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Navigating the complexities of Arabic read ability prediction requires addressing the language’s rich morphology and structural diversity. In the BAREC Shared Task 2025, we participated in all tracks using a stacked ensemble meta learning framework. Our approach combined seven fine-tuned transformer, whose outputs fed into a meta classifier trained on multiple features, including individual predictions, their average, and the average top prediction probabilities. On the blind test set, our ensemble achieved a Quadratic Weighted Kappa (QWK) of 86.4%, demonstrating the effectiveness of integrating diverse transformer encoders for fine grained Arabic readability classification and the potential of meta learning in morphologically rich contexts.
