Year: 2025
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Qwaider, Chatrine, Alhafni, Bashar, Chirkunov, Kirill, Habash, Nizar, Briscoe, Ted
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W6979224659
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