simulate_aidif_data: Simulate item parameter estimates for the AI-DIF model.

In aiDIF: Differential Item Functioning for AI-Scored Assessments

Simulate item parameter estimates for the AI-DIF model.

Description

Generates a synthetic aidif_data-compatible list suitable for benchmarking and method evaluation. The data-generating model contains: classical DIF in the human scoring condition (controlled via dif_items and dif_mag), differential AI scoring bias (controlled via dasb_items and dasb_mag), and a latent group mean difference (impact).

Usage

simulate_aidif_data(
  n_items = 10L,
  n_obs = 500L,
  impact = 0.5,
  dif_items = 1L,
  dif_mag = 0.5,
  dasb_items = 3L,
  dasb_mag = 0.4,
  ai_drift = 0.1,
  seed = 42L
)

Arguments

n_items	Integer. Number of items. Default: 10.
n_obs	Integer. Approximate number of observations per group, used to scale the covariance matrices. Default: 500.
impact	Numeric. Latent mean difference (group 2 minus group 1) in SD units. Default: 0.5.
dif_items	Integer vector. Indices of items with DIF in the human scoring condition (intercept shift added to group 2). Default: 1.
dif_mag	Numeric. Magnitude of the intercept DIF effect (in IRT metric). Default: 0.5.
dasb_items	Integer vector. Indices of items where AI scoring introduces differential bias (intercept shift added to group 2 in the AI condition only). Default: 3.
dasb_mag	Numeric. Magnitude of the DASB effect. Default: 0.4.
ai_drift	Numeric. Uniform intercept shift applied to ALL items in BOTH groups under AI scoring (simulates AI calibration offset). Default: 0.1.
seed	Integer seed for reproducibility, or NULL. Default: 42.

Details

Rather than simulating item responses and refitting IRT models (which requires additional dependencies), this function directly simulates maximum-likelihood estimates and their asymptotic covariance matrices, consistent with a 2PL model fitted to n_obs observations per group.

Value

A list with elements human and ai, each formatted identically to the output of read_ai_scored. Can be passed directly to fit_aidif.

Examples

dat <- simulate_aidif_data(
  n_items   = 8,
  n_obs     = 600,
  dif_items = c(1, 2),
  dasb_items = 5
)
mod <- fit_aidif(dat$human, dat$ai)
summary(mod)

aiDIF documentation built on April 22, 2026, 1:10 a.m.