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Feature Engineering is not Dead: A Step Towards State of the Art for Arabic Automated Essay Scoring

Marwan Sayed,Sohaila Eltanbouly,May Bashendy,Tamer Elsayed

Published 2025 in Proceedings of The Third Arabic Natural Language Processing Conference

ABSTRACT

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. Moreover, the resulting feature set offers a reusable and foundational resource, contributing towards the development of more effective Arabic AES systems.

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