AI Policy for Professor Bear's Courses | Using AI Responsibly in Class

AI Policy for Professor Bear's Courses | Using AI Responsibly in Class

AI Policy for All My Courses

This video covers the essential AI policy for all courses taught by Professor Nik Bear Brown. Key points:

You CAN use AI in all courses, but the purpose is to help you learn, not to avoid doing work

CITATION is mandatory - always cite AI assistance and original source materials Be prepared to EXPLAIN your work to TAs or the professor at any time

20% of your grade is based on RELATIVE quality compared to your peers' submissions

Your ATTITUDE toward AI matters - using it lazily produces mediocre results, while iterative refinement creates exceptional work

This policy applies to all my courses. Use AI to create, improve, and learn - not just to complete assignments quickly.

Links: Nik Bear Brown @nikbearbrown NikBearBrown.com https://spotify.nikbearbrown.com https://muzak.nikbearbrown.com https://www.nikbearbrown.com/ https://github.nikbearbrown.com https://youtube.nikbearbrown.com https://prompts.nikbearbrown.com

Transcript

A lightly formatted transcript of the video, with timestamps so you can jump to any section on YouTube.

[0:00] Okay. Uh bear here. Um this is AI policy for all of my courses. It's the same for all of my courses. Obviously, since I teach AI, you can use AI in the courses. However, the purpose of using AI is not to have it do the work for you, but to help you learn. I'll be using AI in class all the time, showing it in class. So, the first thing you need to do is site it. I'll give you lots of examples and starter assignments. A lot of assignments I've done in previous quarters are on my GitHubs to site what was the original thing. So you're not

[0:37] Allowed basically to take an old assignment, do nothing to it, and turn that in as yours. The purpose of is to take that assignment and improve it and change it and make it better. Which means you need to site the original thing. So what was different between what you did and what you started with? The TAs at any period of time can ask you questions about what you turn in as can I. So you're expected, even if you're using AI to help you write code, you're expected to be able to explain that code. If you're using AI to help you write something about a topic and

[1:16] Equation or whatever, you're expected to be able to explain that. So if you can explain that that's not cheating in the sense that you will fail but you'll get markdown points if you do not understand you know if you can't demonstrate if you can't explain what you've turned in then you're you're doc some points so this is the main thing citation and then at any point a TA can ask you once you turn something in your expectation is that you can explain what you turned um to me or to a TA. The other big thing is because everybody can use AI, everybody can turn in pretty good work

[2:02] Pretty quickly, like within five minutes. You could do most of my assignments in 15 minutes with AI. It can also take you days to do it if you really do what you do with AI, which is to start with AI to refine it, refine it, iterate, refine, iterate, refine, iterate, refine. So because everybody can use AI, your score, 20% of your score is relative to what other people in the class do, which you don't know. But that's also the real world. When you apply for a job, you don't know what the other people applying to the job did. But if they did more than you or what they did

[2:40] Is of higher quality than you, then they're going to get the job and you're not going to get the job. So this was the first week of class. If you don't accept this, don't take the class. It's a qualitative thing. It's a judgment thing. It's only 20% of the grade. But I'll go through lots of examples of the class. It'll be obvious sort of work that people spent no effort on and work that people put a lot of effort into. And those will be obvious in the quality of the work. And that's a qualitative decision. And the TAs will what the TAs will all do is they'll look at everything before

[3:17] They grade to get a sense of who's done what and then go back and do the grading based on what they know is what they perceive is the best work and what they perceive as the worst work and everything in between. Just realize that this is a qualitative thing but it's 20% of your grade. You can get 80 just by, you know, doing what's required of you in the actual bullet points of the rubric. 20% is relative score. So that's probably the big thing. The main thing about AI is your attitude. If you want to use it to do your work for you, you can. But you're going to

[3:55] Turn in sort of mediocre work if you do that. But it's not cheating. It's just crappy work. You can also use AI to create amazing work, absolutely stunning stuff that you probably could not do on your own. But that requires the effort to want to okay, this isn't quite good enough. How can I improve this? This could be better. How could I improve that? That's all attitude. But if you and we'll teach you how to do that in the class, just improve, improve, improve, improve, improve. So that's it for the AI policy. This is for all my courses. So I'm going to triply upload this video under all of

[4:33] The course things. Or actually, I uploaded once and put it in three playlist or four playlists or guess I'm teaching five courses. Five playlists. This is the same AI policy. So to review AI is okay, but you got to one demonstrate that you understand it to a human being ITA or me and that everybody can use AI which means the quality of work has improved for everyone. So for you to get an A in the course, A minus in the course, you need to be basically in the top half of the course in terms of the quality of the work. Okay, that's it. Uh, until next time. Um,

[5:13] This is only for my classes, so I don't know why anybody who's not in my classes would watch this, but it is what it is.