物理教育

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[1] arXiv:2507.14860 (交叉列表自 cs.CY) [中文pdf, pdf, 其他]

标题： 人工智能聊天机器人在物理教师培训中的战略整合：教学法、认识论和网络安全维度的TPACK-SWOT分析 显示英文标题

标题： Strategic Integration of AI Chatbots in Physics Teacher Preparation: A TPACK-SWOT Analysis of Pedagogical, Epistemic, and Cybersecurity Dimensions

N. Mohammadipour

评论： 34页，3图，4表

主题： 计算机与社会 (cs.CY) ; 物理教育 (physics.ed-ph)

本研究通过在三个结构化学习活动中采用基于TPACK的SWOT框架，探讨了将人工智能驱动的聊天机器人战略性且认识论上负责任地融入物理教师教育。 该研究是在大学层面的创新教学工具顶点课程中进行的，活动通过聊天机器人辅助任务聚焦技术、教学法和内容知识（TPACK）的关键交汇点：简化抽象物理概念、构建符号概念图以及设计教学情景。 基于参与者的反思、课堂实物和迭代反馈，结果突显了内部优势，如增强的信息寻求行为、支架式教学计划以及对符号推理的支持。 同时，也出现了内部弱点，包括领域特定的不准确性、符号限制（例如LaTeX渲染错误）以及对AI输出过度依赖的风险。 外部机会包括促进包容性教育、多语言互动和扩展最近发展区（ZPD），而外部威胁则包括提示注入风险、机构访问差距和网络安全漏洞。 通过将现有的基于TPACK的模型扩展为包含人工智能素养、提示设计能力以及认识论验证协议等构念，本研究为在STEM教师培训中嵌入人工智能提供了理论上有依据且实际可操作的路线图。 研究结果证实，当得到批判性支撑时，人工智能聊天机器人可以在实施过程中与数字素养培训和机构支持相结合，从而支持物理教育中的元认知反思、伦理推理和教学创新。 显示英文摘要

This study investigates the strategic and epistemically responsible integration of AI-powered chatbots into physics teacher education by employing a TPACK-guided SWOT framework across three structured learning activities. Conducted within a university-level capstone course on innovative tools for physics instruction, the activities targeted key intersections of technological, pedagogical, and content knowledge (TPACK) through chatbot-assisted tasks: simplifying abstract physics concepts, constructing symbolic concept maps, and designing instructional scenarios. Drawing on participant reflections, classroom artifacts, and iterative feedback, the results highlight internal strengths such as enhanced information-seeking behavior, scaffolded pedagogical planning, and support for symbolic reasoning. At the same time, internal weaknesses emerged, including domain-specific inaccuracies, symbolic limitations (e.g., LaTeX misrendering), and risks of overreliance on AI outputs. External opportunities were found in promoting inclusive education, multilingual engagement, and expanded zones of proximal development (ZPD), while external threats included prompt injection risks, institutional access gaps, and cybersecurity vulnerabilities. By extending existing TPACK-based models with constructs such as AI literacy, prompt-crafting competence, and epistemic verification protocols, this research offers a theoretically grounded and practically actionable roadmap for embedding AI in STEM teacher preparation. The findings affirm that, when critically scaffolded, AI chatbots can support metacognitive reflection, ethical reasoning, and instructional innovation in physics education if implementation is paired with digital fluency training and institutional support.

替换提交 (展示 3 之 3 条目 )

[2] arXiv:2504.10621 (替换) [中文pdf, pdf, 其他]

标题： 以自我为中心的混合方法社会网络分析：对女性和/或LGBTQ+博士物理学家的访谈分析 显示英文标题

标题： Egocentric Mixed-Methods SNA: Analyzing Interviews with Women and/or LGBTQ+ Ph.D. Physicists

Chase Hatcher, Lily Donis, Adrienne Traxler, Madison Swirtz, Camila Manni, Justin Gutzwa, Charles Henderson, Ramón Barthelemy

主题： 物理教育 (physics.ed-ph)

社会网络分析（SNA）近年来在物理教育研究（PER）中被广泛使用，但主要局限于可用模态的有限范围内。 本文描述了一种独特的以自我为中心的混合方法SNA方法，该方法应用于从100名女性和/或酷儿专业物理学家的访谈中获得的定性网络数据。 我们关注从这些定性来源获取定量网络数据的方法，并提出了分析网络的新技术。 我们还探讨了以自我为中心的和混合方法SNA技术如何与批判方法一致，并非常适合研究物理空间和社区中的差异、非规范性以及边缘化经验。 我们探讨了这些方法的局限性和潜在应用，并将这项工作置于我们对这些访谈研究的更大背景中。 这项工作弥合了SNA与PER中关于身份的定性研究之间的方法论差距，并开始发展我们对性别和性少数物理学家所经历的支持方式的理解。 显示英文摘要

Social network analysis (SNA) has been widely used in physics education research (PER) in recent years, but mostly in a limited range of the available modalities. This paper describes a unique approach to egocentric, mixed-methods SNA applied to qualitative network data obtained from 100 interviews with women and/or queer professional physicists. We focus on our methods for obtaining quantitative network data from these qualitative sources and present novel techniques for analysis of the networks. We also examine the ways in which egocentric and mixed-methods SNA techniques are aligned with critical methods and well-suited to the study of difference, non-normativity, and experiences of marginalization in physics spaces and communities. We explore the limitations and potential applications of these methods and situate this work in the larger context of our study of these interviews. This work bridges a methodological gap between SNA and qualitative work on identity in PER and begins to develop our understanding of the way gender and sexual minority physicists experience support.

[3] arXiv:2506.12403 (替换) [中文pdf, pdf, html, 其他]

标题： 弥合数字鸿沟：小型语言模型作为欠发达地区物理和光子学教育的途径 显示英文标题

标题： Bridging the Digital Divide: Small Language Models as a Pathway for Physics and Photonics Education in Underdeveloped Regions

Asghar Ghorbani, Hanieh Fattahi

主题： 物理教育 (physics.ed-ph) ; 人工智能 (cs.AI) ; 计算机与社会 (cs.CY)

有限的基础设施、稀缺的教育资源和不可靠的互联网接入常常阻碍欠发达地区的物理和光子学教育。 这些障碍在科学、技术、工程和数学（STEM）教育中造成了深刻的不平等。 本文探讨了小型语言模型（SLMs）如何作为一种紧凑的、由人工智能驱动的工具，在低功耗设备上离线运行，提供可扩展的解决方案。 通过充当虚拟导师，实现本族语教学，并支持互动学习，SLMs可以帮助解决合格教师和实验室资源短缺的问题。 通过针对人工智能技术的投资缩小数字鸿沟，SLMs为推进STEM教育并在边缘化社区中促进科学赋权提供了可扩展且包容的解决方案。 显示英文摘要

Limited infrastructure, scarce educational resources, and unreliable internet access often hinder physics and photonics education in underdeveloped regions. These barriers create deep inequities in Science, Technology, Engineering, and Mathematics (STEM) education. This article explores how Small Language Models (SLMs)-compact, AI-powered tools that can run offline on low-power devices, offering a scalable solution. By acting as virtual tutors, enabling native-language instruction, and supporting interactive learning, SLMs can help address the shortage of trained educators and laboratory access. By narrowing the digital divide through targeted investment in AI technologies, SLMs present a scalable and inclusive solution to advance STEM education and foster scientific empowerment in marginalized communities.

[4] arXiv:2505.19099 (替换) [中文pdf, pdf, html, 其他]

标题： 看物理：看见有助于思考吗？ -- 基于视觉的物理推理基准测试 显示英文标题

标题： SeePhys: Does Seeing Help Thinking? -- Benchmarking Vision-Based Physics Reasoning

Kun Xiang, Heng Li, Terry Jingchen Zhang, Yinya Huang, Zirong Liu, Peixin Qu, Jixi He, Jiaqi Chen, Yu-Jie Yuan, Jianhua Han, Hang Xu, Hanhui Li, Mrinmaya Sachan, Xiaodan Liang

评论： 46页

主题： 人工智能 (cs.AI) ; 物理教育 (physics.ed-ph) ; 流行物理 (physics.pop-ph)

我们提出SeePhys，这是一个大规模多模态基准，用于基于物理问题的LLM推理，问题范围从中学到博士资格考试。 该基准涵盖了7个基础领域，覆盖物理学学科，包含21类高度异构的图表。 与之前的工作不同，视觉元素主要作为辅助用途，我们的基准包含大量视觉必要问题（75%），这些问题是必须进行视觉信息提取才能正确解答的。 通过广泛的评估，我们发现即使是最先进的视觉推理模型（例如Gemini-2.5-pro和o4-mini）在我们的基准上的准确率也低于60%。 这些结果揭示了当前大型语言模型在视觉理解能力方面的根本性挑战，特别是在：(i) 建立图表解释与物理推理之间的严格耦合，以及(ii) 克服它们对文本线索的持续依赖作为认知捷径。 显示英文摘要

We present SeePhys, a large-scale multimodal benchmark for LLM reasoning grounded in physics questions ranging from middle school to PhD qualifying exams. The benchmark covers 7 fundamental domains spanning the physics discipline, incorporating 21 categories of highly heterogeneous diagrams. In contrast to prior works where visual elements mainly serve auxiliary purposes, our benchmark features a substantial proportion of vision-essential problems (75%) that mandate visual information extraction for correct solutions. Through extensive evaluation, we observe that even the most advanced visual reasoning models (e.g., Gemini-2.5-pro and o4-mini) achieve sub-60% accuracy on our benchmark. These results reveal fundamental challenges in current large language models' visual understanding capabilities, particularly in: (i) establishing rigorous coupling between diagram interpretation and physics reasoning, and (ii) overcoming their persistent reliance on textual cues as cognitive shortcuts.