Large Language Models for Arabic Automated Essay Scoring

Automated Essay Scoring (AES) has become an important research area in educational artificial intelligence due to its potential to support scalable and consistent assessment. The developments within the realm of transformers and large language models (LLMs) have led to great improvements within AESs through their ability to comprehend semantics and context, as well as their knowledge of rubrics. Although these advancements have been realized within the English language, there is still relatively little research surrounding Arabic Automated Essay Scoring (AAES). This survey summarizes some of the latest advancements in AAES and discusses traditional ML models, deep learning, transformers, and LLM-driven evaluation frameworks. In this study, researchers synthesize the relevant literature regarding datasets, prompts, pre-processing methods, performance metrics, and reliability of scores. Typical performance metrics used to analyze the level of agreement between human raters and automated systems are QWK, MAE, and correlation-based metrics. The survey also describes crucial challenges encountered by AAES systems such as insufficient amount of data, inconsistencies, high computation costs, bias, and non-reproducibility. Overall, it can be said that both transformers and LLMs achieve better performance when it comes to capturing context information and providing agreement with human assessment. However, issues with reproducibility and scalability continue to persist. Additionally, the survey presents new areas of research that may be relevant to future AAES studies, such as multilingual evaluation, hybrid grading, explainability, and standardized sources of Arabic essays.

@article{Najjar2026,
  title     = {Large Language Models for Arabic Automated Essay Scoring},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {5},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Leena Najjar and Liyakathunisa Syed},
  doi       = {10.14569/IJACSA.2026.0170570},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170570}
}