Employers Say Students Need AI Skills. What If Students Don't Want Them?

For the STEM students, what they need is statistics (with a focus on high level Bayesian and statistical learning theory, not just frequentist regression tricks) and differential equations. Then they can build AI. AI, or specifically deep neural network enabled machine learning, isn't some sort of magical black box solution with no weaknesses. (I will however admit that it is the best universal function approximator that we currently know of) Otherwise you end up with an uneducated public whose main education is from Hollywood and ChatGPT.

You need to start from high school, the AP classes need to be revamped. Currently they are focused on purely frequentist statistics. Frequentist statistics is great for most empirical sciences like biology. The formulas are mostly plug and play and even pure life science people with no mathematical talent can wield them without trouble. The problem is that they are very far from statistical learning.

Here's the current AP stats curriculum, it is meant to be equivalent to Stat 1.

https://library.fiveable.me/ap-stats

If you want to develop a strong foundation for ML, Unit 6, 7, and 8 ought to be thrown out entirely. The level they are taught at doesn't really teach anything more than plugging formulas. Unit 4.5 (Conditional Probability) and Unit 5 (Sampling) need to be further developed to cover the Bayesian theories, perhaps a segue into graphical models and Markov chains. Generative ML for example interprets likelihood as an information generator (since in the Bayesian formula, it is roughly the "inverse" of conditional probability), unfortunately most stats classes outside of physics and high level ML theory will never mention this. Heck most classically trained statisticians won't ever encounter this idea. But it is the bread and butter of generative AI. Having a vague idea of KL-divergence and what Metropolis-Hastings is coming out of high school is infinitely more useful for their career in ML than knowing how to fiddle with a p-value. You can teach most of these concept without calculus if you simplify some things and replace integrals with their discrete summation versions. Rejection sampling for example is very easy to teach. The Common Core needs a revamp, and perhaps it's time to shift away from the historical focus on calculus/pre-calc as the central tenet of pre-college mathematical teaching.

Who cares what people want \s

AI is just another tool. A pretty good one. And there is a skill to using it efficiently. If you ask it dumb questions, you will get dumb answers. Garbage in, garbage out.

I am a software developer and I hire devs as well. If somebody is ignorant of AI or refuses to use it, that is hard pass for me. It is not all that different from an accountant not wanting to use computers. Sure, you could still do some work. But, you will not be competitive.

I expect most people don’t want their preferences to be coddled in a way that leaves them at a disadvantage when they graduate.

AI is improving fast enough that maintaining fluency with its benefits seems like a credible “very good idea” for a lot of people.

I truly hope so, even IT majors at my college gave me weird looks when I said I used Excel and later Python to quickly do our math homework. I was taught very explicitly in my engineering course to do so, as my teacher had an Excel sheet made for calculating hydraulic cylinder requirements which inspired me to do all the work ahead of time. “Do everything to reduce downtime”, he’d say, because downtime is lost revenue.