# LAILA: A Large Trait-Based Dataset for Arabic Automated Essay Scoring

arXiv: https://arxiv.org/abs/2512.24235 · HTML: https://mlsanity.com/paper/2512.24235/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 46 references that are in the map.
- First posted on arXiv 30 Dec 2025.

## Builds on (most cited first, 8 of 8)

- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [Fanar: An Arabic-Centric Multimodal Generative AI Platform](https://mlsanity.com/paper/2501.13944/) (2025)
- [AraT5: Text-to-Text Transformers for Arabic Language Generation](https://mlsanity.com/paper/2109.12068/) (2021)
- [ALLaM: Large Language Models for Arabic and English](https://mlsanity.com/paper/2407.15390/) (2024)
- [Prompt- and Trait Relation-aware Cross-prompt Essay Trait Scoring](https://mlsanity.com/paper/2305.16826/) (2023)
- [Command R7B Arabic: A Small, Enterprise Focused, Multilingual, and Culturally Aware Arabic LLM](https://mlsanity.com/paper/2503.14603/) (2025)
- [How well can LLMs Grade Essays in Arabic?](https://mlsanity.com/paper/2501.16516/) (2025)
- [Automated essay scoring in Arabic: a dataset and analysis of a BERT-based system](https://mlsanity.com/paper/2407.11212/) (2024)

## Built on by (most cited first, 3 of 3)

- [Qayyem: A Real-time Platform for Scoring Proficiency of Arabic Essays](https://mlsanity.com/paper/2603.01009/) (2026)
- [MAPLE: A Meta-learning Framework for Cross-Prompt Essay Scoring](https://mlsanity.com/paper/2604.17569/) (2026)
- [Automated Scoring of Arabic Text Using Large Language Models: A Literature Review](https://mlsanity.com/paper/2606.09830/) (2026)

## Abstract (arXiv)

Automated Essay Scoring (AES) has gained increasing attention in recent years, yet research on Arabic AES remains limited due to the lack of publicly available datasets. To address this, we introduce LAILA, the largest publicly available Arabic AES dataset to date, comprising 7,859 essays annotated with holistic and trait-specific scores on seven dimensions: relevance, organization, vocabulary, style, development, mechanics, and grammar. We detail the dataset design, collection, and annotations, and provide benchmark results using state-of-the-art Arabic and English models in prompt-specific and cross-prompt settings. LAILA fills a critical need in Arabic AES research, supporting the development of robust scoring systems.