This simulation study evaluates whether predictive item-parameter priors can reduce respondent requirements for 3PL IRT calibration. Across twenty seven prior-quality configurations and seven sample sizes (400–1600), six configurations were able to match a 2000- respondent baseline at 400 respondents, yielding an 80% sample-size reduction when informative priors were properly incorporated.

This study predicts IRT item difficulty and discrimination for passage-based reading comprehension items using lexical, syntactic, and semantic NLP features. Modeling interactions among passages, stems, and options, Light- GBM with semantic embeddings best predicts difficulty (r = 0.594), while discrimination remains harder to recover from text alone.