LAILA: A Large Trait-Based Dataset for Arabic Automated Essay Scoring
This addresses a critical need for researchers and developers working on Arabic AES by providing a large, annotated dataset to support robust scoring systems.
The paper tackles the lack of publicly available datasets for Arabic Automated Essay Scoring by introducing LAILA, a dataset with 7,859 essays annotated with holistic and trait-specific scores, and provides benchmark results using state-of-the-art models.
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.