Enhancing Arabic Automated Essay Scoring with Synthetic Data and Error Injection
Qwaider, Chatrine Alhafni, Bashar Chirkunov, Kirill Habash, Nizar Briscoe, Ted
Qwaider, Chatrine
Alhafni, Bashar
Chirkunov, Kirill
Habash, Nizar
Briscoe, Ted
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Department
Natural Language Processing
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Conference proceeding
Date
2025
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English
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Abstract
Automated Essay Scoring (AES) plays a crucial role in assessing language learners’ writingquality, reducing grading workload, and providing real-time feedback. The lack of annotatedessay datasets inhibits the development of Arabic AES systems. This paper leverages LargeLanguage Models (LLMs) and Transformermodels to generate synthetic Arabic essays forAES. We prompt an LLM to generate essaysacross the Common European Framework ofReference (CEFR) proficiency levels and introduce and compare two approaches to errorinjection. We create a dataset of 3,040 annotated essays with errors injected using our twomethods. Additionally, we develop a BERTbased Arabic AES system calibrated to CEFRlevels. Our experimental results demonstratethe effectiveness of our synthetic dataset in improving Arabic AES performance. We makeour code and data publicly available
Citation
C. Qwaider, B. Alhafni, K. Chirkunov, N. Habash, and T. Briscoe, “Enhancing Arabic Automated Essay Scoring with Synthetic Data and Error Injection,” pp. 549–563, Aug. 2025, doi: 10.18653/V1/2025.BEA-1.40.
Source
Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications
Conference
20th Workshop on Innovative Use of NLP for Building Educational Applications
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20th Workshop on Innovative Use of NLP for Building Educational Applications
Publisher
Association for Computational Linguistics