-10-30T06:08:27Z)
• Measuring Teaching with LLMs [4.061135251278187]
  This paper uses custom Large Language Models built on sentence-level embeddings.<n>We show that these specialized models can achieve human-level and even super-human performance with expert human ratings above 0.65.<n>We also find that aggregate model scores align with teacher value-added measures, indicating they are capturing features relevant to student learning.
  arXiv  Detail & Related papers  (2025-10-27T03:42:04Z)
• Benchmarking Large Language Models for Personalized Guidance in AI-Enhanced Learning [4.990353320509215]
  Large Language Models (LLMs) are increasingly envisioned as intelligent assistants for personalized learning.<n>This study conducts an empirical comparison of three state-of-the-art LLMs on a tutoring task that simulates a realistic learning setting.
  arXiv  Detail & Related papers  (2025-09-02T14:21:59Z)
• Can LLMs Reliably Simulate Real Students' Abilities in Mathematics and Reading Comprehension? [8.558834738072363]
  Large Language Models (LLMs) are increasingly used as proxy students in the development of Intelligent Tutoring Systems (ITSs)<n>We collect a dataset of 489 items from the National Assessment of Educational Progress (NAEP) covering mathematics and reading comprehension in grades 4, 8, and 12.<n>We apply an Item Response Theory (IRT) model to position 11 diverse and state-of-the-art LLMs on the same ability scale as real student populations.
  arXiv  Detail & Related papers  (2025-07-11T00:36:57Z)
• EvaLearn: Quantifying the Learning Capability and Efficiency of LLMs via Sequential Problem Solving [61.99289768925256]
  EvaLearn is a benchmark designed to evaluate large language models (LLMs) on their learning capability and efficiency in challenging tasks.<n>We benchmark nine frontier models and observe varied performance profiles.<n>We observe that current LLMs with stronger static abilities do not show a clear advantage in learning capability across all tasks.
  arXiv  Detail & Related papers  (2025-06-03T09:18:33Z)
• Can Large Language Models Match Tutoring System Adaptivity? A Benchmarking Study [0.0]
  Large Language Models (LLMs) hold promise as dynamic instructional aids.<n>Yet, it remains unclear whether LLMs can replicate the adaptivity of intelligent tutoring systems (ITS)
  arXiv  Detail & Related papers  (2025-04-07T23:57:32Z)
• MathTutorBench: A Benchmark for Measuring Open-ended Pedagogical Capabilities of LLM Tutors [76.1634959528817]
  We present MathTutorBench, an open-source benchmark for holistic tutoring model evaluation.<n>MathTutorBench contains datasets and metrics that broadly cover tutor abilities as defined by learning sciences research in dialog-based teaching.<n>We evaluate a wide set of closed- and open-weight models and find that subject expertise, indicated by solving ability, does not immediately translate to good teaching.
  arXiv  Detail & Related papers  (2025-02-26T08:43:47Z)
• Learning to Love Edge Cases in Formative Math Assessment: Using the AMMORE Dataset and Chain-of-Thought Prompting to Improve Grading Accuracy [0.0]
  This paper introduces AMMORE, a new dataset of 53,000 math open-response question-answer pairs from Rori. We conduct two experiments to evaluate the use of large language models (LLM) for grading challenging student answers.
  arXiv  Detail & Related papers  (2024-09-26T14:51:40Z)
• Evaluating and Optimizing Educational Content with Large Language Model Judgments [52.33701672559594]
  We use Language Models (LMs) as educational experts to assess the impact of various instructions on learning outcomes. We introduce an instruction optimization approach in which one LM generates instructional materials using the judgments of another LM as a reward function. Human teachers' evaluations of these LM-generated worksheets show a significant alignment between the LM judgments and human teacher preferences.
  arXiv  Detail & Related papers  (2024-03-05T09:09:15Z)
• Automated Evaluation of Classroom Instructional Support with LLMs and BoWs: Connecting Global Predictions to Specific Feedback [9.51494089949975]
  Large Language Models (LLMs) can be used to estimate Instructional Support'' domain scores of the CLassroom Assessment Scoring System (CLASS) We design a machine learning architecture that uses either zero-shot prompting of MetaPearson's Llama2, and/or a classic Bag of Words (BoW) model, to classify individual utterances of teachers' speech. These utterance-level judgments are aggregated over a 15-min observation session to estimate a global CLASS score.
  arXiv  Detail & Related papers  (2023-10-02T12:11:17Z)
• Language models are weak learners [71.33837923104808]
  We show that prompt-based large language models can operate effectively as weak learners. We incorporate these models into a boosting approach, which can leverage the knowledge within the model to outperform traditional tree-based boosting. Results illustrate the potential for prompt-based LLMs to function not just as few-shot learners themselves, but as components of larger machine learning pipelines.
  arXiv  Detail & Related papers  (2023-06-25T02:39:19Z)
• Evaluating the Performance of Large Language Models on GAOKAO Benchmark [53.663757126289795]
  This paper introduces GAOKAO-Bench, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples. With human evaluation, we obtain the converted total score of LLMs, including GPT-4, ChatGPT and ERNIE-Bot. We also use LLMs to grade the subjective questions, and find that model scores achieve a moderate level of consistency with human scores.
  arXiv  Detail & Related papers  (2023-05-21T14:39:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.