# Structured Prompting for Arabic Essay Proficiency: A Trait-Centric Evaluation Approach

arXiv: https://arxiv.org/abs/2603.19668 · HTML: https://mlsanity.com/paper/2603.19668/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 20 Mar 2026.

## Builds on (most cited first, 10 of 10)

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- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [ALLaM: Large Language Models for Arabic and English](https://mlsanity.com/paper/2407.15390/) (2024)
- [Can Large Language Models Automatically Score Proficiency of Written Essays?](https://mlsanity.com/paper/2403.06149/) (2024)
- [Unleashing Large Language Models' Proficiency in Zero-shot Essay Scoring](https://mlsanity.com/paper/2404.04941/) (2024)
- [TRATES: Trait-Specific Rubric-Assisted Cross-Prompt Essay Scoring](https://mlsanity.com/paper/2505.14577/) (2025)
- [How well can LLMs Grade Essays in Arabic?](https://mlsanity.com/paper/2501.16516/) (2025)
- [Automated essay scoring in Arabic: a dataset and analysis of a BERT-based system](https://mlsanity.com/paper/2407.11212/) (2024)
- [Enhancing Arabic Automated Essay Scoring with Synthetic Data and Error Injection](https://mlsanity.com/paper/2503.17739/) (2025)

## Abstract (arXiv)

This paper presents a novel prompt engineering framework for trait specific Automatic Essay Scoring (AES) in Arabic, leveraging large language models (LLMs) under zero-shot and few-shot configurations. Addressing the scarcity of scalable, linguistically informed AES tools for Arabic, we introduce a three-tier prompting strategy (standard, hybrid, and rubric-guided) that guides LLMs in evaluating distinct language proficiency traits such as organization, vocabulary, development, and style. The hybrid approach simulates multi-agent evaluation with trait specialist raters, while the rubric-guided method incorporates scored exemplars to enhance model alignment. In zero and few-shot settings, we evaluate eight LLMs on the QAES dataset, the first publicly available Arabic AES resource with trait level annotations. Experimental results using Quadratic Weighted Kappa (QWK) and Confidence Intervals show that Fanar-1-9B-Instruct achieves the highest trait level agreement in both zero and few-shot prompting (QWK = 0.28 and CI = 0.41), with rubric-guided prompting yielding consistent gains across all traits and models. Discourse-level traits such as Development and Style showed the greatest improvements. These findings confirm that structured prompting, not model scale alone, enables effective AES in Arabic. Our study presents the first comprehensive framework for proficiency oriented Arabic AES and sets the foundation for scalable assessment in low resource educational contexts.