Real talk though, I’m seeing more and more of my peers in university ask AI first, then spending time debugging code they don’t understand.
I’ve yet to have chat gpt or copilot solve an actual problem for me. Simple, simple things are good, but any problem solving i find them more effort than just doing the thing.
I asked for instructions on making a KDE Widget to get weather canada information, and it sent me an api that doesn’t exist and python packages that don’t exist. By the time I fixed the instructions, very little of the original output remained.
As a prof, it’s getting a little depressing. I’ll have students that really seem to be getting to grips with the material, nailing their assignments, and then when they’re brought in for in-person labs… yeah, they can barely declare a function, let alone implement a solution to a fairly novel problem. AI has been hugely useful while programming, I won’t deny that! It really does make a lot of the tedious boilerplate a lot less time-intensive to deal with. But holy crap, when the crutch is taken away people don’t even know how to crawl.
Seem to be 2 problems. One is obvious, the other is that such tedious boilerplate exists.
I mean, all engineering is divide and conquer. Doing the same thing over and over for very different projects seems to be a fault in paradigm. Like when making a GUI with tcl/tk you don’t really need that, but with qt you do.
I’m biased as an ASD+ADHD person that hasn’t become a programmer despite a lot of trying, because there are a lot of things which don’t seem necessary, but huge, turning off my brain via both overthinking and boredom.
But still - students don’t know which work of what they must do for an assignment is absolutely necessary and important for the core task and which is maybe not, but practically required. So they can’t even correctly interpret the help that an “AI” (or some anonymous helper) is giving them. And thus, ahem, prepare for labs …
Real talk though, I’m seeing more and more of my peers in university ask AI first, then spending time debugging code they don’t understand.
I’ve yet to have chat gpt or copilot solve an actual problem for me. Simple, simple things are good, but any problem solving i find them more effort than just doing the thing.
I asked for instructions on making a KDE Widget to get weather canada information, and it sent me an api that doesn’t exist and python packages that don’t exist. By the time I fixed the instructions, very little of the original output remained.
As a prof, it’s getting a little depressing. I’ll have students that really seem to be getting to grips with the material, nailing their assignments, and then when they’re brought in for in-person labs… yeah, they can barely declare a function, let alone implement a solution to a fairly novel problem. AI has been hugely useful while programming, I won’t deny that! It really does make a lot of the tedious boilerplate a lot less time-intensive to deal with. But holy crap, when the crutch is taken away people don’t even know how to crawl.
Seem to be 2 problems. One is obvious, the other is that such tedious boilerplate exists.
I mean, all engineering is divide and conquer. Doing the same thing over and over for very different projects seems to be a fault in paradigm. Like when making a GUI with tcl/tk you don’t really need that, but with qt you do.
I’m biased as an ASD+ADHD person that hasn’t become a programmer despite a lot of trying, because there are a lot of things which don’t seem necessary, but huge, turning off my brain via both overthinking and boredom.
But still - students don’t know which work of what they must do for an assignment is absolutely necessary and important for the core task and which is maybe not, but practically required. So they can’t even correctly interpret the help that an “AI” (or some anonymous helper) is giving them. And thus, ahem, prepare for labs …
When AI achieves sentience, it’ll simply have to wait until the last generation of humans that know how to code die off. No need for machine wars.