A Tool-Invariant Framework for Teaching and Assessing Computational Methods in the Age of Agentic AI

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.