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), cross-checked
against the reference implementation in 'robustDIF', 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,
including a bootstrap estimator of the cross-condition covariance that
paired designs require, a one-call workflow from raw item scores
through 'mirt' calibration, robust linking of the cross-condition
contrast, bootstrap standard errors, multiplicity control, simulation
of paired human and AI item scores, anchor weight diagnostics, and an
AI-effect classification framework. Methods follow Halpin (2024)
<doi:10.1007/s11336-024-09957-6>.
| Version: |
0.3.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
Matrix, stats, graphics, utils, parallel |
| Suggests: |
mirt, robustDIF, testthat (≥ 3.1.5), knitr, rmarkdown |
| Published: |
2026-10-22 |
| DOI: |
10.32614/CRAN.package.aiDIF |
| Author: |
Subir Hait  [aut,
cre] |
| Maintainer: |
Subir Hait <haitsubi at msu.edu> |
| BugReports: |
https://github.com/subirhait/aiDIF/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://github.com/subirhait/aiDIF |
| NeedsCompilation: |
no |
| Citation: |
aiDIF citation info |
| Materials: |
README, NEWS |
| CRAN checks: |
aiDIF results |
Documentation:
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