Large language models are increasingly becoming part of software engineering education, including activities involving empirical software engineering and evidence synthesis. This paper reports an educational experience involving the integration of reflective LLM use into an empirical methods assignment in a third-year software architecture course. Students were asked to develop a short research paper using either a rapid review or a gray literature review methodology and to disclose how LLMs were used throughout the assignment. We analyzed 146 student disclosure statements using a cross-analysis process combining LLM-assisted categorization with manual verification and refinement by the researchers. The reflections describe how students incorporated LLMs during activities such as brainstorming, methodological clarification, organization of findings, and writing refinement, while also reporting concerns regarding inaccuracies and verification of generated content. This experience report discusses lessons learned and educational implications for integrating AI-assisted technologies into empirical software engineering education.

翻译：大型语言模型正日益成为软件工程教育的一部分，包括涉及经验软件工程和证据综合的活动。本文报告了一项教学经验，将反思性的大语言模型使用整合到一门三年级软件架构课程的经验方法作业中。学生被要求使用快速综述或灰色文献综述方法撰写一篇简短的研究论文，并披露在整个作业中如何使用大语言模型。我们采用结合大语言模型辅助分类与研究者手动验证和细化的交叉分析流程，分析了146份学生披露声明。反思描述了学生如何在头脑风暴、方法澄清、发现组织与写作润色等活动中使用大语言模型，同时也报告了对生成内容不准确性和验证问题的担忧。本经验报告讨论了将人工智能辅助技术整合到经验软件工程教育中的经验教训与教育启示。