arXiv:2606.09830v1 Announce Type: new Abstract: In modern educational systems, Automatic Text Scoring (ATS) plays a central role by enabling scalable and consistent evaluation of learner responses without human intervention. Recently, the increased accessibility of LLMs and Arabic-specific datasets has sparked renewed interest in this area. In this work, we investigate LLM-Based approaches for the automated evaluation of Arabic texts, focusing on both short answer grading (ASAG) and essay scoring (AES). We further introduce a structured taxonomy comprising five dimensions: application domain,
The increased accessibility of Large Language Models (LLMs) and growing Arabic-specific datasets are enabling more nuanced applications like automated text scoring for non-English languages.
This development indicates the expansion of AI's practical applications beyond dominant languages, fostering localized technological advancements and potentially impacting educational systems globally.
The ability to accurately automate the scoring of complex Arabic texts using LLMs changes the landscape for educational assessment and content moderation, particularly in the Middle East.
- · Educational technology companies
- · Learners in Arabic-speaking regions
- · AI developers focused on multilingual NLP
- · Governments investing in localized AI
- · Traditional human Arabic text graders
- · Legacy educational assessment providers
- · Companies without multilingual AI capabilities
Widespread adoption of AI-driven Arabic text scoring in educational and professional settings.
Improved standardization and scalability of evaluations in Arabic-speaking markets, leading to enhanced educational outcomes.
The development of more sophisticated, culturally aware AI models for other non-dominant languages, reducing dependence on Western-centric AI frameworks.
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