标题： AI支持的微型实验室：结合基于智能手机的实验和多模态AI 显示英文标题

标题： AI-Supported Mini-Labs: Combining Smartphone-Based Experiments and Multimodal AI

摘要： 本文介绍了人工智能支持的微型实验室的概念，将基于智能手机的实验与多模态大语言模型（MLLMs）相结合。智能手机凭借其集成的传感器和计算能力，可作为物理教育的多功能移动实验室。虽然它们能够收集丰富的实验数据，但复杂日常现象的分析在课堂上往往受到限制。MLLMs的进展现在使学习者能够处理多模态数据、文本、图像、音频和视频，并在实验设计、数据分析和科学解释方面获得支持。三个案例研究突出了该方法：从加速度计数据中确定车辆阻力系数，从闪电生成的信号分析得到的音频频谱图中测量电离层反射高度，以及使用智能手机视频实时分析血容量动力学。结果表明，与传统方法相比具有明显优势，包括节省时间、高质量的可视化效果和个性化指导。除了简化数据分析，人工智能增强的便携实验室促进了表征能力、批判性思维和21世纪技能。这种混合方法为个性化和探究式科学教育提供了一条有前景的途径，尽管需要进一步研究来评估长期学习效果和潜在风险。 显示英文摘要

摘要： This paper presents the concept of AI-supported Mini-Labs, combining smartphone-based experiments with multimodal large language models (MLLMs). Smartphones, with their integrated sensors and computational power, function as versatile mobile laboratories for physics education. While they enable the collection of rich experimental data, the analysis of complex everyday phenomena has often been limited in the classroom. Advances in MLLMs now allow learners to process multimodal data, text, images, audio, and video, and receive support in experiment design, data analysis, and scientific interpretation. Three case studies highlight the approach: determining a vehicle drag coefficient from accelerometer data, measuring the ionospheric reflection height from lightning-generated signals analyzed as audio spectrograms, and real-time spectroscopy of blood volume dynamics using smartphone video. The results show clear advantages over conventional methods, including time savings, high-quality visualizations, and individualized guidance. Beyond simplifying data analysis, AI-augmented pocket labs foster representational competence, critical thinking, and 21st-century skills. This hybrid approach offers a promising pathway for individualized and inquiry-based science education, though further studies are needed to assess long-term learning effects and potential risks.