# Do Tutors Learn from Equity Training and Can Generative AI Assess It?

arXiv: https://arxiv.org/abs/2412.11255 · HTML: https://mlsanity.com/paper/2412.11255/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 42 references that are in the map.
- First posted on arXiv 15 Dec 2024.

## Builds on (most cited first, 7 of 7)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [Automatic Short Math Answer Grading via In-context Meta-learning](https://mlsanity.com/paper/2205.15219/) (2022)
- [How Can I Get It Right? Using GPT to Rephrase Incorrect Trainee Responses](https://mlsanity.com/paper/2405.00970/) (2024)
- [Can Large Language Models Make the Grade? An Empirical Study Evaluating LLMs Ability to Mark Short Answer Questions in K-12 Education](https://mlsanity.com/paper/2405.02985/) (2024)
- [How Can I Improve? Using GPT to Highlight the Desired and Undesired Parts of Open-ended Responses](https://mlsanity.com/paper/2405.00291/) (2024)
- [Using Large Language Models to Assess Tutors' Performance in Reacting to Students Making Math Errors](https://mlsanity.com/paper/2401.03238/) (2024)

## Built on by (most cited first, 5 of 5)

- [Can Large Language Models Match Tutoring System Adaptivity? A Benchmarking Study](https://mlsanity.com/paper/2504.05570/) (2025)
- [Augmenting Human-Annotated Training Data with Large Language Model Generation and Distillation in Open-Response Assessment](https://mlsanity.com/paper/2501.09126/) (2025)
- [LLM-Generated Feedback Supports Learning If Learners Choose to Use It](https://mlsanity.com/paper/2506.17006/) (2025)
- [Large Language Models as Students Who Think Aloud: Overly Coherent, Verbose, and Confident](https://mlsanity.com/paper/2602.01015/) (2026)
- [Understanding Teacher Revisions of Large Language Model-Generated Feedback](https://mlsanity.com/paper/2603.27806/) (2026)

## Abstract (arXiv)

Equity is a core concern of learning analytics. However, applications that teach and assess equity skills, particularly at scale are lacking, often due to barriers in evaluating language. Advances in generative AI via large language models (LLMs) are being used in a wide range of applications, with this present work assessing its use in the equity domain. We evaluate tutor performance within an online lesson on enhancing tutors' skills when responding to students in potentially inequitable situations. We apply a mixed-method approach to analyze the performance of 81 undergraduate remote tutors. We find marginally significant learning gains with increases in tutors' self-reported confidence in their knowledge in responding to middle school students experiencing possible inequities from pretest to posttest. Both GPT-4o and GPT-4-turbo demonstrate proficiency in assessing tutors ability to predict and explain the best approach. Balancing performance, efficiency, and cost, we determine that few-shot learning using GPT-4o is the preferred model. This work makes available a dataset of lesson log data, tutor responses, rubrics for human annotation, and generative AI prompts. Future work involves leveling the difficulty among scenarios and enhancing LLM prompts for large-scale grading and assessment.