While advances in materials informatics have accelerated the development of Self-Driving Laboratories (SDLs), human-led experiments remain standard in many educational and exploratory research laboratories. In specific lab settings, formal documentation alone is often insufficient for safe and reliable operation. We refer to the gap between formal documentation and reliable execution in such settings as the experimental last mile; this gap mainly involves site-specific operational know-how, including local rules, routine checks, procedural details, and safety-conscious actions that are can be verbalizable but are often under-documented in standard manuals. In this proof-of-concept study, we developed a human-in-the-loop AI assistant that combines first-person experimental video, multimodal AI, and retrieval-augmented generation (RAG). Using powder X-ray diffraction experiments and student-recorded video data as inputs, the system extracts site-specific laboratory knowledge from recorded procedures, including physical techniques and audible confirmation that conventional manuals could omit. It then provides grounded responses based on the resulting manual. To reduce the risk of unsupported outputs, the system employs a two-layer safety design: source restriction through RAG and strict system-prompt constraints. Instructor-based evaluation showed alignment with expected guidance for questions covered by the manual. For out-of-scope queries, the system appropriately refused to answer, indicating a reduced risk of hallucination. Expert evaluation further indicated that the generated advisory reports were useful and safe (utility: 3.25/4.00; safety: 4.00/4.00). These results suggest the feasibility of a framework for bridging the experimental last mile in which AI supports laboratory practice under explicit human supervision rather than replacing human judgment.

翻译：尽管材料信息学的进步加速了自驱动实验室（Self-Driving Laboratories, SDLs）的发展，但在许多教学性和探索性研究实验室中，人类主导的实验仍是标准模式。在特定实验室环境中，仅凭正式文档往往不足以确保安全可靠的实验操作。我们将此类环境中正式文档与可靠执行之间的差距称为实验最后一英里；这一差距主要涉及特定场所的操作隐性知识，包括本地规则、常规检查、流程细节以及安全意识行为——这些知识虽可口头表述，但在标准手册中常缺乏记录。在这项概念验证研究中，我们开发了一种人机协同AI助手，该助手结合了第一人称实验视频、多模态AI与检索增强生成（Retrieval-Augmented Generation, RAG）。以粉末X射线衍射实验和学生录制的视频数据为输入，系统从记录的操作流程中提取场地特异性实验室知识，包括传统手册可能省略的物理操作技巧和听觉确认信息。随后系统基于生成手册提供有依据的响应。为降低无依据输出的风险，系统采用双层安全设计：通过RAG进行来源限制，并施加严格的系统提示约束。基于指导教师的评估表明，对于手册涵盖的问题，系统回答与预期指导一致；对于超出范围的问题，系统会恰当拒绝回答，显示出降低幻觉现象的风险。专家评估进一步指出，生成的咨询报告兼具实用性与安全性（实用性：3.25/4.00；安全性：4.00/4.00）。这些结果表明，构建弥合实验最后一英里的框架具有可行性——在该框架中，AI在人类明确监督下支持实验室实践，而非替代人类判断。