IJIET 2025 Vol.15(12): 2796-2807
doi: 10.18178/ijiet.2025.15.12.2474
doi: 10.18178/ijiet.2025.15.12.2474
Harnessing Transformers for Enhancing Arabic Educational Assessment
Emad Nabil1,*, Mostafa Mohamed Saeed2, Rana Reda3, Safiullah Faizullah4,
and Wael Hassan Gomaa5
1. Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah, Saudi Arabia
2. Computational Approaches to Modeling Language (CAMeL) Lab, New York University, Abu Dhabi, United Arab Emirates
3. Digital Egypt for Investment Co., Ministry of Communications and Information Technology (MCIT), Cairo, Egypt
4. Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah, Saudi Arabia
5. Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, Egypt
Email: e.nabil@fci-cu.edu.eg (E.N.); mms10094@nyu.edu (M.M.S.); rana.reda@defi.com.eg (R.R.); safi@iu.edu.sa (S.F.); wael.goma@gmail.com (W.H.G.)
*Corresponding author
2. Computational Approaches to Modeling Language (CAMeL) Lab, New York University, Abu Dhabi, United Arab Emirates
3. Digital Egypt for Investment Co., Ministry of Communications and Information Technology (MCIT), Cairo, Egypt
4. Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah, Saudi Arabia
5. Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, Egypt
Email: e.nabil@fci-cu.edu.eg (E.N.); mms10094@nyu.edu (M.M.S.); rana.reda@defi.com.eg (R.R.); safi@iu.edu.sa (S.F.); wael.goma@gmail.com (W.H.G.)
*Corresponding author
Manuscript received May 6, 2025; revised July 4, 2025; accepted July 28, 2025; published December 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
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).
Keywords—automatic scoring, Arabic short answer scoring, Natural Language Processing (NLP), transformers, Artificial Intelligence (AI) in education
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.
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).