Comment by Waterluvian

It’s better than nothing but the problem is students will figure out feeding step 2 right back to the AI logged in via another session to get 3.

This is actually a great way to foster the learning spirit in the age of AI. Even if the student uses AI to arrive at an answer, they will still need to, at the very least, ask the AI to give it an explanation that will teach them how it arrived to the solution.

Props to the teacher for putting in the work to thoughtfully grade an AI transcript! As I typed that I wondered if a lazy teacher might then use AI to grade the students AI transcript?

That's roughly what we did as well. Use anything you want, but in the end you have to be able to explain the process and the projects are harder than before.

If we can do more now in a shorter time then let's teach people to get proficient at it, not arbitrarily limit them in ways they won't be when doing their job later.

Oh wow this is a great reference/image/metaphor for "software engineers" who misuse these tools - "the great pakledification" of software

People have tried that with everything from COBOL to low code. Its even succeeded in some problem domains (e.g. thing people code with spreadsheet formula) but there is no general solution that replaces programmers entirely.

That was literally the opposite of my intention. Maybe the choice of word wasn't perfect, but basically, I was trying to highlight that domain expertise is still valuable in the specific scenario of software engineering.

The same could be said about any other job, if you put me against a construction worker and give us both expensive power tools, he will still do a better job than me because I have no experience in that domain.

You misunderstand me. While I left Windows over a decade ago, I recognize it was a great OS in some aspects. I was referring to the recent AI fueled Windows developments and Ad riddled experiences. Someone decided that is fine, and you won't see orgs or regular users drop it...tolerance.

The other day I said something along the lines of, "be interested in the class, not the instance" and I meant to try to articulate a sense of metaprogramming and metaanalysis of a problem.

Y is causing Z and we should fix that. But if we stop and study the problem, we might discover that X causes the class of Y problem so we can fix the entire class, not just the instance. And perhaps W causes the class of X issue. I find my job more and more being about how far up this causality tree can I reason, how confident am I about my findings, and how far up does it make business sense to address right now, later, or ever?

is it? I really fail to see the metaphor as an F1 fan. The cars do not change that much; only the setup does, based on track and conditions. The drivers are fairly consistent through the season. Once a car is built and a pecking order is established in the season, it is pretty unrealistic to expect a team with a slower car to outcompete a team with a faster car, no matter what track it is (since the conditions affect everyone equally).

Over the last 16 years, Red Bull has won 8 times, Mercedes 7 times and Mclaren 1. Which means, regardless of the change in tracks and conditions, the winners are usually the same.

So either every other team sucks at "understanding the requirements and the technical challenges" on a clinical basis or the metaphor doesn't make a lot of sense.