Automated Essay Scoring (AES) has shown significant advancements in educational assessment. However, under-resourced languages like Arabic have received limited attention. To bridge this gap and enable robust Arabic AES, this paper introduces the first publicly-available comprehensive set of engineered features tailored for Arabic AES, covering surface-level, readability, lexical, syntactic, and semantic features. Experiments are conducted on a dataset of 620 Arabic essays, each annotated with both holistic and trait-specific scores. Our findings demonstrate that the proposed feature set is effective across different models and competitive with recent NLP advances including LLMs, establishing the state-of-the-art performance and providing strong baselines for future Arabic AES research. Moroever, the resulting feature set offers a reusable and foundational resource, contributing towards the development of more effective Arabic AES 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.

This paper presents the participation of Qatar University team in MADAR shared task, which addresses the problem of sentence-level fine-grained Arabic Dialect Identification over 25 different Arabic dialects in addition to the Modern Standard Arabic. Arabic Dialect Identification is not a trivial task since different dialects share some features, e.g., utilizing the same character set and some vocabularies. We opted to adopt a very simple approach in terms of extracted features and classification models; we only utilize word and character n-grams as features, and Na ̈ıve Bayes models as classifiers. Surprisingly, the simple approach achieved non-na ̈ıve performance. The official results, reported on a held-out testing set, show that the dialect of a given sentence can be identified at an accuracy of 64.58% by our best submitted run.