AIGENIE: Automatic Item Generation and Validation via Network-Integrated
Evaluation
Automated psychological scale development and structural
validation using large language models (LLMs) and network psychometric
methods. Implements the AI-GENIE framework (Automatic Item Generation
and Validation via Network-Integrated Evaluation) to generate candidate
items, compute embedding representations, and estimate dimensional
structure using Exploratory Graph Analysis (EGA). Item quality is
evaluated using Unique Variable Analysis to identify redundant items
and Bootstrap EGA to assess item and dimension stability. Supports
both fully automated item generation and analysis of user-provided
item sets, facilitating efficient, theory-informed measurement
development prior to empirical data collection.
| Version: |
2.1.2 |
| Depends: |
R (≥ 3.6.0), EGAnet (≥ 2.4.0) |
| Imports: |
reticulate, ggplot2, igraph, patchwork, jsonlite |
| Suggests: |
testthat (≥ 3.0.0), tictoc, knitr, rmarkdown |
| Published: |
2026-09-08 |
| DOI: |
10.32614/CRAN.package.AIGENIE (may not be active yet) |
| Author: |
Lara Russell-Lasalandra
 [aut, cph],
Alexander Christensen
[aut, cph],
Hudson Golino
[aut, cre, cph] |
| Maintainer: |
Hudson Golino <hfg9s at virginia.edu> |
| BugReports: |
https://github.com/laralee/AIGENIE/issues |
| License: |
AGPL (≥ 3) |
| URL: |
https://laralee.github.io/AIGENIE/,
https://github.com/laralee/AIGENIE |
| NeedsCompilation: |
no |
| Citation: |
AIGENIE citation info |
| CRAN checks: |
AIGENIE results |
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