WEBVTT NOTE This file was generated by Descript 00:00:00.205 --> 00:00:03.085 Jethro: Welcome to Artificial Intelligence, real Talk. 00:00:03.145 --> 00:00:18.985 I am very excited today to be joined by Michael Angel, who's a professor at American River College, which is where my two brothers went to college many, many years ago, back in the late nineties, early two thousands, and, uh, he didn't know them. 00:00:19.165 --> 00:00:21.820 I didn't know him, but we're together now. 00:00:21.820 --> 00:00:24.565 Michael, welcome to Artificial Intelligence, real Talk. 00:00:24.565 --> 00:00:25.345 Great to have you here. 00:00:25.432 --> 00:00:26.412 Michael Angelone: Thanks for having me. 00:00:26.597 --> 00:00:26.972 I appreciate it. 00:00:28.060 --> 00:00:48.160 Jethro: So, uh, the world has changed quite a bit over the past couple years as we've, uh, been exposed to artificial intelligence and you, you have this idea about how we, how we need to act in a world that is increasingly being shaped by machines. 00:00:48.520 --> 00:00:54.670 And I'm reading a book right now called, I believe it's called the Next Renaissance. 00:00:55.075 --> 00:00:59.485 I better look up that title and make sure that I, that I got it right. 00:00:59.485 --> 00:01:08.965 It's by the, um, guy who, uh, Crosby, I think, I'm just gonna look it up so I don't put my foot in mouth. 00:01:09.385 --> 00:01:21.895 So it's called the Next Renaissance AI and the Expansion of Human Potential by Zach Cass, who used to be the uh, the go to market guy for open ai and. 00:01:22.960 --> 00:01:24.970 Anyway, very interesting stuff. 00:01:25.330 --> 00:01:31.870 Um, and he, he has an optimistic view of it, AI and the expansion of human potential is how he views it. 00:01:33.520 --> 00:01:39.760 one of the things he talks about is that our world is shaped by machines and very much it. 00:01:39.760 --> 00:01:42.460 is going to continue being shaped by machines. 00:01:42.520 --> 00:01:45.070 And what does that bring about for you? 00:01:45.070 --> 00:01:47.320 What are some of the things you're thinking about that Michael? 00:01:48.767 --> 00:01:49.157 Michael Angelone: Yeah. 00:01:49.157 --> 00:02:01.697 And like my world, which is in the community college and in higher education, it's, it's, it's funny to think about how different learning tools have come and, and, and, and people are. 00:02:03.242 --> 00:02:15.752 Uh, reminded of like, course Hero, which is the most recent thing I think, which was, I don't know if you're familiar with it, but was a real threat to our way of life because, um, students were uploading your work. 00:02:15.812 --> 00:02:24.902 If you're a professor, they would like take your quizzes and they would get, it was gamified, so they would get points for uploading, like a prompt from Jethro's class or my class. 00:02:24.902 --> 00:02:27.272 And, and before that it was Wikipedia. 00:02:27.882 --> 00:02:32.592 And, um, there's a lot of analogies and metaphors thrown out on like what AI means. 00:02:32.592 --> 00:02:35.052 That's another noisy environment to be involved in. 00:02:35.052 --> 00:02:37.272 It's, uh, it's not a calculator. 00:02:37.662 --> 00:02:42.642 Um, it's not, in my opinion, as reducible as to just being a tool. 00:02:43.032 --> 00:02:44.472 Um, it's sort of more than that. 00:02:45.242 --> 00:02:50.102 Um, but the panic that that happens, I think is like all based on emotion. 00:02:50.102 --> 00:02:56.672 And so that's really been what I've been swimming in for like the last few years is, uh, panic reptilian mode. 00:02:56.912 --> 00:03:01.802 Um, but as far as your reference is concerned, I, I do think of it as a new renaissance. 00:03:01.802 --> 00:03:10.922 I've heard the Gutenberg press as another, as another analogy, but I think the best one I've come up, I, I, I, I don't know who I, I'm gonna, I'm stealing this from somebody, but, um. 00:03:10.967 --> 00:03:21.827 So I want to pay credit, but someone compared it to like the expansion, like the expanding universe and how we have to sort of see it as astrophysicists, see and measure the expanding universe. 00:03:22.127 --> 00:03:28.667 And they don't just throw their hands up in the air and go, oh, we can't measure the distance between us and that distant galaxy. 00:03:28.907 --> 00:03:31.967 Uh, they figure it out based on, on science and methodology. 00:03:31.967 --> 00:03:35.177 So I think that if we approach it with the same kind of method. 00:03:35.437 --> 00:03:39.457 And when I mean it, I mean the amorphous blob of generative ai. 00:03:39.697 --> 00:03:46.207 Um, both with like literacy in schools, um, and both in, um, and higher ed as well. 00:03:46.207 --> 00:03:53.557 Trying to address some of the things that it impacts, which, um, I think the elephant in the room isn't AI or generative ai. 00:03:53.557 --> 00:03:55.477 I think like we're too focused on that. 00:03:55.867 --> 00:03:56.677 Um, the real. 00:03:56.852 --> 00:04:10.292 The elephant in the room for me is, is uh, like this idea that it's a threat to learning and we're not sort of camped with that, with the idea as teachers of like the how to approach this new, um, this new discovery, if you will. 00:04:10.442 --> 00:04:14.222 So like, think of it more as a discovery of an expansion of a universe. 00:04:14.222 --> 00:04:19.922 And so that's why we're sort of wobbling and, and, and that and that messiness of what we do next. 00:04:21.860 --> 00:04:24.835 Jethro: I think that that part is really interesting because if you. 00:04:26.050 --> 00:04:33.070 If you think of it in one way, then it becomes this like existential threat that it's something that is happening to you. 00:04:33.400 --> 00:04:42.730 If you think about it another way, then it becomes this empowering, amazing thing that really can help you do things that you could never do before. 00:04:42.760 --> 00:04:46.090 And that's one of the things that I find so interesting. 00:04:46.090 --> 00:04:54.310 I recently finished my, uh, dissertation, which was that principals who use AI for. 00:04:55.165 --> 00:04:57.835 Innovation, create cognitive equity. 00:04:58.255 --> 00:05:04.915 Equity as in debt versus equity and principles who use AI for just to speed things up. 00:05:05.515 --> 00:05:07.375 They create cognitive debt. 00:05:07.465 --> 00:05:36.190 And there's a paper that came out last year called, um, that focused on creating this idea of cognitive death that you don't remember what AI creates and, and All this stuff, which is true, but at the same time, There's AI can create things that I could have never even fathomed and has significantly improved my life in some very specific ways, and in other ways has made no impact at all. 00:05:36.370 --> 00:05:42.065 You know, like it, has not improved my relationship with my wife or kids. 00:05:42.790 --> 00:05:43.450 At all. 00:05:43.660 --> 00:06:05.620 And I don't really expect it to, you know, but when it comes to a, a school environment, either higher ed or, or K 12, there are some very real things because to me, the biggest thing is that, uh, AI has made it so that all of these assignments and tests and projects that, that most schools have been working on 00:06:05.957 --> 00:06:06.017 Michael Angelone: All 00:06:06.610 --> 00:06:08.350 Jethro: are super easy to do with ai. 00:06:08.712 --> 00:06:09.062 Michael Angelone: right. 00:06:09.235 --> 00:06:16.435 Jethro: The AI can do a much better job anyway, and it kind of forces that conversation of, well, what is, what are we actually here for Then 00:06:16.892 --> 00:06:18.092 Michael Angelone: Oh, I, yes, 00:06:18.235 --> 00:06:19.495 Jethro: yeah, so talk a little bit about 00:06:19.527 --> 00:06:20.102 Michael Angelone: I've been having, I've been asking that 00:06:20.275 --> 00:06:20.905 Jethro: would get you going. 00:06:20.942 --> 00:06:28.052 Michael Angelone: question since like, uh, November of like 2020 or whatever it was when Chatt dropped. 00:06:28.477 --> 00:06:35.972 And, uh, my students, uh, in November were normal in the sense of like what I'd been dealing with for about at that point. 00:06:36.362 --> 00:06:40.562 It's been a minute now to, jeez, uh, 14 years in the, in the business. 00:06:40.892 --> 00:06:42.392 Uh, pretty normal, right? 00:06:42.392 --> 00:06:45.332 In terms of what students would turn in at the end of a semester. 00:06:45.362 --> 00:06:49.412 Carefully scaffolded portfolio based, process based approach to writing. 00:06:50.297 --> 00:06:52.667 More general composition courses, things of that nature. 00:06:52.667 --> 00:07:00.197 Not to bore your audience, but you know, really trying to give you a context, you know, your first semester English one A or one B class, right? 00:07:00.197 --> 00:07:03.737 That everybody who's entered college and gone through knows they need to take. 00:07:03.737 --> 00:07:03.887 Right. 00:07:03.887 --> 00:07:04.997 You gotta check those boxes. 00:07:05.237 --> 00:07:08.357 So you get, you get this, you get a be, you get a bell curve. 00:07:08.747 --> 00:07:12.497 But it was like, by December, everyone sounded like Toni Morrison, right? 00:07:12.497 --> 00:07:15.947 And everyone was gifted and great. 00:07:16.277 --> 00:07:18.677 Um, and I, and I had, you know, kind of. 00:07:19.262 --> 00:07:26.672 I'm, I'm, I'm pretty, I'm not a technocrat, but I'm pretty plugged in as an English professor to what's changing with technology, especially as it impacts me. 00:07:27.152 --> 00:07:30.092 Um, um, I wouldn't call myself a late adopter. 00:07:30.242 --> 00:07:34.802 I wouldn't either call myself an an, an, an innovator either by any stretch of the imagination. 00:07:34.802 --> 00:07:43.052 But with ai I would say that I probably, anything in my life, I'm probably a little bit in the back of the line on early adoption, but. 00:07:43.277 --> 00:07:46.607 With that said, um, I had a mental nervous breakdown, man. 00:07:46.607 --> 00:07:47.177 I had an extension. 00:07:47.177 --> 00:07:49.097 I as extension, like panic. 00:07:49.097 --> 00:07:50.717 It was no, no, no lie. 00:07:50.717 --> 00:07:56.597 I was calling friends in December going, can you, is your sales is, is, is, uh, is pharmaceutical sales? 00:07:56.597 --> 00:07:58.817 Well, how you doing inform surgical sales. 00:07:58.817 --> 00:08:00.407 I heard you guys do very well, right? 00:08:00.460 --> 00:08:00.790 Jethro: Yeah. 00:08:00.820 --> 00:08:02.980 Can I, can I get a job transfer right now? 00:08:03.070 --> 00:08:03.790 Change my career? 00:08:04.150 --> 00:08:04.600 Yeah. 00:08:05.597 --> 00:08:05.687 Michael Angelone: And. 00:08:05.687 --> 00:08:07.277 they're like, like my livelihood. 00:08:07.277 --> 00:08:08.507 It just completely shifted. 00:08:08.507 --> 00:08:15.677 So I was trying to tell that human tale because, um, in January I started to have less of like that emotional reaction. 00:08:15.677 --> 00:08:18.047 My brother's also like a, a, a psychologist. 00:08:18.047 --> 00:08:24.017 So we've, that makes me kind of like an armchair cognitive behavioral therapist on Monday morning. 00:08:24.092 --> 00:08:24.152 Um, 00:08:24.190 --> 00:08:24.520 Jethro: Yep. 00:08:25.562 --> 00:08:30.092 Michael Angelone: I have a little introspection in that area, so I was like, man, you know, I could do three things. 00:08:30.092 --> 00:08:32.792 Like, am I having an emotional reaction to my, to this? 00:08:32.822 --> 00:08:33.332 Yes. 00:08:33.662 --> 00:08:34.592 Is it going to you? 00:08:34.592 --> 00:08:35.972 Sort of, all right. 00:08:35.972 --> 00:08:37.112 Allow that to happen. 00:08:37.112 --> 00:08:43.532 Going through my like brother, sort of like voice in my head and that's, these are just data points, like, don't think about how it makes you feel. 00:08:43.532 --> 00:08:44.492 Just be glad you feel. 00:08:44.492 --> 00:08:45.242 Then go from there. 00:08:45.862 --> 00:08:46.282 Alright. 00:08:46.522 --> 00:08:52.822 And then, and from there it was like, okay, into community, into like discussions with other like-minded people. 00:08:52.822 --> 00:08:59.692 I think Steve Jobs called them, like what the, uh, the crazies or whatever that, the, you know, the group of people that just find my tribe. 00:08:59.962 --> 00:09:07.042 And I did, uh, with all due respect to my department at American River College, I kind of knew it wouldn't be happening there. 00:09:07.252 --> 00:09:12.022 Everyone was still kind of in frozen mode, uh, or in constable mode. 00:09:12.835 --> 00:09:13.105 Jethro: Yeah. 00:09:13.110 --> 00:09:22.435 Well, let me just address that real quick because this is the other thing that is a huge impact on what we're doing and, and does have a huge impact on students specifically. 00:09:23.710 --> 00:09:27.880 Is, at first everybody was like, not allowed to use ai. 00:09:27.880 --> 00:09:28.750 It's banned. 00:09:28.780 --> 00:09:30.640 And that was everybody's first response. 00:09:30.640 --> 00:09:32.230 And it was like, well wait a second. 00:09:32.560 --> 00:09:34.510 Maybe that's not the Right. 00:09:34.720 --> 00:09:35.650 response. 00:09:35.890 --> 00:09:40.150 But if you are in an environment, either at work or as a student. 00:09:40.630 --> 00:09:44.620 Where that is the response, then you can't even talk about it comfortably. 00:09:44.740 --> 00:09:47.440 You know, you can't even say like, what could I do with this? 00:09:47.477 --> 00:09:47.827 Michael Angelone: Right. 00:09:47.980 --> 00:09:57.220 Jethro: if you're in another place where they're like, oh, you know what, let's, let's figure out what we can do and figure some, figure out how this fits in, then it totally changes the whole, the whole game. 00:09:58.217 --> 00:09:58.787 Michael Angelone: Crazy. 00:09:58.787 --> 00:10:03.232 You mentioned that like this, like psychological safety alone was like a huge. 00:10:04.192 --> 00:10:08.782 You know, I don't have like numbers in front of me, but anecdotally it was like a huge piece of this pie. 00:10:08.812 --> 00:10:12.442 It was one students felt like, and there's ways I did that too. 00:10:12.442 --> 00:10:14.122 And I think that I doing that. 00:10:14.122 --> 00:10:14.782 Okay. 00:10:15.112 --> 00:10:17.782 Um, one full transparency. 00:10:17.932 --> 00:10:18.412 I'm doing it. 00:10:18.442 --> 00:10:18.862 Okay. 00:10:18.862 --> 00:10:21.232 'cause I'm a humble person and it's like, I don't know anything. 00:10:21.267 --> 00:10:23.367 Better than the throw spaghetti at the wall. 00:10:23.367 --> 00:10:27.477 See what sticks and not go insane like I did back when this thing dropped. 00:10:27.477 --> 00:10:29.367 So it's like, what do I, what am I doing here? 00:10:29.607 --> 00:10:33.357 And so like any scientist, it's like observe, see what's happening, what works. 00:10:33.357 --> 00:10:38.307 And I think, but with my, with students, that psychological safety that you're pointing to was so important. 00:10:38.307 --> 00:10:43.317 And that had to be, I think, developed through things they can see. 00:10:44.057 --> 00:10:50.537 Feel because I, we had a couple of panels in American River College that I was happy to host and we had students on, which was super important. 00:10:50.537 --> 00:10:59.627 And the number one thing they said, uh, each student across the board was they would rather have a ban on AI in their class than a teacher who's like. 00:10:59.822 --> 00:11:06.122 You can use ai, but to brainstorm and to chit and not really like lay it out. 00:11:06.302 --> 00:11:08.132 And it was like problem pain 0.1. 00:11:08.282 --> 00:11:15.367 I, I wanted to create some kind of like rubric where we could all discuss freely in my classroom what those degrees of usage. 00:11:16.212 --> 00:11:22.272 Even meant, and I've used some usage, seen some usage scales and other things that a lot of interesting smart people put together. 00:11:22.272 --> 00:11:24.612 And just like I said, went, let's see if this sticks. 00:11:24.672 --> 00:11:36.372 So my, my classroom became a point of psychological safety immediately because it was like my students were like, oh, okay, we're showing up to class, we're using it, and the teachers here, not just we're using it, but there's this guide rail. 00:11:36.632 --> 00:11:42.992 Framework that seems legit because it comes from this place called unesco, and these are like people with PhDs after their names. 00:11:42.992 --> 00:11:44.822 You know how the ethos goes for students. 00:11:45.062 --> 00:11:52.682 And then bam, once I had that buy-in for like, eh, I'm not gonna say everybody, you know, I wasn't Jaime Escalante, I wasn't slicing apples. 00:11:52.832 --> 00:11:53.882 There were a few students. 00:11:53.882 --> 00:12:02.042 And as there always should be, I think wonderfully, uh, to make the discourse community work, um, to be there and be like, you know what, can we opt out? 00:12:02.592 --> 00:12:13.392 So I've created even things like Bill of Rights, opt out clauses for each assignment, you know, just so that students feel like I can be here psychologically safe, using it, not using it. 00:12:13.422 --> 00:12:15.282 And so, so far so good. 00:12:15.642 --> 00:12:19.422 Um, it's been sort of successful, but my, my next thing is to measure it. 00:12:19.722 --> 00:12:31.362 So like, what my next thing is to sort of talk, uh, uh, more, more as many people as I can about how we measure learning now in an age of AI where there's transparency and ethical usage. 00:12:32.180 --> 00:12:35.060 Jethro: Well, let's talk a little bit about that, because like. 00:12:35.875 --> 00:12:47.605 My learning has grown exponentially since I've been using ai, but it is not in a way that makes sense in a classroom situation. 00:12:48.115 --> 00:13:01.525 And, and that to me is, is one of the biggest changes that have to happen for educators as they need to be able to know how to, how to judge learning in a way that is appropriate and. 00:13:02.230 --> 00:13:05.440 Uh, the way we've been doing it just isn't going to work anymore. 00:13:05.440 --> 00:13:09.970 So what are some of the conclusions you've come to with, with what that looks like for you? 00:13:10.532 --> 00:13:16.952 Michael Angelone: So a couple years ago for, I've been on a dozen or so committees in-house at my own campus and in my own district. 00:13:17.012 --> 00:13:22.197 So I'm not gonna name drop a bunch of bureaucratic acronyms, but one of the first ones was for like our. 00:13:22.215 --> 00:13:23.020 Jethro: appreciate that. 00:13:23.297 --> 00:13:26.627 Michael Angelone: Student Success Council, which is like a local thing. 00:13:26.627 --> 00:13:37.577 And it's, you know, it's, uh, I wanna say it was like four guys that were like interested in ai and then a supervisor, a manager, a ar uh, and so there, like, what do we do? 00:13:37.577 --> 00:13:38.507 And I, I was like, you know what? 00:13:38.507 --> 00:13:40.607 We really should start with a literature review. 00:13:40.817 --> 00:13:43.097 And everyone was in agreement and we spearheaded. 00:13:43.607 --> 00:13:45.617 Trying to put together was what was out there. 00:13:45.647 --> 00:13:51.047 And what we discovered was that there was some early usage of ai, not not of great studies. 00:13:51.317 --> 00:13:57.617 The MIT study had dropped, which is the fame, the, I like to call it the like sort of the infamous study because it was a small end. 00:13:57.737 --> 00:14:00.377 It was done with a group of students, graduate students. 00:14:00.437 --> 00:14:05.087 It really didn't kill the idea that AI killed learning like the tabloid headline, click bait. 00:14:05.672 --> 00:14:10.232 Thing that it did, and that's probably a whole nother podcast to talk about because it's still a matter of debate. 00:14:10.562 --> 00:14:12.002 And so that's what we're trying to do here, right? 00:14:12.002 --> 00:14:14.252 You know, uh, test this stuff to see if it works. 00:14:14.252 --> 00:14:18.692 So first things first, I just came back from as CC, C about this is, this is a narrative. 00:14:18.692 --> 00:14:24.602 This is a timeline from this a, this student Success council work, and now doing this statewide work. 00:14:24.872 --> 00:14:30.722 And the, the state of California community college system is basically saying, help us. 00:14:30.842 --> 00:14:32.822 Um, and the second thing they're saying is. 00:14:32.837 --> 00:14:37.697 Maybe we think that we gotta stop putting so much emphasis on a, on product assessment. 00:14:38.207 --> 00:14:47.987 And, um, I'm looking at my friend who was there with me going, you know, I, you know, it's one of those moments in time where it's like, I've been saying this for years now. 00:14:48.047 --> 00:14:50.897 Uh, and finally they're listening, but I don't get paid the big salary. 00:14:50.897 --> 00:14:51.527 Like this guy. 00:14:51.527 --> 00:14:56.597 Oh, Peter does, he's said and he's saying it, but at least he's shown up to my two breakout sessions, which is a good sign. 00:14:56.627 --> 00:14:56.927 Uh, 00:14:57.355 --> 00:14:57.715 Jethro: Yeah, 00:14:58.502 --> 00:14:58.802 Michael Angelone: you know. 00:14:58.945 --> 00:14:59.095 Jethro: it. 00:14:59.185 --> 00:15:05.815 So that's one of the things specifically for me that I've been saying for years that we need to have more focus on. 00:15:06.295 --> 00:15:15.925 Projects and process rather than on the end goal, which is how do you perform at one point in time and say, I get this. 00:15:15.925 --> 00:15:30.145 And, you know, as a, as a teacher and as a, as a principal, those were the things that I was driving for my, students as much as I could was this is how, this is what real learning looks like, and. 00:15:30.865 --> 00:15:41.065 And if you learn how to learn, then it doesn't matter what tools are there, because learning is a personal endeavor regardless of whatever else is going on. 00:15:41.065 --> 00:15:44.110 You can have two twins sitting in your classroom. 00:15:45.505 --> 00:15:56.155 those two kids, because it's a personal experience, are going to get different things from that class, even though they could have everything else in common. 00:15:56.575 --> 00:16:04.255 But because it's such a personal thing, then we have to figure out ways to, to balance that and figure out what learning actually looks like. 00:16:04.255 --> 00:16:13.285 Because our, experience is that learning looks like you do well on tests, and that may not be the thing that we use to measure. 00:16:13.675 --> 00:16:14.845 Uh, going forward. 00:16:15.212 --> 00:16:15.722 Michael Angelone: I can't. 00:16:16.172 --> 00:16:20.042 And, and that was like the, and, and honestly, um. 00:16:21.752 --> 00:16:34.712 I'm not gonna sound, I don't wanna sound like a blowhard, but when I, when I hit the wall there in my lab with that same idea, I, I probably in the same timeframe that probably the other educators hit that wall, you know, who were involved in generative ai? 00:16:35.042 --> 00:16:45.242 Um, both like how it was impacting them, you know how you had mentioned that it's making you smarter and then like imagining, 'cause you sound like a passionate person like me, imagining what it could do for students, right? 00:16:45.242 --> 00:16:46.112 Like if they had this. 00:16:46.587 --> 00:16:54.477 If you could share this power with them of how to and I, and then, so I hit this wall and I'm gonna get back to that how I heard you used the word judgment. 00:16:54.807 --> 00:16:59.547 And then, um, one thing I hear, uh, is like, how to restrain. 00:16:59.847 --> 00:17:01.167 So like, I want to get to that. 00:17:01.227 --> 00:17:03.507 Um, um, so put a, put a pin in that. 00:17:04.052 --> 00:17:07.172 But, so it was like, okay, so then how do I measure those things too? 00:17:07.382 --> 00:17:08.822 Are they important for measurement? 00:17:08.972 --> 00:17:14.282 Will it be convincing enough to like, you know, you know, except for a letter grade into a whole system. 00:17:14.282 --> 00:17:16.082 Well, I'll cross that bridge later. 00:17:16.142 --> 00:17:19.322 For now, I'll just go under the veil of academic freedom and test the stuff out. 00:17:19.562 --> 00:17:27.932 So I, I, I, I thought to myself, um, well, like the, one of the craziest polls that started me into this was, I don't wanna like throw. 00:17:28.347 --> 00:17:30.417 I'm not, I'm not in, first of all, I'm not in a fallacy. 00:17:30.567 --> 00:17:34.377 So it's like, just because my mom used to teach us, tell me when I was a little kid, I'm from New York. 00:17:34.377 --> 00:17:40.977 She would say, if your friends jumped off the Brooklyn Bridge, would you, you know, that old saying, so just because everyone's doing it doesn't make it cool. 00:17:40.977 --> 00:17:41.577 I know that. 00:17:41.817 --> 00:17:48.597 But like, and then there's all kinds of environmental concerns, and I'm not, I don't not want to put those on the back burner whatsoever. 00:17:48.597 --> 00:17:50.547 And from my, from my perspective. 00:17:50.547 --> 00:17:52.317 So I'm not necessarily about usage. 00:17:52.317 --> 00:17:55.317 My whole thing is impact and impact on the learner. 00:17:55.467 --> 00:17:57.112 So when, when you find out that night, like. 00:17:57.707 --> 00:18:11.987 86% of learners across the globe from two years ago in a study said they use it regularly now imagining how much usage of it is on a daily, you know, by any given student in the United States of America. 00:18:11.987 --> 00:18:12.707 So if we could just say. 00:18:13.057 --> 00:18:15.907 It's ubiquitous, even if they're unintentionally using it. 00:18:16.297 --> 00:18:38.467 That means we start having to have conversations about environmental sustainability when it comes to things that we use that are technology, whether it's GPS, discord accounts, red Dead Redemption Two, or ai, like we gotta teach our, there's a literacy involved in, if you use a tool and it uses like as much energy as it takes to charge your phone, then you should really think about why you're using it, right? 00:18:39.077 --> 00:18:39.677 That's one. 00:18:39.737 --> 00:18:46.037 So there's literacy and that has to be really baked in, excuse me, to the whole process-based approach that we're talking about. 00:18:46.097 --> 00:19:06.077 Because if there's no buy-in to, okay, I'm using this machine, it's gonna help me not, um, hit executive friction, but like exact, uh, allow me to sort of like develop my executive function, which is something that I think a lot of colleges assume our students kind of come in with. 00:19:07.217 --> 00:19:07.907 I went, aha. 00:19:08.117 --> 00:19:16.787 That's the place where we can be measure if they're using it, when they're using it, how they're using it, what questions they're asking, how they're iterating. 00:19:16.967 --> 00:19:29.507 And if like four basic areas for me on the executive function list are checking off, then I think I can measure that is basically my hypothesis and my approach to usage in my classes. 00:19:30.655 --> 00:19:31.405 Jethro: I like that. 00:19:31.405 --> 00:19:34.975 Just a, putting a pin in the environmental sustainability thing. 00:19:35.275 --> 00:19:40.915 Simon Wilson, who is very much in the uh, AI boat, um. 00:19:41.905 --> 00:19:50.395 Posted a, a post a while ago and has come back to this again and again about, uh, the AI water issue being fake. 00:19:50.845 --> 00:19:53.125 And so yes, it uses water. 00:19:53.275 --> 00:19:55.135 Yes, it has other impacts. 00:19:55.135 --> 00:19:59.155 However, it is, it is not as much as a lot of people are making it 00:19:59.232 --> 00:19:59.582 Michael Angelone: Right. 00:19:59.905 --> 00:20:01.015 Jethro: And I don't know the Right. 00:20:01.015 --> 00:20:09.565 answer here, but what I'm thinking about is the sustainability of relying on something that. 00:20:09.860 --> 00:20:23.210 You can't possibly understand and how that, the environmental thing is a question mark and we should be looking into that, but your own sustainability and using it. 00:20:23.270 --> 00:20:29.510 Like if you, if you are supposed to be learning and you're using AI to circumvent your learning, 00:20:29.697 --> 00:20:30.047 Michael Angelone: Great. 00:20:30.050 --> 00:20:31.640 Jethro: me that's a hard stop. 00:20:31.910 --> 00:20:33.290 Like you can't do that. 00:20:34.210 --> 00:20:41.080 But if you're using it to enhance your learning, that's a very different conversation and a very worthwhile one to have with kids. 00:20:41.080 --> 00:20:43.690 And they need to be able to understand that and articulate that. 00:20:44.020 --> 00:20:47.560 And I know your, your kids are adult students. 00:20:47.560 --> 00:20:50.680 My kids are, are younger than 18 students. 00:20:51.715 --> 00:20:53.875 But still, those are questions that need to be answered. 00:20:53.875 --> 00:21:02.635 And as I build stuff with it myself, need to know what, what is worthwhile, having it do it for me and what I should be doing myself. 00:21:02.635 --> 00:21:10.075 And I still after, uh, more than I, 'cause I was using it before chat. 00:21:10.075 --> 00:21:12.595 JBT came out just in a different way. 00:21:13.015 --> 00:21:17.875 Um, like for a long time I've been using and teaching about this stuff. 00:21:17.875 --> 00:21:18.355 And so. 00:21:19.495 --> 00:21:21.055 I still don't know the right answer to be 00:21:21.237 --> 00:21:21.587 Michael Angelone: Right. 00:21:22.997 --> 00:21:23.417 Yeah. 00:21:23.927 --> 00:21:24.647 You know, I don't know. 00:21:24.647 --> 00:21:26.567 And that's a whole like astrophysicist thing. 00:21:26.567 --> 00:21:37.577 I keep always going back to, it makes me like sleep better at night knowing that if I'm an astrophysicist, like you had mentioned, that framing maybe is bs, who knows? 00:21:37.817 --> 00:21:45.467 But I, I, I think it helps me in like not just throwing my hands up in the air and, um, and saying, well. 00:21:46.457 --> 00:21:48.077 Um, this is dangerous. 00:21:48.077 --> 00:21:52.577 I know it's dangerous in a lot of ways, just like I know autonomous vehicles are dangerous. 00:21:52.997 --> 00:22:06.797 Um, and the thing that we depend on, analogous leads with other things that we grow to depend on, that are technological, that are resource-based, that are finite, right? 00:22:07.217 --> 00:22:08.957 Like automobiles and other things. 00:22:09.197 --> 00:22:10.547 Start getting into like, you know. 00:22:11.132 --> 00:22:21.842 Um, not questions of banning or questions, but there are questions of like how we get the most use out of how we make environmental protection laws, how we protect drivers from one another. 00:22:22.022 --> 00:22:23.672 I mean, like Ralph Nader, right? 00:22:23.702 --> 00:22:26.372 Um, you know, how we protect ourselves from each other. 00:22:26.612 --> 00:22:30.152 Um, helmet laws from motorcyclists, you know, like the list goes on and on and on. 00:22:30.152 --> 00:22:34.292 When we talk about things that we depend on each day and not to get into some kind of like. 00:22:34.967 --> 00:22:37.007 Philosophical notion of like, you know, argument. 00:22:37.007 --> 00:22:44.267 But I really think that AI belongs in the category of something that we don't scapegoat per se, because I have read those articles and um. 00:22:46.292 --> 00:22:54.122 There's a certain amount of water that's used that's continuously recycled, is not like in continuous stream and things of that nature, so I don't know either. 00:22:54.362 --> 00:23:06.037 I just know that to stay informed is my job so that I'm relevant as an instructor so that I can make sure that this is baked into our curriculum so that our students know this too, so that they learn. 00:23:06.862 --> 00:23:14.242 And it's not just thrown into the category of critical or, uh, like, you know, um, digital literacy like that to me is a, I think that's a cop out. 00:23:14.452 --> 00:23:18.202 I think that it belongs in its own category, AI literacy. 00:23:18.352 --> 00:23:22.337 And I think that it's something that, um, our state, others lawmakers need to get. 00:23:22.722 --> 00:23:34.152 Need to get off of whatever they're doing and they need to figure it out because I think that accreditation come down to program outcomes, the things that people like you really care about more, more than a lot of things that you, that'll keep you up at night. 00:23:34.212 --> 00:23:34.602 I know. 00:23:34.812 --> 00:23:37.212 So it's like that reframing needs to happen. 00:23:37.212 --> 00:23:46.797 I think you unilaterally between senates, districts, boards, and local principals for their and their teacher staff and, and I think that's a, it's not. 00:23:47.247 --> 00:23:48.567 I think it's not as urgent. 00:23:48.597 --> 00:23:53.367 It doesn't have to happen overnight, but I think it needs to happen like as much time as it takes to plan like a World Cup. 00:23:54.625 --> 00:23:55.660 Jethro: Yeah, I like that. 00:23:55.960 --> 00:23:59.110 Uh, So you mentioned come back to this idea of how to restraint. 00:23:59.110 --> 00:24:00.550 So talk, talk about that. 00:24:01.527 --> 00:24:08.847 Michael Angelone: So I think that if a student doesn't know how to restrain with ai, that's a, that, I think that's a measurable thing. 00:24:09.392 --> 00:24:22.172 Um, and I think that it, it could be written up into a rubric, and I really believe that actually you could actually norm people who know what that means and what's, how that's measurable in a certain large language model context. 00:24:22.172 --> 00:24:24.722 So I'll give you a sort of cash on demand example. 00:24:25.112 --> 00:24:32.042 Um, let's say I have a classroom that day and it's a AI workshop and students are iterating with AI and trying to come to, um. 00:24:32.422 --> 00:24:38.662 Let's say a brainstorming session with AI to come up with a thesis statement for something that they want to explore, right? 00:24:38.842 --> 00:24:47.092 Maybe they're gonna write an in-class essay, you know, at the end of this so they can perform, as you had mentioned earlier, the knowledge, because we still need those outcomes, right? 00:24:47.092 --> 00:24:50.452 So I'll let them use AI in class to. 00:24:50.567 --> 00:24:58.937 Prepare for the in-class essay just as you or I could have used the writing center or our teacher's office hours to help us prepare for that in-class essay during finals week. 00:24:58.937 --> 00:25:06.797 Same kind of, kind of external, um, mechanisms, but this one's on demand 24 7 and gives you basically everything you want. 00:25:06.797 --> 00:25:09.407 So about that bias mitigation is huge to me. 00:25:09.527 --> 00:25:10.382 If I don't see a student. 00:25:11.702 --> 00:25:16.862 Was able to go, oh yeah, but what about, but wait, did you consider this? 00:25:17.132 --> 00:25:21.422 What would a person, uh, who disagreed with me say? 00:25:21.422 --> 00:25:29.492 So this concept of entertaining and entertaining a naysayer rhetorically I in the rhetorical situation is huge for us. 00:25:29.492 --> 00:25:31.022 Whether you're using AI or. 00:25:31.577 --> 00:25:37.397 So if you're writing a paper, none, uh uh, and you're not including or an objection or an opposing viewpoint, right? 00:25:37.397 --> 00:25:45.347 One thing that guys like me in our lane we've been doing for years is teaching this sort of dominion, Rogerian process. 00:25:45.377 --> 00:25:45.557 Yeah. 00:25:45.587 --> 00:25:47.897 Uh, rhetorical sort of approach. 00:25:48.167 --> 00:25:51.137 Um, uh, ethos, pathos, Lagos, right? 00:25:51.137 --> 00:25:54.677 And so audience awareness context is everything. 00:25:54.857 --> 00:25:57.617 And so you can actually see rhetorical ability. 00:25:58.937 --> 00:26:00.647 In the actual inputs. 00:26:01.007 --> 00:26:10.667 And so it's like you compare one student who has the ability for bias mitigation, their follow ups to AI inputs are interesting to see. 00:26:10.817 --> 00:26:20.627 And I measure 'em, I look at them and I collect that data and go, um, I could run this through my own agent and it could give me sort of a, is this person in advanced? 00:26:20.867 --> 00:26:21.017 Um. 00:26:21.977 --> 00:26:23.147 Now people go, what's the a? 00:26:23.207 --> 00:26:24.017 What's the thing? 00:26:24.107 --> 00:26:25.397 Is it a prompt engineer? 00:26:25.847 --> 00:26:26.957 I go, no. 00:26:27.107 --> 00:26:41.327 Um, they are able to go through the four phase AI regulation, scaffolding process, which I sort of had to come up with a stupid name so I could convince people that it's not just prompt engineering. 00:26:41.477 --> 00:26:43.367 It's like, are they asking good questions? 00:26:43.637 --> 00:26:43.997 No. 00:26:44.477 --> 00:26:44.717 Yeah. 00:26:44.717 --> 00:26:45.887 You know, is that measurable? 00:26:46.162 --> 00:26:46.672 Yes. 00:26:46.762 --> 00:26:47.512 Simple as that. 00:26:47.512 --> 00:27:01.372 Can I norm Jethro in a two hour session and say, read these iterations and you tell me, is this student asking ex, you know, do, can we measure their executive functions when it comes to these particular areas? 00:27:01.372 --> 00:27:03.962 Specific specifically with judgment and, um. 00:27:04.937 --> 00:27:07.757 I think that student's able to judge the why they're using it. 00:27:08.237 --> 00:27:18.677 Um, and I try to balance it off with more metacognitive writing, where they're like, just bring your authentic voice and tell me what you learned from the process of iterating with some large language model. 00:27:19.610 --> 00:27:19.960 Jethro: Yeah. 00:27:20.020 --> 00:27:25.030 Well, and that's one of those skills that is to me, vitally important. 00:27:25.030 --> 00:27:27.160 Do you take whatever it gives you? 00:27:27.850 --> 00:27:29.860 Or do you iterate on it? 00:27:30.160 --> 00:27:37.660 Or do you say, I have no idea what's going on here, and I'm just gonna like move on to the next thing. 00:27:38.110 --> 00:27:48.010 And that, that range does exist and it is very real and people who take whatever it says at face value is, is one thing. 00:27:48.010 --> 00:27:51.520 And in certain domains and contexts that makes sense. 00:27:51.520 --> 00:27:56.620 For example, I've had it create several apps for me and I don't know. 00:27:56.965 --> 00:28:06.415 If it's doing it, if it's writing the best code ever, because I'm not a developer, there's no way for me to know other than does the thing work. 00:28:06.445 --> 00:28:12.055 And if it does, then honestly that's good enough for me and that's all that I need it for. 00:28:12.505 --> 00:28:15.865 But through that process, I have also learned when to say. 00:28:16.780 --> 00:28:23.500 Uh, that doesn't work how I need it to, or that doesn't sound right based on what I do know. 00:28:23.770 --> 00:28:28.540 Explain it to me better and help me understand why you're making this design decision. 00:28:28.870 --> 00:28:40.090 You know, all those kinds of things that can have at one shot, something for you, but almost always it Needs multiple layers and refinement and, and things like that. 00:28:40.247 --> 00:28:40.597 Michael Angelone: Needs 00:28:40.750 --> 00:28:41.050 Jethro: there? 00:28:41.102 --> 00:28:41.432 Michael Angelone: judgment. 00:28:41.912 --> 00:28:43.982 And it's not to cut you off an needs human judgment. 00:28:44.290 --> 00:28:44.710 Jethro: Yeah. 00:28:44.715 --> 00:28:44.875 Mm-hmm. 00:28:45.092 --> 00:28:47.552 Michael Angelone: And I think it could be taught is my, is my thing. 00:28:47.552 --> 00:28:52.832 And I know other people are saying it, but I think it can be taught, I think it could be a place where it's taught. 00:28:53.462 --> 00:29:00.092 Um, and it's, and it's not just you're teaching, I think you're teaching them to, to drive a new car. 00:29:00.617 --> 00:29:06.707 In a weird way, like a new, kind of, a new kind of vehicle, if you will. 00:29:06.857 --> 00:29:07.547 You know what I mean? 00:29:07.547 --> 00:29:12.437 That that does take, um, I like analogy, so I'll use this one. 00:29:12.737 --> 00:29:20.957 Like, I think that, like for me personally, if you're gonna say, Mike, choose a car right now, and you put a 1984 Porsche nine 11. 00:29:21.392 --> 00:29:47.312 Out there, manual transmission or a Tesla model, whatever autonomous, ludicrous mode I'm taking the Porsche and the reason why is 'cause I maybe call me old school, but I love an open road, a little German car that hugs it doing, you know, with a manual transmission feeling me and the car working together, getting to where I want to go, and having kind of, and feeling, and it's an aesthetic experience, right? 00:29:47.582 --> 00:29:48.392 A Tesla. 00:29:48.647 --> 00:29:52.217 For me is the exact opposite, especially on autonomous driving mode. 00:29:52.247 --> 00:29:53.927 It's, it's getting me to where I need to be. 00:29:53.927 --> 00:29:58.067 It's achieving the same goal, but it's, I, I could fall asleep if I want to. 00:29:58.397 --> 00:30:05.297 And um, more importantly, and it's an it that sleeping and an anesthetic, so it's an anesthetic driving experience. 00:30:05.507 --> 00:30:08.177 I want my classroom to be an AI Porsche experience. 00:30:09.587 --> 00:30:14.597 I want them to recognize that they're driving something and the driver is the most important thing. 00:30:15.887 --> 00:30:20.327 And I do not want idiots driving a Porsche 'cause it's a very expensive car. 00:30:20.987 --> 00:30:21.407 Right? 00:30:21.587 --> 00:30:30.797 And I don't trust that just because you graduated high school or you're returning to school and maybe you have a few trips around the sun and it's your chance to go back to school. 00:30:30.827 --> 00:30:33.197 'cause community college sees all different kinds of students. 00:30:33.527 --> 00:30:38.807 I'm still not convinced that even with those trips around the sun that you are 100%. 00:30:38.807 --> 00:30:42.832 This might sound deficit minded, but I'm just one of the few people that will keep it real, especially with AI Now. 00:30:43.682 --> 00:30:45.902 Because especially with AI now, I'm not afraid to say it. 00:30:46.292 --> 00:30:49.247 I don't trust you behind the wheel of my Porsche. 00:30:49.967 --> 00:30:53.177 Like I'm, I, I, I, we're gonna start small. 00:30:53.267 --> 00:30:54.497 We're gonna go on the track. 00:30:54.767 --> 00:30:57.227 We're gonna go around in first gear, second gear, third gear. 00:30:57.317 --> 00:31:00.467 I'll let you open up the hammer after about two hours of driving this thing. 00:31:00.467 --> 00:31:02.687 And you don't, 'cause I don't want you to fishtail this thing. 00:31:02.692 --> 00:31:04.187 It, it, it's a funny kind of car. 00:31:04.187 --> 00:31:10.757 And I'm gonna that stop with my analogy because I think it works because I think that if teachers are ignoring it. 00:31:11.492 --> 00:31:18.632 We're gonna just have people driving their, their dad's Porsche around and crashing them, you know, and metal surrenders when oak trees meet fenders. 00:31:18.775 --> 00:31:19.225 Jethro: Yep. 00:31:19.412 --> 00:31:36.602 Michael Angelone: uh, and, and I, I, I'm, I think that's, that's what the danger in AI is, is we have to treat it like a license to drive, not, not, not police its usage, but in a weird way, like mothers against Drunk Driving, do pro bono work just for schools and go around and do, uh, to, to, to, you know. 00:31:37.232 --> 00:31:42.182 To make announcements about the dangers of drunk driving or whatever, of driving, texting and driving, everything included. 00:31:42.212 --> 00:31:46.472 I've been talking to you long, Jeff, but I'm so excited about it because I'm just saying that's the shift. 00:31:46.682 --> 00:31:55.742 That's the shift that has to happen for me first epistemologically and then teachers buy-in is next because students are already bought in. 00:31:56.102 --> 00:31:57.152 That's, I'm gonna see straight. 00:31:57.760 --> 00:31:58.120 Jethro: Yeah. 00:31:58.150 --> 00:32:04.660 Well, taking back to the analogy, uh, if somebody, you know, if, if a kid's like, Hey, can I drive that Porsche? 00:32:04.660 --> 00:32:09.280 Every kid's gonna wanna say yes, but they don't understand how to do it and how to do it well. 00:32:09.370 --> 00:32:11.980 And but given the opportunity, yes. 00:32:11.980 --> 00:32:13.270 I'm gonna say I want to drive it. 00:32:13.270 --> 00:32:14.350 And that's, and that's. 00:32:15.100 --> 00:32:16.300 That's exactly where we're at. 00:32:16.300 --> 00:32:17.710 I really like this analogy. 00:32:18.070 --> 00:32:25.480 Uh, we are out of time for today, but this was a, a great conversation and we'll probably need to have this again and talk more about it. 00:32:25.480 --> 00:32:31.390 But my big takeaway is that AI Porsche experience is, is a powerful way to look at it. 00:32:31.740 --> 00:32:34.140 And, and that is a good way to do it. 00:32:34.140 --> 00:32:36.570 That's why we need some handholding. 00:32:36.600 --> 00:32:37.860 We need some structure. 00:32:37.860 --> 00:32:41.910 We need some frameworks to help people learn how to use ai. 00:32:42.240 --> 00:32:52.650 And, you know, people who, who can, uh, can just go learn how to do it and use their own wisdom and judgment to be cautious and smart about it. 00:32:53.260 --> 00:32:57.730 Um, and if they can't, then they, they should definitely have some help. 00:32:57.730 --> 00:33:02.350 So, Michael, if people wanna connect with you more, what's a great way for them to get in touch with you? 00:33:02.932 --> 00:33:04.942 Michael Angelone: I could look me up on LinkedIn. 00:33:05.032 --> 00:33:08.512 Um, uh, search my name, Michael Angeloni. 00:33:08.512 --> 00:33:11.302 It's uh, A-N-G-E-L-O-N-E. 00:33:11.362 --> 00:33:18.022 I'm also, um, a professor at American River College, so you can find me on my bio@americanrivercollege.edu. 00:33:19.030 --> 00:33:25.540 Jethro: I've got a link to your LinkedIn, uh, in the show notes@visionforlearning.com. 00:33:25.660 --> 00:33:27.640 Thank you again so much for being here. 00:33:27.640 --> 00:33:31.420 Michael, this was great chatting with you and I really appreciate your insight. 00:33:32.122 --> 00:33:33.237 Michael Angelone: Thank you so much for having me.