# Overview of AI Grading of Physics Olympiad Exams

arXiv: https://arxiv.org/abs/2505.02121 · HTML: https://mlsanity.com/paper/2505.02121/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 4 May 2025.

## Builds on (most cited first, 4 of 4)

- [Performance of the Pre-Trained Large Language Model GPT-4 on Automated Short Answer Grading](https://mlsanity.com/paper/2309.09338/) (2023)
- [Automated Theorem Provers Help Improve Large Language Model Reasoning](https://mlsanity.com/paper/2408.03492/) (2024)
- [CON-FOLD -- Explainable Machine Learning with Confidence](https://mlsanity.com/paper/2408.07854/) (2024)
- [Highlighting Case Studies in LLM Literature Review of Interdisciplinary System Science](https://mlsanity.com/paper/2503.16515/) (2025)

## Built on by (most cited first, 1 of 1)

- [Bounding Boxes to Improve Small Language Model Performance on Vision-Based Grading Tasks](https://mlsanity.com/paper/2607.18767/) (2026)

## Abstract (arXiv)

Automatically grading the diverse range of question types in high school physics problem is a challenge that requires automated grading techniques from different fields. We report the findings of a Systematic Literature Review of potential physics grading techniques. We propose a multi-modal AI grading framework to address these challenges and examine our framework in light of Australia's AI Ethical Principles.