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.

翻译：传统心理量表开发中，心理测量评估启动前通常需要大量专家参与、反复修订和大规模预测试。`AIGENIE` R软件包实现了AI-GENIE框架（基于网络集成的自动条目生成），该框架将大语言模型文本生成与网络心理测量方法相结合，以自动化这一流程的早期阶段。该软件包利用大语言模型生成候选条目集，将其转化为高维嵌入表示，随后通过多步精简管道——探索性图分析、唯一变量分析和自举EGA——完全在硅基环境下生成经过结构验证的条目集。本教程分六部分介绍该软件包：安装与配置、应用程序编程接口解析、文本生成、条目生成、`AIGENIE`函数及`GENIE`函数。通过两个运行实例展示软件包用法：大五人格模型（成熟构念）与AI焦虑（新兴构念）。该软件支持多种大语言模型提供商（OpenAI、Anthropic、Groq、HuggingFace及本地模型），提供无需外部API调用的完全离线模式，并为希望将心理测量精简管道应用于现有条目集（无论来源）的研究人员提供`GENIE()`函数。`AIGENIE`软件包可通过R-universe（https://laralee.r-universe.dev/AIGENIE）免费获取。