# Enhancing Arabic Automated Essay Scoring with Synthetic Data and Error Injection

arXiv: https://arxiv.org/abs/2503.17739 · HTML: https://mlsanity.com/paper/2503.17739/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in LLM synthetic data generation, one of 13 directions in Synthetic data and self-improvement for LLMs.
- Builds on 12 of its 39 references that are in the map.
- First posted on arXiv 22 Mar 2025.

## Placed in

- Synthetic data and self-improvement for LLMs / [LLM synthetic data generation](https://mlsanity.com/synth/llm-synthetic-data-generation/)

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

- [On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey](https://mlsanity.com/paper/2406.15126/) (2024)
- [The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://mlsanity.com/paper/2103.06678/) (2021)
- [ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic](https://mlsanity.com/paper/2402.12840/) (2024)
- [Data Augmentation Approaches in Natural Language Processing: A Survey](https://mlsanity.com/paper/2110.01852/) (2021)
- [Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation](https://mlsanity.com/paper/2305.14734/) (2023)
- [Automatic Error Type Annotation for Arabic](https://mlsanity.com/paper/2109.08068/) (2021)
- [Can LLMs Augment Low-Resource Reading Comprehension Datasets? Opportunities and Challenges](https://mlsanity.com/paper/2309.12426/) (2023)
- [Native vs Non-Native Language Prompting: A Comparative Analysis](https://mlsanity.com/paper/2409.07054/) (2024)
- [Enhancing Text Editing for Grammatical Error Correction: Arabic as a Case Study](https://mlsanity.com/paper/2503.00985/) (2025)
- [Automated essay scoring in Arabic: a dataset and analysis of a BERT-based system](https://mlsanity.com/paper/2407.11212/) (2024)
- [An Automated System for Essay Scoring of Online Exams in Arabic based on Stemming Techniques and Levenshtein Edit Operations](https://mlsanity.com/paper/1611.02815/) (2016)
- [Data Augmentation using Transformers and Similarity Measures for Improving Arabic Text Classification](https://mlsanity.com/paper/2212.13939/) (2022)

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

- [ARWI: Arabic Write and Improve](https://mlsanity.com/paper/2504.11814/) (2025)
- [Structured Prompting for Arabic Essay Proficiency: A Trait-Centric Evaluation Approach](https://mlsanity.com/paper/2603.19668/) (2026)
- [Incentives Of EdTech: A Systematic Review Of EduNLP Research](https://mlsanity.com/paper/2606.13691/) (2026)
- [Can LLMs Control Readability? A Multi-Dimensional Evaluation Framework for CEFR-Controlled Arabic Generation](https://mlsanity.com/paper/2606.21981/) (2026)
- [Error-Supervised Synthetic Learner Writing for Automated Essay Scoring](https://mlsanity.com/paper/2609.23573/) (2026)

## Abstract (arXiv)

Automated Essay Scoring (AES) plays a crucial role in assessing language learners' writing quality, reducing grading workload, and providing real-time feedback. The lack of annotated essay datasets inhibits the development of Arabic AES systems. This paper leverages Large Language Models (LLMs) and Transformer models to generate synthetic Arabic essays for AES. We prompt an LLM to generate essays across the Common European Framework of Reference (CEFR) proficiency levels and introduce and compare two approaches to error injection. We create a dataset of 3,040 annotated essays with errors injected using our two methods. Additionally, we develop a BERT-based Arabic AES system calibrated to CEFR levels. Our experimental results demonstrate the effectiveness of our synthetic dataset in improving Arabic AES performance. We make our code and data publicly available.