Harnessing Transformers for Enhancing Arabic Educational Assessment

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.