Despite significant advances in Automated Essay Scoring (AES), progress in Arabic AES remains limited by the scarcity and imbalance of publicly available datasets. Manual curation of such data is labor-intensive and lacks scalability. To address this, we introduce COrE, a corruption-based data augmentation method that targets the organization trait of Arabic essays. COrE generates synthetic essays by intentionally disrupting the organization of well-written essays through controlled, distance-aware sentence swapping. Our experiments are conducted on TAQAE, a dataset of 620 essays across 4 distinct writing prompts. We evaluate the effectiveness of COrE using two widely-adopted pre-trained models: AraBERTv2 and CAMeLBERT-mix. Both models show improved performance with COrE, achieving gains of 9-17% over the no-augmentation baseline. These results highlight the potential of trait-specific augmentation to address data scarcity and enhance AES performance for low-resource languages.