Plus it worries me that people will no longer know how to do anything basic and we will at some point in the future be slaves to AI if we aren't careful.

If it wasn't for grinding all of the hard problems and hands-on projects and various programming tasks I had to do by hand over the years, I wouldn't be able to keep up with the agent, and wouldn't be able to get good results out of it, because I wouldn't know what it was doing and wouldn't be able to steer it appropriately. My schoolwork 15 years ago was more than formative, it remains critically foundational for my job work today.

Based on the other comment it sounds like maybe I need to give Fable another chance, and then I suppose I won't have to steer anything at all. But at the end of the day I am not paid to be a meat proxy, I am paid to deliver things for the business that employs me, and the things I deliver are my responsibility, not Anthropic's.

So I say, embrace AI as a learning tool, let students use it to bypass programming on assignments where the programming is of secondary importance, require oral defense/presentation for projects, make homework worth a much smaller fraction of final grade, and make all of your tests in-class and hand-written.

The reality is that the entire nature of education has to change to account for cheating becoming essentially free and virtually impossible to detect. Grinding through a problem set in the library is still tremendously important, but it's going to be harder to motivate students to do it without just cheating. Some students are conscientious and will do it anyway. Some won't. The hard part is going to be crafting curricula and exams that separate the two kinds of students. Fortunately you share that problem with just about every other professor in the world right now, so at least you shouldn't feel responsible for solving it all yourself.

Relevant anecdote: I used Wolfram Alpha to cheat on my calculus homework in freshman year, and then I got my butt kicked in the exam and a C final grade that never quite averaged its way out of my GPA. That felt horrible and it was a strong motivating force to get my shit together, but that's only because I'm a fucking nerd and I really cared about learning in school and making a career out of what I learned. On the other hand, I really was struggling with the material, which is why I tried cheating in the first place. Maybe if I had today's AI I could just ask it to help me learn better.

You need to teach your students why all of those are important and how to tell if the llm is giving them what they need.

From my experience working with data scientists, it outperforms humans on an extreme level. I’ve seen so much bad data science, and here I have a model that can do all of that from one prompt. It is getting really good on finding caveats in data too.

Is it reasonable to take up some of the class with statistical basics and intuition? E.g. you just referred to a bigger N; I don't know what level your students are but do they have a basic intuition for notions like "variance of estimator"? Or is that another class? I think my point is that even if they can nowadays press a button and have the most amazing reports and visualizations produced, that doesn't give anyone any actual understanding of the theoretical side of data science.

did you find tricks to turn LLMs into super-assistants so students can learn deeper ?

i also wonder if they're not losing motivation due to the fact that the llm can race through everything so swiftly

I'm so glad I learnt it before LLMs. I can't imaging trying to get good at Pandas/Polars coding nowdays when so much of my analysis is "Fable, please do this for me, k thx bye"