# How Can I Get It Right? Using GPT to Rephrase Incorrect Trainee Responses

arXiv: https://arxiv.org/abs/2405.00970 · HTML: https://mlsanity.com/paper/2405.00970/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 40 references that are in the map.
- First posted on arXiv 2 May 2024.

## Builds on (most cited first, 12 of 12)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Graph of Thoughts: Solving Elaborate Problems with Large Language Models](https://mlsanity.com/paper/2308.09687/) (2023)
- [The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only](https://mlsanity.com/paper/2306.01116/) (2023)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [A Review of Generalized Zero-Shot Learning Methods](https://mlsanity.com/paper/2011.08641/) (2020)
- [A Theoretical Analysis of the Number of Shots in Few-Shot Learning](https://mlsanity.com/paper/1909.11722/) (2019)
- [Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preference](https://mlsanity.com/paper/2310.03184/) (2023)
- [Teaching Language Models to Self-Improve through Interactive Demonstrations](https://mlsanity.com/paper/2310.13522/) (2023)
- [Automated Distractor and Feedback Generation for Math Multiple-choice Questions via In-context Learning](https://mlsanity.com/paper/2308.03234/) (2023)
- [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 Provide Explanatory Feedback to Human Tutors](https://mlsanity.com/paper/2306.15498/) (2023)

## Built on by (most cited first, 12 of 12)

- [Tutor CoPilot: A Human-AI Approach for Scaling Real-Time Expertise](https://mlsanity.com/paper/2410.03017/) (2024)
- [SPL: A Socratic Playground for Learning Powered by Large Language Model](https://mlsanity.com/paper/2406.13919/) (2024)
- [Does Multiple Choice Have a Future in the Age of Generative AI? A Posttest-only RCT](https://mlsanity.com/paper/2412.10267/) (2024)
- [RAMO: Retrieval-Augmented Generation for Enhancing MOOCs Recommendations](https://mlsanity.com/paper/2407.04925/) (2024)
- [Beyond Text-to-Text: An Overview of Multimodal and Generative Artificial Intelligence for Education Using Topic Modeling](https://mlsanity.com/paper/2409.16376/) (2024)
- [Do Tutors Learn from Equity Training and Can Generative AI Assess It?](https://mlsanity.com/paper/2412.11255/) (2024)
- [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)
- [Automated Bias Assessment in AI-Generated Educational Content Using CEAT Framework](https://mlsanity.com/paper/2505.12718/) (2025)
- [LLM-as-a-Grader: Practical Insights from Large Language Model for Short-Answer and Report Evaluation](https://mlsanity.com/paper/2511.10819/) (2025)
- [MisEdu-RAG: A Misconception-Aware Dual-Hypergraph RAG for Novice Math Teachers](https://mlsanity.com/paper/2604.04036/) (2026)
- [Controllable Spoken Dialogue Generation: An LLM-Driven Grading System for K-12 Non-Native English Learners](https://mlsanity.com/paper/2604.22542/) (2026)

## Abstract (arXiv)

One-on-one tutoring is widely acknowledged as an effective instructional method, conditioned on qualified tutors. However, the high demand for qualified tutors remains a challenge, often necessitating the training of novice tutors (i.e., trainees) to ensure effective tutoring. Research suggests that providing timely explanatory feedback can facilitate the training process for trainees. However, it presents challenges due to the time-consuming nature of assessing trainee performance by human experts. Inspired by the recent advancements of large language models (LLMs), our study employed the GPT-4 model to build an explanatory feedback system. This system identifies trainees' responses in binary form (i.e., correct/incorrect) and automatically provides template-based feedback with responses appropriately rephrased by the GPT-4 model. We conducted our study on 410 responses from trainees across three training lessons: Giving Effective Praise, Reacting to Errors, and Determining What Students Know. Our findings indicate that: 1) using a few-shot approach, the GPT-4 model effectively identifies correct/incorrect trainees' responses from three training lessons with an average F1 score of 0.84 and an AUC score of 0.85; and 2) using the few-shot approach, the GPT-4 model adeptly rephrases incorrect trainees' responses into desired responses, achieving performance comparable to that of human experts.