Deserved it. Shouldn't have beem a racist xenophobe. Hate speech and incitement of violence is not legally protected in the UK. All those far-right rioters deserves prison.

At least with AI it's easy to see how shitty it gets as the codebase grows working on even a toy project over a week. Then again, if you have no frame of reference maybe that doesn't feel as awful as it should.

But do you also sometimes leave out AI for steps the AI often does for you, like the conceptualisation or the implementation? Would it be possible for you to do these steps as efficiently as before the use of AI? Would you be able to spot the mistakes the AI makes in these steps, even months or years along those lines? The main issue I have with AI being used in tasks is that it deprives you from using logic by applying it to real life scenarios, the thing we excel at. It would be better to use AI in the opposite direction you are currently use it as: develop methods to view the works critically. After all, if there is one thing a lot of people are bad at, it's thorough critical thinking. We just suck at knowing of all edge cases and how we test for them. Let the AI come up with unit tests, let it be the one that questions your work, in order to get a better perspective on it.

The Convergiance is beginning. Altman Be Praised!!

It has their name and where it came from so. Yes? That's not what I asked. Are you expecting people to direct link everything even when it is already atributed? I mean is that really too much to expect of people? To simply copy the link where they found the information and post it along with where they shared it?

A carrot perhaps... Or a very big stick.

In those situations I usually enable 1.5x.

Sorry for the late reply, I had to sit and think on this one for a little bit. I think there are would be a few things going on when it comes to designing a course to teach critical thinking, nuances, and originality; and they each have their own requirements. For critical thinking: The main goal is to provide students with a toolbelt for solving various problems. Then instilling the habit of always asking "does this match the expected outcome? What was I expecting?". So usually courses will be setup so students learn about a tool, practice using the tool, then have a culminating assignment on using all the tools. Ideally, the problems students face at the end require multiple tools to solve. Nuance mainly naturally comes with exposure to the material from a professional - The way a mechanical engineer may describe building a desk will probably differ greatly compared to a fantasy author. You can also explain definitions and industry standards; but thats really dry. So I try to teach nuances via definitions by mixing in the weird nuances as much as possible with jokes. Then for originality; I've realized I dont actually look for an original idea; but something creative. In a classroom setting, you're usually learning new things about a subject so a student's knowledge of that space is usually very limited. Thus, an idea that they've never heard about may be original to them, but common for an industry expert. For teaching originality creativity, I usually provide time to be creative & think, and provide open ended questions as prompts to explore ideas. My courses that require originality usually have it as a part of the culminating assignment at the end where they can apply their knowledge. I'll also add in time where students can come to me with preliminary ideas and I can provide feedback on whether or not it passes the creative threshold. Not all ideas are original, but I sometimes give a bit of slack if its creative enough. The amount of course overhauling to get around AI really depends on the material being taught. For example, in programming - you teach critical thinking by always testing your code, even with parameters that don't make sense. For example: Try to add 123 + "skibbidy", and see what the program does.