Field testing is a resource-intensive bottleneck in test development. This study applied an interpretable framework that leverages a Large Language Model (LLM) for structured feature extraction from TIMSS items. These features will train several classifiers, whose predictions will be explained using SHAP, providing actionable, diagnostic insights insights for item writers.