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

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.

Source: arXiv:2603.28643v1 - http://arxiv.org/abs/2603.28643v1 PDF: https://arxiv.org/pdf/2603.28643v1 Original Link: http://arxiv.org/abs/2603.28643v1