aiDIF: Differential Item Functioning for AI-Scored Assessments
Detects and quantifies differential item functioning (DIF) in
AI-scored educational and psychological assessments. Provides a fully
self-contained robust DIF engine (M-estimation via iteratively
re-weighted least squares with the bi-square loss) alongside the
Differential AI Scoring Bias (DASB) test, which detects item-level
scoring shifts that differ across subgroups when comparing human and AI
scoring conditions. Supports independent and paired scoring designs,
robust linking of the cross-condition contrast, multiplicity control,
conversion of fitted 'mirt' models to package inputs, simulation
utilities, anchor weight diagnostics, and an AI-effect classification
framework. Methods follow Halpin (2024) <doi:10.1007/s11336-024-09957-6>.
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