标题： 物理学教育的青蛙沸腾问题 显示英文标题

标题： The Boiling-Frog Problem of Physics Education

摘要： 令人惊讶的是，通用人工智能在基础物理中迅速跨越了熟悉的门槛。 比较连续模型的输出， GPT-5 思维远远超越了早期的套用公式倾向：在一个经典的电梯问题中，它进行符号运算，注意变量何时抵消，并验证结果；试图提示类似新手的行为主要影响语气，而不是方法。 在表示转换方面，该模型在 TUG-Kv4.0 上得分为 24/26（92.3%）。 在使用我的两份综合期末考试（60 个项目）作为卡片分类代理时，其分类反映了求解方法，而不是表面特征。 解决这些相同的考试，它得到 27/30 和 25/30，大部分错误出现在基于尺子的光路绘制和电路解释上。 在认识论方面，五次独立的 CLASS 运行得出 100% 的积极结果，表明一种模拟专家的立场。 以“青蛙煮沸”问题的形式，本文主张采取决定性行动：停止有学分的非监督封闭式在线评估；评估过程证据；使用纸张、白板；将权重转移到建模、数据和真实实验；要求透明、可引用的人工智能使用；重新构建问题类型；并依靠基于研究的教学和同伴学习。 机会在于突出人工智能无法替代的东西：对世界的建模，从证据中论证，以及做出有原则的近似。 显示英文摘要

摘要： It is astonishing how rapidly general-purpose AI has crossed familiar thresholds in introductory physics. Comparing outputs from successive models, GPT-5 Thinking moves far beyond the plug-and-chug tendencies seen earlier: on a classic elevator problem it works symbolically, notes when variables cancel, and verifies results; attempts to prompt novice-like behavior mainly affect tone, not method. On representation translation, the model scores 24/26 (92.3%) on TUG-Kv4.0. In a card-sorting proxy using two of my comprehensive finals (60 items), its categories reflect solution method rather than surface features. Solving those same exams, it attains 27/30 and 25/30, with most misses in ruler-based ray tracing and circuit interpretation. On epistemology, five independent CLASS runs yield 100\% favorable, indicating a simulated expert-like stance. Framed as a "boiling frog" problem, the paper argues for a decisive jump: retire credit-bearing unsupervised closed-response online assessments; grade process evidence; use paper, whiteboarding; shift weight to modeling, data, and authentic labs; require transparent, citable AI use; rebuild problem types; and lean on research-based instruction and peer learning. The opportunity is to foreground what AI cannot substitute for: modeling the world, arguing from evidence, and making principled approximations.