A Transformation-Based AI Framework for Equitable RTI Tier Classification in Qatar

Abstract

Consistent and equitable classification of students within Response to Intervention (RTI) frameworks remains a significant challenge in Qatar’s educational system, where tier-assignment decisions rely predominantly on qualitative, multidisciplinary evaluation reports rather than standardized quantitative measures. This research proposes a transformation-based framework for RTI tier classification using Qatari student evaluation reports. The framework converts qualitative clinical descriptors into structured intermediate representations through descriptor extraction, ordinal severity mapping, score translation, and composite aggregation. Seven classification approaches were systematically evaluated within this framework: direct zero-shot and few-shot large language model (LLM) classification; hierarchical prompting; rule-based transformation; LLM-assisted transformation; and a hybrid transformation-based approach. All experiments utilized OCR-extracted Arabic evaluation report text and were assessed using accuracy, balanced accuracy, macro F1-score, weighted F1-score, and class-wise F1-scores. The hybrid transformation-based approach demonstrated the strongest overall performance across evaluation metrics and was the only approach to maintain meaningful classification performance across all three RTI tiers, including the underrepresented and most challenging Tier 1 category. Direct and hierarchical prompting approaches produced lower and less consistent classification performance across the evaluated RTI tiers. These findings indicate that the introduction of structured intermediate transformation stages substantially enhances the consistency, interpretability, and equity of RTI tier classification from qualitative evaluation reports, providing a principled mechanism for standardizing classification decisions across Qatar’s schools and evaluation teams.