用AI自动生成心理量表题项并自动验证结构，省去人工反复修改和大规模测试。
The Ultimate Tutorial for AI-driven Scale Development in Generative Psychometrics: Releasing AIGENIE from its Bottle
- 结合大模型生成题项，用图模型方法在计算机中完成结构验证。
- 支持多平台大模型，可离线运行，生成的题项池经三步降维筛选。
- 适合心理学研究者快速开发新量表，尤其适用于新兴心理概念。
心理量表的传统开发需大量专家参与、反复修订及大规模预测试，方可进入心理测量评估阶段。AIGENIE R 包实现了 AI-GENIE 框架（基于网络整合评估的大规模题项自动生成），将大语言模型（LLM）文本生成与网络心理测量方法融合，自动化处理该流程的早期阶段。该包利用 LLM 生成候选题项池，将其转化为高维嵌入，再通过多步降维流程——探索性图分析（EGA）、唯一变量分析（UVA）和自助式 EGA——在计算机内生成结构有效的题项池。本教程分为六个部分：安装与设置、理解 API、文本生成、题项生成、AIGENIE 函数与 GENIE 函数使用。通过两个实例展示应用：已确立的五大人格特质模型（Big Five）和新兴的 AI 焦虑（AI Anxiety）。该包支持 OpenAI、Anthropic、Groq、HuggingFace 及本地模型等多种 LLM 提供商，提供完全离线模式，无需外部 API 调用，并通过 GENIE() 函数支持对任意来源题项池进行心理测量降维处理。AIGENIE 包免费发布于 R-universe：https://laralee.r-universe.dev/AIGENIE。
原文摘要 · Abstract (English)
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.
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