# Large Language Models for Education: Grading Open-Ended Questions Using ChatGPT

arXiv: https://arxiv.org/abs/2307.16696 · HTML: https://mlsanity.com/paper/2307.16696/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2023.

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

- [Large Language Models for Education: A Survey and Outlook](https://mlsanity.com/paper/2403.18105/) (2024)
- [Can AI grade your essays? A comparative analysis of large language models and teacher ratings in multidimensional essay scoring](https://mlsanity.com/paper/2411.16337/) (2024)
- [A Novel Approach to Scalable and Automatic Topic-Controlled Question Generation in Education](https://mlsanity.com/paper/2501.05220/) (2025)
- [Subtle Biases Need Subtler Measures: Dual Metrics for Evaluating Representative and Affinity Bias in Large Language Models](https://mlsanity.com/paper/2405.14555/) (2024)
- [Grading Massive Open Online Courses Using Large Language Models](https://mlsanity.com/paper/2406.11102/) (2024)
- [CoTAL: Human-in-the-Loop Prompt Engineering for Generalizable Formative Assessment Scoring and Feedback](https://mlsanity.com/paper/2504.02323/) (2025)
- [Revolutionising Distance Learning: A Comparative Study of Learning Progress with AI-Driven Tutoring](https://mlsanity.com/paper/2403.14642/) (2024)
- [Large Language Models As MOOCs Graders](https://mlsanity.com/paper/2402.03776/) (2024)
- [From Motion Signals to Insights: A Unified Framework for Student Behavior Analysis and Feedback in Physical Education Classes](https://mlsanity.com/paper/2503.06525/) (2025)
- [A Comparison of Conversational Models and Humans in Answering Technical Questions: the Firefox Case](https://mlsanity.com/paper/2510.21933/) (2025)
- [Humanizing AI Grading: Student-Centered Insights on Fairness, Trust, Consistency and Transparency](https://mlsanity.com/paper/2602.07754/) (2026)

## Abstract (arXiv)

As a way of addressing increasingly sophisticated problems, software professionals face the constant challenge of seeking improvement. However, for these individuals to enhance their skills, their process of studying and training must involve feedback that is both immediate and accurate. In the context of software companies, where the scale of professionals undergoing training is large, but the number of qualified professionals available for providing corrections is small, delivering effective feedback becomes even more challenging. To circumvent this challenge, this work presents an exploration of using Large Language Models (LLMs) to support the correction process of open-ended questions in technical training. In this study, we utilized ChatGPT to correct open-ended questions answered by 42 industry professionals on two topics. Evaluating the corrections and feedback provided by ChatGPT, we observed that it is capable of identifying semantic details in responses that other metrics cannot observe. Furthermore, we noticed that, in general, subject matter experts tended to agree with the corrections and feedback given by ChatGPT.