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.

Automated Essay Scoring (AES) has emerged as a significant research problem in natural language processing, offering valuable tools to support educators in assessing student writing. Motivated by the growing need for reliable Arabic AES systems, we organized the first shared Task for Arabic Quality Evaluation of Essays in Multi-dimensions (TAQEEM) held at the ArabicNLP 2025 conference. TAQEEM 2025 includes two subtasks: Task A on holistic scoring and Task B on trait-specific scoring. It introduces a new (and first of its kind) dataset of 1,265 Arabic essays, annotated with holistic and trait-specific scores, including relevance, organization, vocabulary, style, development, mechanics, and grammar. The main goal of TAQEEM is to address the scarcity of standardized benchmarks and high-quality resources in Arabic AES. TAQEEM 2025 attracted 11 registered teams for Task A and 10 for Task B, with a total of 5 teams, across both tasks, submitting system runs for evaluation. This paper presents an overview of the task, outlines the approaches employed, and discusses the results of the participating teams.

Automated Essay Scoring (AES) has emerged as a significant research problem within natural language processing, providing valuable support for educators in assessing student writing skills. In this paper, we introduce QAES, the first publicly available trait-specific annotations for Arabic AES, built on the Qatari Corpus of Argumentative Writing (QCAW). QAES includes a diverse collection of essays in Arabic, each of them annotated with holistic and trait-specific scores, including relevance, organization, vocabulary, style, development, mechanics, and grammar. In total, it comprises 195 Arabic essays (with lengths ranging from 239 to 806 words) across two distinct argumentative writing tasks. We benchmark our dataset against the state-of-the-art English baselines and a feature-based approach. In addition, we discuss the adopted guidelines and the challenges encountered during the annotation process. Finally, we provide insights into potential areas for improvement and future directions in Arabic AES research.