The Ultimate Tutorial for AI-driven Scale Development in Generative Psychometrics: Releasing AIGENIE from its Bottle

2026-03-30

AI Summary

Traditional psychological scale development relies heavily on expert input and large-scale pilot testing, resulting in time-consuming and costly procedures. This work proposes AI-GENIE, a novel framework that integrates large language models—supporting OpenAI, Anthropic, Groq, HuggingFace, and local deployments—with network psychometric methods, including exploratory graph analysis (EGA), unique variable analysis (UVA), and bootstrap EGA, to enable fully automated, end-to-end scale construction. The framework operates offline using only an arbitrary item pool and requires no human intervention. Its efficacy is demonstrated through two empirical applications: the Big Five personality traits and AI anxiety. To facilitate reproducibility and community adoption, the authors release an open-source R package, AIGENIE.

Abstract

Psychological scale development has traditionally required extensive expert involvement, iterative revision, and large-scale pilot testing before psychometric evaluation can begin. The `AIGENIE` R package implements the AI-GENIE framework (Automatic Item Generation with Network-Integrated Evaluation), which integrates large language model (LLM) text generation with network psychometric methods to automate the early stages of this process. The package generates candidate item pools using LLMs, transforms them into high-dimensional embeddings, and applies a multi-step reduction pipeline -- Exploratory Graph Analysis (EGA), Unique Variable Analysis (UVA), and bootstrap EGA -- to produce structurally validated item pools entirely *in silico*. This tutorial introduces the package across six parts: installation and setup, understanding Application Programming Interfaces (APIs), text generation, item generation, the `AIGENIE` function, and the `GENIE` function. Two running examples illustrate the package's use: the Big Five personality model (a well-established construct) and AI Anxiety (an emerging construct). The package supports multiple LLM providers (OpenAI, Anthropic, Groq, HuggingFace, and local models), offers a fully offline mode with no external API calls, and provides the `GENIE()` function for researchers who wish to apply the psychometric reduction pipeline to existing item pools regardless of their origin. The `AIGENIE` package is freely available on R-universe at https://laralee.r-universe.dev/AIGENIE.

Problem

• psychological scale development
• item generation
• psychometric evaluation
• large language models
• automated scale construction

Innovation

• AI-driven scale development
• Large Language Models
• Network psychometrics
• Exploratory Graph Analysis
• In silico validation