@inproceedings{bashendy-etal-2026-laila,
    title = "{LAILA}: A Large Trait-Based Dataset for {A}rabic Automated Essay Scoring",
    author = "Bashendy, May  and
      Massoud, Walid  and
      Eltanbouly, Sohaila  and
      Albatarni, Salam  and
      Sayed, Marwan  and
      Abir, Abrar  and
      Bouamor, Houda  and
      Elsayed, Tamer",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/manual-author-scripts/2026.eacl-long.142/",
    pages = "3074--3091",
    ISBN = "979-8-89176-380-7",
    abstract = "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."
}