# Can Large Language Models Make the Grade? An Empirical Study Evaluating LLMs Ability to Mark Short Answer Questions in K-12 Education

arXiv: https://arxiv.org/abs/2405.02985 · HTML: https://mlsanity.com/paper/2405.02985/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 5 May 2024.

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

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [True Few-Shot Learning with Language Models](https://mlsanity.com/paper/2105.11447/) (2021)
- [Multi-source Domain Adaptation in the Deep Learning Era: A Systematic Survey](https://mlsanity.com/paper/2002.12169/) (2020)
- [ChatGPT: Beginning of an End of Manual Linguistic Data Annotation? Use Case of Automatic Genre Identification](https://mlsanity.com/paper/2303.03953/) (2023)
- [Automated Scoring for Reading Comprehension via In-context BERT Tuning](https://mlsanity.com/paper/2205.09864/) (2022)

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

- [No Free Labels: Limitations of LLM-as-a-Judge Without Human Grounding](https://mlsanity.com/paper/2503.05061/) (2025)
- [A LLM-Powered Automatic Grading Framework with Human-Level Guidelines Optimization](https://mlsanity.com/paper/2410.02165/) (2024)
- [Does Multiple Choice Have a Future in the Age of Generative AI? A Posttest-only RCT](https://mlsanity.com/paper/2412.10267/) (2024)
- [How well can LLMs Grade Essays in Arabic?](https://mlsanity.com/paper/2501.16516/) (2025)
- [Do Tutors Learn from Equity Training and Can Generative AI Assess It?](https://mlsanity.com/paper/2412.11255/) (2024)
- [Evaluating the capability of large language models to personalize science texts for diverse middle-school-age learners](https://mlsanity.com/paper/2408.05204/) (2024)
- [Identifying and Mitigating Gender Cues in Academic Recommendation Letters: An Interpretability Case Study](https://mlsanity.com/paper/2604.12337/) (2026)
- [Distinguishing Artificial from Authentic: Evaluating LLMs for Detecting LLM-Generated Content](https://mlsanity.com/paper/2607.20446/) (2026)
- [Grading Handwritten Engineering Exams with Multimodal Large Language Models](https://mlsanity.com/paper/2601.00730/) (2026)
- [The Impact of LLM Self-Consistency and Reasoning Effort on Automated Scoring Accuracy and Cost](https://mlsanity.com/paper/2604.26954/) (2026)
- [Bounding Boxes to Improve Small Language Model Performance on Vision-Based Grading Tasks](https://mlsanity.com/paper/2607.18767/) (2026)

## Abstract (arXiv)

This paper presents reports on a series of experiments with a novel dataset evaluating how well Large Language Models (LLMs) can mark (i.e. grade) open text responses to short answer questions, Specifically, we explore how well different combinations of GPT version and prompt engineering strategies performed at marking real student answers to short answer across different domain areas (Science and History) and grade-levels (spanning ages 5-16) using a new, never-used-before dataset from Carousel, a quizzing platform. We found that GPT-4, with basic few-shot prompting performed well (Kappa, 0.70) and, importantly, very close to human-level performance (0.75). This research builds on prior findings that GPT-4 could reliably score short answer reading comprehension questions at a performance-level very close to that of expert human raters. The proximity to human-level performance, across a variety of subjects and grade levels suggests that LLMs could be a valuable tool for supporting low-stakes formative assessment tasks in K-12 education and has important implications for real-world education delivery.