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 novel Differential AI Scoring Bias (DASB) test, which detects item-level scoring shifts that differ across subgroups when comparing human and AI scoring conditions. Includes simulation utilities, anchor weight diagnostics, and an AI-effect classification framework.

Version:	0.1.0
Depends:	R (≥ 3.5.0)
Imports:	Matrix, stats, graphics
Suggests:	mirt, testthat (≥ 3.0.0), knitr, rmarkdown
Published:	2026-04-21
DOI:	10.32614/CRAN.package.aiDIF
Author:	Subir Hait [aut, cre]
Maintainer:	Subir Hait <haitsubi at msu.edu>
BugReports:	https://github.com/causalfragility-lab/aiDIF/issues
License:	GPL (≥ 3)
URL:	https://github.com/causalfragility-lab/aiDIF
NeedsCompilation:	no
Materials:	README
CRAN checks:	aiDIF results

Documentation:

Reference manual:	aiDIF.html , aiDIF.pdf
Vignettes:	Introduction to aiDIF (source, R code)

Downloads:

Package source:	aiDIF_0.1.0.tar.gz
Windows binaries:	r-release: aiDIF_0.1.0.zip, r-oldrel: aiDIF_0.1.0.zip
macOS binaries:	r-release (arm64): aiDIF_0.1.0.tgz, r-oldrel (arm64): aiDIF_0.1.0.tgz, r-release (x86_64): aiDIF_0.1.0.tgz, r-oldrel (x86_64): aiDIF_0.1.0.tgz

Linking:

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