AI 中文总结
探讨在智能人工智能时代计算方法教学与评估，提出工具不变框架，指出验证是关键技能，阐述评估变化，介绍针对小班课程的实际应对，强调计算物理课程中学生解释和捍卫计算工件能力的重要性。
AI 中文摘要
学习计算方法一直意味着学习操作一种工具，如铅笔、计算尺、计算器或编程语言。智能人工智能能根据自然语言规范编写、执行和修改模拟代码，是机械工作从人类向工具迁移漫长过程中的最新且最大的一步。本文认为学习者必须知道的内容保持相对稳定，包括方法的输入输出、其功能概念等。将这些要求组织成一个涵盖从个位数加法到智能编排分子动力学的工具不变框架，指出验证而非代码编写是关键技能，并得出评估的结论：当能按需生成工件时，工件不再能证明学生的能力。描述了针对小班课程的实际应对措施，认为计算物理课程的真正产物是学生用学科语言解释和捍卫计算工件的能力。
英文摘要
Learning a computational method has always meant learning to operate a tool -- pencil, slide rule, calculator, or programming language. Agentic artificial intelligence, which writes, executes, and revises simulation code from natural-language specifications, is the latest and largest step in a centuries-long migration of mechanical work from human to tool. I argue that what a learner must know has remained remarkably stable: the inputs and outputs of a method, the concept of what it does, the terminology to communicate about it, the judgment to evaluate its results, and the skill of operating the current tool. This paper organizes these requirements into a tool-invariant framework spanning single-digit addition to agent-orchestrated molecular dynamics, argues that verification -- not code authorship -- is now the load-bearing skill, and draws the consequence for assessment: when artifacts can be generated on demand, the artifact no longer certifies the student. I describe a practical response, designed for the small classes where the subject lives -- AI-free in-class coding quizzes paired with oral defenses of comment-stripped, AI-assisted work -- and argue that the real product of a computational physics course is the student's ability to explain and defend computational artifacts in the language of the discipline.
