# Using Large Language Models to Assess Tutors' Performance in Reacting to Students Making Math Errors

arXiv: https://arxiv.org/abs/2401.03238 · HTML: https://mlsanity.com/paper/2401.03238/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 6 Jan 2024.

## Builds on (most cited first, 6 of 6)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks](https://mlsanity.com/paper/2211.12588/) (2022)
- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://mlsanity.com/paper/2102.07350/) (2021)
- [Is ChatGPT a Good Teacher Coach? Measuring Zero-Shot Performance For Scoring and Providing Actionable Insights on Classroom Instruction](https://mlsanity.com/paper/2306.03090/) (2023)
- [GPT-3.5, GPT-4, or BARD? Evaluating LLMs Reasoning Ability in Zero-Shot Setting and Performance Boosting Through Prompts](https://mlsanity.com/paper/2305.12477/) (2023)
- [Measuring Five Accountable Talk Moves to Improve Instruction at Scale](https://mlsanity.com/paper/2311.10749/) (2023)

## Built on by (most cited first, 9 of 9)

- [Training LLM-based Tutors to Improve Student Learning Outcomes in Dialogues](https://mlsanity.com/paper/2503.06424/) (2025)
- [Improving the Validity of Automatically Generated Feedback via Reinforcement Learning](https://mlsanity.com/paper/2403.01304/) (2024)
- [DiaHalu: A Dialogue-level Hallucination Evaluation Benchmark for Large Language Models](https://mlsanity.com/paper/2403.00896/) (2024)
- [Does Multiple Choice Have a Future in the Age of Generative AI? A Posttest-only RCT](https://mlsanity.com/paper/2412.10267/) (2024)
- [Do Tutors Learn from Equity Training and Can Generative AI Assess It?](https://mlsanity.com/paper/2412.11255/) (2024)
- [AI-Mediated Feedback Improves Student Revisions: A Randomized Trial with FeedbackWriter in a Large Undergraduate Course](https://mlsanity.com/paper/2602.16820/) (2026)
- [Designing and Evaluating Chain-of-Hints for Scientific Question Answering](https://mlsanity.com/paper/2510.21087/) (2025)
- [Large Language Models Approach Expert Pedagogical Quality in Math Tutoring but Differ in Instructional and Linguistic Profiles](https://mlsanity.com/paper/2512.20780/) (2025)
- [Methodologies for Improving the Quality of AI Tutoring in K-12 Education](https://mlsanity.com/paper/2608.11259/) (2026)

## Abstract (arXiv)

Research suggests that tutors should adopt a strategic approach when addressing math errors made by low-efficacy students. Rather than drawing direct attention to the error, tutors should guide the students to identify and correct their mistakes on their own. While tutor lessons have introduced this pedagogical skill, human evaluation of tutors applying this strategy is arduous and time-consuming. Large language models (LLMs) show promise in providing real-time assessment to tutors during their actual tutoring sessions, yet little is known regarding their accuracy in this context. In this study, we investigate the capacity of generative AI to evaluate real-life tutors' performance in responding to students making math errors. By analyzing 50 real-life tutoring dialogues, we find both GPT-3.5-Turbo and GPT-4 demonstrate proficiency in assessing the criteria related to reacting to students making errors. However, both models exhibit limitations in recognizing instances where the student made an error. Notably, GPT-4 tends to overidentify instances of students making errors, often attributing student uncertainty or inferring potential errors where human evaluators did not. Future work will focus on enhancing generalizability by assessing a larger dataset of dialogues and evaluating learning transfer. Specifically, we will analyze the performance of tutors in real-life scenarios when responding to students' math errors before and after lesson completion on this crucial tutoring skill.