doi: 10.18178/ijiet.2025.15.12.2474
Harnessing Transformers for Enhancing Arabic Educational Assessment
Emad Nabil1,*
,
Mostafa Mohamed Saeed2 ,
Rana Reda3 ,
Safiullah Faizullah4 ,
Wael Hassan Gomaa5
- 1Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah, Saudi Arabia
- 2Computational Approaches to Modeling Language (CAMeL) Lab, New York University, Abu Dhabi, United Arab Emirates
- 3Digital Egypt for Investment Co., Ministry of Communications and Information Technology (MCIT), Cairo, Egypt
- 4Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah, Saudi Arabia
- 5Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, Egypt
- Manuscript receivedMay 6, 2025
- revisedJuly 4, 2025
- acceptedJuly 28, 2025
- publishedDecember 19, 2025
Abstract
This study introduces an intelligent scoring approach that leverages natural language processing and transformer-based models to evaluate student responses across various academic subjects. Given the linguistic complexity and variability of Arabic short-answer questions, the research proposes a novel grading method that moves beyond traditional techniques. Using the Cairo University Dataset a widely recognized benchmark focused on environmental science the study explores different preprocessing strategies and applies multiple transformer models. These models are integrated into a custom regression-based neural network designed specifically for Arabic short-answer grading. The proposed system achieves a Pearson correlation of 92.34%, surpassing the current state-of-the-art on the Cairo University Dataset. To evaluate generalizability, the model was also tested on the Arabic Short Answer Grading dataset, achieving an 80% Pearson correlation and outperforming existing benchmarks. These results demonstrate the approach’s strong potential for educational applications, offering a scalable and fair grading solution that reduces teacher workload while maintaining assessment accuracy.
Keywords
- automatic scoring
- Arabic short answer scoring
- Natural Language Processing (NLP)
- transformers
- Artificial Intelligence (AI) in education
How to Cite
Emad Nabil, Mostafa Mohamed Saeed, Rana Reda, Safiullah Faizullah, and Wael Hassan Gomaa, "Harnessing Transformers for Enhancing Arabic Educational Assessment," International Journal of Information and Education Technology, vol. 15, no. 12, pp. 2796-2807, 2025. https://doi.org/10.18178/ijiet.2025.15.12.2474
Copyright & License
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).