INDRA: Iterative Difficulty Refinement Attention for MCQ Difficulty Estimation for Indic Languages

AbstractEstimating the difficulty of multiple-choice questions (MCQs) is central to adaptive testing and learner modeling. We introduce INDRA (Iterative Difficulty Refinement Attention), a novel attention mechanism that unifies psychometric priors with neural refinement for Indic MCQ difficulty estimation. INDRA incorporates three key innovations: (i) IRT-informed initialization, which assigns token-level discrimination and difficulty scores to embed psychometric interpretability; (ii) entropy-driven iterative refinement, which progressively sharpens attention to mimic the human process of distractor elimination; and (iii) Indic Aware Graph Coupling, which propagates plausibility across morphologically and semantically related tokens, a critical feature for Indic languages. Experiments on TEEMIL-H and TEEMIL-K datasets show that INDRA achieves consistent improvements, with absolute gains of up to +1.02 F1 and +1.68 F1 over state-of-the-art, while demonstrating through ablation studies that psychometric priors, entropy refinement, and graph coupling contribute complementary gains to accuracy and robustness.