从提示到物理定律：面向工程物理教育的生成式人工智能工作流

中文摘要 AI 辅助

本工作探讨了将生成式人工智能（AI）作为工程教育中入门物理课程合成实验内容来源的应用。利用This http URL、Grok Imagine和Pippit，生成了三个视频场景，分别代表不同的阻力机制：恒定摩擦、线性阻力和二次阻力。使用开源视频分析工具Tracker从生成的视频中提取运动学数据，随后通过Microsoft Excel中的非线性最小二乘回归将数据拟合到相应的解析模型。结果表明，合成数据与由牛顿第二定律推导出的经典运动学方程之间具有良好的一致性。从拟合参数中，每种情况都恢复了具有物理意义的量，其数值与文献中在假设条件下报道的值大体一致。一个反复出现的观察结果是，生成运动的物理合理性取决于用于生成视频的文本描述中包含的细节水平。更具体的描述往往产生更连贯的动力学行为，这表明输入提示的表述在塑造最终物理一致性方面起着重要作用，并可被视为实验设计过程的一个组成部分。生成式AI、基于视频的运动跟踪和曲线拟合的整合提供了一个完整的工作流，该工作流模拟了实验实践的关键阶段，从模型构建到定量验证。所提出的方法使学生参与实验设计、数据采集、参数估计和模型评估，通过广泛可用的工具促进物理模型建模方面的关键工程能力。总体而言，所提出的方法展示了生成式AI支持物理教育的潜力。

英文摘要

This work explores the use of generative artificial intelligence (AI) as a source of synthetic experimental content for introductory physics courses in engineering education. Using PixVerse.ai, Grok Imagine, and Pippit, three video scenarios were generated to represent distinct resistive force regimes: constant friction, linear drag, and quadratic drag. Kinematic data were extracted from the generated videos using Tracker, an open-source video analysis tool, and subsequently fitted to the corresponding analytical models through non-linear least-squares regression in Microsoft Excel. The results show good agreement between the synthetic data and the classical kinematic equations derived from Newton's Second Law. From the fitted parameters, physically meaningful quantities were recovered in each case, with values broadly consistent with those reported in the literature under the assumed conditions. A recurring observation is that the physical plausibility of the generated motion depends on the level of detail included in the text description used to generate the videos. More specific descriptions tend to produce more coherent dynamical behaviour, suggesting that the formulation of input prompts plays a relevant role in shaping the resulting physical consistency and can be regarded as an integral component of the experimental design process. The integration of generative AI, video-based motion tracking, and curve fitting provides a complete workflow that mirrors key stages of experimental practice, from model construction to quantitative validation. The proposed methodology engages students in experimental design, data acquisition, parameter estimation, and model evaluation, promoting key engineering competencies in modelling of physical models using widely available tools. Overall, the proposed methodology demonstrates the potential of generative AI to support physics education

发表机构

• Centro de Tecnologías Físicas, Universitat Politècnica de València（巴伦西亚理工大学物理技术中心）

机构由 AI 辅助整理，请以论文原文为准。