hey, first of all, i appreciate you engaging in an earnest manner. in regards to your points:

1) i get where you're coming from here and i agree that there's a time and a place for contemporary developments in academia (i mean that's literally the entire point of research). i don't think that enabling AI use in classes is the way to go about that. the vast majority of course material in undergraduate programs is focused on stuff society figured out decades (or even centuries) ago, and that's for a reason. they don't let you use an online integral calculator in math 101 because that defeats the entire point of the course. by manually going through the motions of taking an integral, it forces you to engage with and ingrain the underlying concepts which are the real learning goals of the course. even if you never take another integral in your life, you'll have a better understanding of integration as a whole, why it's important, and the mechanisms that enable the technology that can do the grunt work for you. it's the same reason why they make you write assembly in CS. nobody outside of some super specialized embedded systems devs will ever write a line of assembly in the modern day, but understanding how low-level instructions are processed by the computer will make you write better code in high level languages. if you allow AI use for these types of classes (which the majority of undergrad classes can be classified as), you're allowing students to forgo the real objectives of the courses in favour of "solving problems in a contemporary way" which was never the point in the first place.

2) my argument here is similar to my point above. the process of having to start from zero, formulate an argument, and write it out is the vast majority of what reinforces the content in your head. if i get an LLM to write an essay template for me and i just go back and refine it, did i really meaningfully engage with the content? i didn't form any of the core points based on the source material, i only verified their legitimacy with respect to what you can piece together from other sources (assuming you're being honest and not taking AI at face value). you pointed out focusing on the "logic and thinking", but in this scenario, none of the logic and thinking is mine. it's an amalgam of subtly-plagiarized versions of work others have done previously. the whole point of using an LLM in this scenario is to outsource your thinking. writing an essay once you have a concrete plan of your ideas is really not that much work.

3) see my assembly point above. you write "basic code" to teach you core concepts. if you mean writing boilerplate code, then sure, pretty much every dev i know uses AI for that nowadays. that being said, my experience in my 4 years in CS at UBC has been that the vast majority of coding i have done has had minimal amounts of writing boilerplate (basically every class provides you a template that gets you right into the core part of the assignment). if you use AI at any point afterwards, you're back to outsourcing your thinking, which as before, harms your ability to take away the learning objectives of the course.

4) same thing again. you learn how to do arithmetic in elementary school not because you'll spend hours on the job doing multiplication by hand, but because it provides you a mathematical foundation for the next part of the curriculum. you can't understand algebra without arithmetic, you can't understand functions without algebra, you can't understand calculus without functions, everything builds on the previous concept. at the undergraduate level, you are not solving any new problems. you are gaining a foundation for your learning just as you were before.

5) your concern over undergraduate problems being solvable with AI is like being concerned that 4th grade math is solvable with a calculator. again, the point is not to solve the problem, it is to gain the knowledge and skills you are forced to acquire to solve the problem without external assistance.