[dansmyers]

I'm a CS professor at an undergraduate college. I'm currently teaching a class on AI coding for non-programmers and also using Claude Code for all the projects in my upper level courses. I'm also planning to update our first course in the fall to include an intro to agentic programming.

Here are some practical suggestions based on what I've seen with my students. To summarize: AI programming de-emphasizes the specifics of languages and frameworks but rewards having a good knowledge of systems and a careful, structured development process.

1. Do learn some by-hand programming in a standard language like Python. Even if you aren't looking at the AI-generated code, this will teach you the building blocks of software design - things like functions, classes, files, and data types - which will help you design more complex applications. The ability to look at a program and reason about what it's doing is a key skill.

2. The terminal environment if you aren't already using it. This will unlock additional levels of control and help you understand how agents use tools.

3. Architecture of the applications you're creating. If you're making web apps, for example, learn about the front-end and back-end, how they exchange information, how a back-end database works, etc. The key is not the low-level details of those things, but how an application is divided into parts that exchange data with each other. This knowledge helps you move from a general concept for an application to an actual design.

4. Related to that point: the concept of encapsulation in software design. This is the idea that each part of your system should be self-contained and exchange information with other parts through a well-defined interface. If a component is encapsulated, you can change its internal details without messing with the rest of the system. This is important for AI, because it allows you to carefully control the targets of your generations.

5. Specs-driven development. This is the evolution of vibe coding and is the main approach I teach now. Chat about the problem, then develop a detailed spec that describes the desired behavior. Refine that into a system design and detailed step-by-step task list with tests for each step. Working with the AI to compare design options is a great learning tool.

6. You don't need to learn much about algorithms unless you want to. Models are very strong at choosing and implementing all of the standard algorithms. Let these emerge naturally as you work on interesting problems.

[gmichnikov]

This seems like the most helpful response for someone like OP. Is the curriculum or syllabus of your class public?

[dansmyers]

All of my course materials are on GitHub: https://github.com/dansmyers

The Programming with AI class is here:

https://github.com/dansmyers/ProgrammingWithAI

We're building it out as the semester goes on. Right now, it's mostly the in-class practice activities we're working through each day. This is the first time I've taught this version of the class so it's pretty experimental.

Here's the complete version of my intro class with notes, labs, and projects:

https://github.com/dansmyers/IntroToCS/tree/Fall-2025