A Phase-Aware Hybrid Control Architecture for 3PL IRT-Based Computerized Adaptive Testing
Résumé
Computerized Adaptive Testing (CAT) is a key component of large-scale digital assessment systems, where high measurement accuracy must be achieved under constraints of test length, computational cost, and sustainable item bank usage. Although classical 3PL-IRT Item Selection Rules (ISRs) are efficient and interpretable, their effectiveness varies across test stages and ability regions, especially under short-test stopping conditions. This paper presents ADAPT-CAT, an interpretable phase-aware hybrid control architecture that models CAT as a state-dependent adaptive control process rather than a static one-step optimization rule. The framework coordinates complementary psychometric ISRs across three phases: early stabilization, conditional adaptation, and final convergence. A machine-learning selector based on gradient-boosted trees (XGB-ISR) is implemented exclusively as an offline benchmarking policy. Large-scale Monte Carlo simulations using a real 50-item 3PL-calibrated bank show that Fisher Information (FI) yields the best overall accuracy (RMSE = 0.649) with an average length of 16 items, while XGB-ISR achieves competitive precision but increases test length (18 items) and concentrates exposure. ADAPT-CAT reduces mean test length to 13 items while maintaining monotonic convergence and more balanced exposure, at the cost of lower global accuracy (RMSE = 0.940). Tail precision loss is primarily driven by structural item bank information deficits rather than the selection mechanism. These results support interpretable hybrid control architectures as a practical alternative for scalable and resource-aware CAT deployment.
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