WEBVTT NOTE This file was generated by Descript 00:00:05.249 --> 00:00:05.959 Eric Makelky: Welcome. 00:00:05.969 --> 00:00:11.089 This is Erik McKelky, your host today from Creskenova Labs. 00:00:11.089 --> 00:00:13.589 I have joining me Christine Braun. 00:00:13.589 --> 00:00:15.359 She's got one of the coolest titles. 00:00:15.409 --> 00:00:16.309 I'm very envious. 00:00:16.309 --> 00:00:20.119 She is the chief creative officer at Creskenova. 00:00:20.879 --> 00:00:21.809 How are you, Christine? 00:00:22.749 --> 00:00:23.619 Christine: I'm doing well. 00:00:23.669 --> 00:00:24.429 Thank you so much. 00:00:24.429 --> 00:00:25.079 Thanks for having me 00:00:25.315 --> 00:00:25.635 Eric Makelky: Yeah. 00:00:25.655 --> 00:00:31.175 first and foremost, I want to hear a little bit about your background and how did you end up being the chief creative officer? 00:00:32.271 --> 00:00:32.721 Christine: Certainly. 00:00:33.051 --> 00:00:48.971 I had-- So I'm a furniture designer by training, a master's in furniture design, and when my children were little, I recognized that one of my sons was very creative and noticed also that the things that he was interested in were robotics and things like that. 00:00:49.181 --> 00:00:50.921 I couldn't find any classes for him. 00:00:50.921 --> 00:00:57.291 Started creating my own company to bridge a technology gap that I saw widening within the school systems. 00:00:57.871 --> 00:01:05.091 Did that for quite a few years, then went to the Nueva School, and the Nueva School is in the Silicon Valley. 00:01:05.091 --> 00:01:10.661 It's in well, it's in Hillsborough, the one I was at, and I worked at the innovation lab at the Nueva School. 00:01:11.091 --> 00:01:17.721 From Nueva, I went to Harvard University, where I became the director of the Makerspace at the School of Engineering and Applied Sciences. 00:01:18.101 --> 00:01:26.151 My role was to break down the silos of Harvard and to really welcome everybody into a community at the, at the Makerspace. 00:01:26.871 --> 00:01:34.341 And then while I was at the Makerspace, I taught a class for Crescando Global because I know Sarah and Katie from Nueva. 00:01:35.221 --> 00:01:50.721 And while I did that, I started talking to the founders, Barbara and Yen, and they had explained to me that they were hoping to create an in-person space, so I accept the pos- the position, and as soon as I started, we started looking for buildings in and around London to create Crescando Labs. 00:01:50.869 --> 00:01:51.239 Eric Makelky: All right. 00:01:51.289 --> 00:01:59.329 And, and you're finishing up your first year of having the brick-and-mortar Crescenova Labs program open 00:02:00.323 --> 00:02:13.633 Christine: Yeah, so we bought the building in 2024, came out of construction August 2025, offered our first workshops this past October, and now are full swing into courses, workshops, and camps. 00:02:13.963 --> 00:02:14.383 Yes. 00:02:14.721 --> 00:02:14.961 Eric Makelky: Right. 00:02:15.631 --> 00:02:40.881 Well, you know, for listeners out there that aren't in the London area or LABS wouldn't be a viable option at the moment, there's still a lot of great things happening in the LABS program, and I'm hoping we can start today talking a little bit about the framework that you use w- with all of your programs and how that can apply to families and students everywhere, even if they're not attending LABS courses. 00:02:42.343 --> 00:02:42.783 Christine: Excellent. 00:02:43.163 --> 00:02:55.123 So the Crescenova Labs framework was built up over my years of teaching as a owner of a company and at a school for gifted students, and then at Harvard University. 00:02:55.673 --> 00:03:02.413 What I found overall was that first and foremost, we need to teach students how to ask beautiful questions. 00:03:02.833 --> 00:03:09.353 And I believe that parents can utilize this information, especially when, you know, your kid gets in the car at the end of the day. 00:03:09.563 --> 00:03:13.763 Instead of asking something like, "Did you have a good day?" Like, that's a closed-ended question. 00:03:14.233 --> 00:03:14.973 End of conversation. 00:03:14.983 --> 00:03:17.113 Kid's either gonna say yes or no, and the conversation's dead. 00:03:17.693 --> 00:03:26.053 But if you look at like the schedule that your child has, you know about their math, you know about the subjects, the projects that they're making, right? 00:03:26.103 --> 00:03:28.013 Ask a more open-ended question. 00:03:28.213 --> 00:03:37.013 And by asking that open-ended question, you're really giving your child the opportunity to answer, to really dive into what happened that day. 00:03:37.143 --> 00:03:43.393 And of course, once you open that, that nut, so to speak, you know, the, the flow is gonna happen. 00:03:43.393 --> 00:03:45.183 They're gonna tell you everything that happened throughout their day. 00:03:45.333 --> 00:03:53.413 And the most exciting project, even if you say, "What was the most exciting thing that you learned today?" That in and of itself could be really critical for a child. 00:03:53.673 --> 00:04:02.343 I have three boys, and that was one of my questions to my, my boys, was, "What did you learn today that was really exciting?" And that's how you drive that curiosity 00:04:02.501 --> 00:04:02.811 Eric Makelky: Yeah. 00:04:02.841 --> 00:04:04.391 And that can take some time as a parent. 00:04:04.391 --> 00:04:14.731 If you've been asking them, you know, "How was school?" Or, "Was school good?" And then all of a sudden you say, "Tell me the most exciting thing that you learned about in math," your kid's gonna look at you like, "What's wrong, Mom?" 00:04:14.789 --> 00:04:15.469 Christine: Exactly. 00:04:15.599 --> 00:04:15.949 Yeah. 00:04:16.049 --> 00:04:16.579 Yeah, yeah, yeah. 00:04:16.599 --> 00:04:17.609 Maybe lead up to that one. 00:04:17.781 --> 00:04:18.951 Eric Makelky: it can take a little practice. 00:04:18.951 --> 00:04:29.191 And that's also a big contrast from, at least in the traditional education setting, you know, our approach has been get kids to answer questions. 00:04:29.191 --> 00:04:36.511 But really what you're talking about, the first step in the Crescenova Labs framework is get them to ask good questions. 00:04:37.407 --> 00:04:38.377 Christine: Exactly. 00:04:38.547 --> 00:04:42.607 when we offer a course at Crescanova Labs, right, it's a broad course. 00:04:42.617 --> 00:04:45.637 So we're working-- I can give you an example of microorganisms. 00:04:45.987 --> 00:05:03.187 And my whole first day of microorganisms, which is a two-hour studio class with students ages eight to 12 was to give them the microscopes, give them the moss, which I'd been soaking for a week that I've collected around London and teach them how to make a slide. 00:05:04.127 --> 00:05:13.477 And then from there, all I have to say is, you know, "What do you see, and what questions would you ask me?" And then the, this popcorn it. 00:05:13.777 --> 00:05:15.217 I don't like a quiet classroom. 00:05:15.217 --> 00:05:16.307 That's not how I educate. 00:05:16.347 --> 00:05:17.837 I like it to be conversational. 00:05:17.837 --> 00:05:18.967 I like it to be noisy. 00:05:19.507 --> 00:05:22.847 And pretty soon the students are like, "I found something. 00:05:22.847 --> 00:05:31.427 I don't know what it is." And then everybody runs and looks in it, right? And then they all start asking these questions and I say, "I'm not gonna answer these right now. 00:05:31.427 --> 00:05:32.827 Let's write them down in our notebooks. 00:05:33.077 --> 00:05:34.347 Let's record these questions. 00:05:34.347 --> 00:05:40.077 Draw what you see, record your questions." And that's a really great way to have a really interesting class. 00:05:40.587 --> 00:05:50.097 But for a parent, if I knew my child was in microorganisms, I might say, "What did you see today in that microscope? And what, what questions came up for you?" 00:05:51.149 --> 00:05:51.439 Eric Makelky: Yeah. 00:05:51.439 --> 00:05:53.759 Just getting that inquiry going, right? 00:05:53.789 --> 00:05:59.619 Making sure they're curious and they're hungry, and that's when you see kids light up and get excited about what they're learning. 00:05:59.649 --> 00:06:03.169 And then as a parent, you don't have to ask them what the most exciting thing was they learned. 00:06:03.199 --> 00:06:05.269 They will tell you as soon as they get in the car 00:06:05.921 --> 00:06:06.721 Christine: Exactly. 00:06:06.901 --> 00:06:10.881 And especially if, you know, like there's room for that, right? 00:06:10.881 --> 00:06:16.091 I mean, if you have three kids, you know, them all being like, "Oh my God, guess what I saw today? That's so exciting," you know? 00:06:16.321 --> 00:06:29.631 You have to maybe level set and say, "Okay, each one of you is gonna tell me what was so exciting." But when you feel that excitement from your student, then you know that s- that they're growing in a different way, right? 00:06:29.661 --> 00:06:34.951 Their growth mindset, their curiosity, and their creativity is growing along with them. 00:06:35.451 --> 00:06:49.881 I always like to talk about this invisible backpack idea, and if you can picture that every child has an invisible backpack, and every time we teach them something, they're filling that backpack with the knowledge that we're giving them. 00:06:50.431 --> 00:07:02.711 And for Crescando Labs, then what we want them to walk away with is the fact that they're-- they can drive their own curiosity, learn by doing, and growing confidence. 00:07:02.721 --> 00:07:04.311 That's really what we want them to do. 00:07:04.331 --> 00:07:08.541 So the framework is what they put in their invisible backpack. 00:07:08.541 --> 00:07:17.661 So if I'm teaching them how to ask an open-ended question and how to ask a close-ended question and what the difference is and why there's that difference, they're filling their backpack with that. 00:07:17.901 --> 00:07:25.231 So wherever they go in the future, they're still gonna be able to be like, "You know what? I think I need a little bit more information about that, and I'm not gonna be afraid to ask a question." 00:07:26.509 --> 00:07:26.789 Eric Makelky: Yeah. 00:07:26.839 --> 00:07:27.459 that's great. 00:07:27.499 --> 00:07:48.989 And you and I were talking before we started recording today a little bit about first of all, I love the invisible backpack analogy because we all have one and that's a great way to look at it as an educator is what am I putting in our students' backpacks that they can take with them and use especially after education when they're entering the workforce and becoming adults 00:07:50.109 --> 00:07:51.889 and productive members of society. 00:07:51.989 --> 00:07:56.109 But also let's talk a little bit about the AI role, 00:07:56.319 --> 00:08:13.189 uh, comes alongside, because I know there's a lot of parents out there that, you know, AI is the front of mind for a lot of people, and there's different views on AI's role, but especially when it comes to educating kids, there's just not a lot of resources or framework out there. 00:08:13.229 --> 00:08:17.729 But this is something that you've been working on and the labs program has implemented. 00:08:17.729 --> 00:08:20.349 So let's talk a little bit about the AI framework. 00:08:20.965 --> 00:08:21.955 Christine: Yes, 100%. 00:08:22.325 --> 00:08:32.665 I think that one of the things that we do at Chris Genova Labs is we look, you know, we're, we're constantly learning, and we're lifelong learners as we want our students to do, especially as we don't know what the jobs are gonna be in the future. 00:08:33.315 --> 00:08:41.775 And when I listen a lot to speakers about AI, and I learn a lot about AI, I love to learn about AI, and I utilize it often, just so you know. 00:08:41.825 --> 00:08:55.995 But what I realized in all of this is that there's not a process for educators to implement with students, and there's not a process for parents to truly understand what's happening with their student as it surrounds AI. 00:08:56.435 --> 00:09:04.235 And what that's going to do is just like we had the growing technology gap, and I believe we still do, we're-- we have this growing AI gap. 00:09:04.455 --> 00:09:09.115 And that's even more critical because there's gonna be the, those who have it and those who don't. 00:09:09.115 --> 00:09:15.495 And what we wanna do instead of the polarizing the whole entire AI process, we wanna just create a bridge. 00:09:15.725 --> 00:09:20.225 And I believe that, that this human to AI interaction does create a bridge. 00:09:20.845 --> 00:09:35.895 And to go back to the framework for just a minute, we do inquiry and then into systems thinking, and systems thinking is really important because we want the students to take on real world problems, but to look at every problem from a systems perspective, and that's the overall perspective. 00:09:35.895 --> 00:09:38.745 Like, if you're looking at the solar system, right? 00:09:38.755 --> 00:09:44.445 Or the environmental system we want them to understand that there's a community who upholds them. 00:09:44.685 --> 00:09:49.185 There-- It's not one student working to solve a problem without anybody else. 00:09:49.185 --> 00:09:50.105 There's a community. 00:09:50.105 --> 00:09:54.105 So that systems thinking, we create this community that upholds our students. 00:09:54.575 --> 00:09:59.105 Then we go into design thinking, and that's where AI interaction lives. 00:09:59.105 --> 00:10:01.115 It lives within the design thinking cycle. 00:10:01.415 --> 00:10:12.105 And the reason that it lives there is because once students go through inquiry into systems, and they understand how the systems works and they, they've identified the problem. 00:10:12.275 --> 00:10:14.885 Without systems thinking, they could be solving the wrong problem. 00:10:15.185 --> 00:10:20.855 Systems thinking definitely gives you an archetype, and then they can say, "Oh, this is the problem," and then they can solve for that problem. 00:10:21.655 --> 00:10:26.455 Then they go into the design thinking, which design thinking is really like, how are you gonna do it, you know? 00:10:26.505 --> 00:10:31.935 Now we know we've identified it and we've researched it because you've asked the questions, but how will you do it? 00:10:32.755 --> 00:10:39.755 That's where we get into, you know, divergent and convergent thinking, blue sky thinking brainstorming if you will. 00:10:40.755 --> 00:10:50.295 And then the AI comes in, and then the educator needs to say, "W- do I need to put AI into this project?" Sometimes yes and sometimes no. 00:10:50.475 --> 00:10:51.275 It's really up to you. 00:10:51.605 --> 00:10:56.375 Then we go, the AI interaction process starts with a question. 00:10:56.375 --> 00:10:57.235 I mean, why wouldn't it? 00:10:57.235 --> 00:10:59.145 It has to start with a question. 00:10:59.505 --> 00:11:07.273 And for the students, very simply it's, "What am I trying to understand?" The educator has said, "This is what we're gonna do. 00:11:07.283 --> 00:11:10.713 We're gonna use this AI tool." Actually, I don't wanna call it a tool. 00:11:10.723 --> 00:11:19.473 "We're gonna use AI." And we start to, through this process, tell students that AI is a tapestry. 00:11:19.993 --> 00:11:21.023 It's not a linear. 00:11:21.453 --> 00:11:34.273 When you ask a question to AI, be it a generative AI or text-based AI, and you're trying to get an answer back for you, you're not hearing from one, one specific aspect of the world. 00:11:34.273 --> 00:11:35.363 It's a tapestry. 00:11:35.713 --> 00:11:44.633 Just like there are multiple people who fed this machine, because it is machine learning they have given it all of this information. 00:11:44.973 --> 00:11:53.353 They-- So an AI doesn't specifically know what a human looks like because just because you put an image in, it's, it's deriving that. 00:11:53.563 --> 00:11:58.023 It has never-- It's gonna see the world as a representation of what it was given. 00:11:59.023 --> 00:12:00.693 As humans, we all have faults. 00:12:00.873 --> 00:12:03.603 We all do, and that's where our biases come in. 00:12:03.913 --> 00:12:09.403 So as the machine is learning, biases are being given because it's a human who's teaching it. 00:12:10.343 --> 00:12:22.873 Our students need to know that because when they get feedback from an AI, it's a tapestry, it's a community that has built the AI, so that's a community giving the feedback, right? 00:12:23.553 --> 00:12:24.263 So there's that. 00:12:24.313 --> 00:12:30.323 The student has to understand what the AI is, then it has to ask the question, "What do I need to get from this AI?" 00:12:30.373 --> 00:12:32.063 Then we go into frame. 00:12:32.783 --> 00:12:34.193 Framing is really important. 00:12:34.503 --> 00:12:43.183 Framing is how am I setting up my prompt so I get the information back that I need in order to move forward with my project? 00:12:43.943 --> 00:12:44.203 Great. 00:12:45.999 --> 00:12:50.979 In that prompt, you may get biases back. 00:12:51.139 --> 00:12:52.629 You have your bias, right? 00:12:52.639 --> 00:12:56.359 The person who's entering in this prompt has biases, right? 00:12:56.369 --> 00:13:01.739 The person who created the AI that they're using, right, the multiple people, that has a biases. 00:13:01.749 --> 00:13:07.319 So what we have to say to the students is, when you're setting this up, you need to look around the corner. 00:13:07.319 --> 00:13:09.849 We need to identify what these biases are. 00:13:09.889 --> 00:13:11.539 So framing is really important. 00:13:11.539 --> 00:13:13.639 And again, open and close-ended questions here. 00:13:13.659 --> 00:13:17.779 If you ask a close-ended question, you may not get the information that you need back. 00:13:18.219 --> 00:13:26.629 You ask an open-ended question, right, you're gonna get more information back, and how do you be specific without closing that question? 00:13:27.669 --> 00:13:30.109 Again, that's what this-- that's what the educator needs to work out. 00:13:30.459 --> 00:13:33.349 So now we have question, do I need this? 00:13:33.369 --> 00:13:34.569 And then what am I trying to get? 00:13:34.869 --> 00:13:39.049 Frame, how am I setting up this prompt so I'm getting back what I need? 00:13:39.769 --> 00:13:41.349 Then unpack. 00:13:41.679 --> 00:13:42.799 Unpack is critical. 00:13:43.399 --> 00:13:52.399 Unpack says you've gotten the information, whether it's an image that was generated for you, a research question that you asked and you got that information back. 00:13:52.779 --> 00:13:59.949 Unpack is looking at the information that you have, what's here and what's missing? 00:14:00.289 --> 00:14:06.599 Let's identify the problems and then go back and ask another question. 00:14:06.599 --> 00:14:07.769 This is a great place to iterate. 00:14:07.799 --> 00:14:09.359 Go back and ask another question. 00:14:09.749 --> 00:14:13.549 And then after unpack is own. 00:14:14.959 --> 00:14:25.899 We need to get the students to own the output, because once they take that and they add it to their project, they are saying, "I agree with this AI. 00:14:25.899 --> 00:14:38.669 I agree with the output that has come out." And I have to say, like even when I'm working with my team or if I'm working with my students, I highly dislike when somebody says to me, "Well, the AI said this, so I'm using it." No. 00:14:39.039 --> 00:14:41.439 That's-- Yes, the AI said that, right? 00:14:41.639 --> 00:14:42.889 But did you unpack it? 00:14:43.129 --> 00:14:45.189 Because now you have to own that. 00:14:45.499 --> 00:14:48.769 Now it's yours to use, and how you use it matters. 00:14:48.909 --> 00:14:50.569 Matters so much, right? 00:14:51.019 --> 00:14:55.959 So after they own it, then you can go back to iterate. 00:14:56.929 --> 00:14:58.619 What would I change next, right? 00:14:58.619 --> 00:15:00.259 I got all of this information. 00:15:00.349 --> 00:15:03.549 What else do I need to know to solve the problem? 00:15:03.879 --> 00:15:09.179 And then we can go back through the rest of the design thinking process and into driving towards excellence 00:15:09.509 --> 00:15:10.389 Eric Makelky: Yeah, I love that. 00:15:10.389 --> 00:15:12.319 That was a great deep dive, Christine. 00:15:12.379 --> 00:15:20.369 So in my experience, there's really two different mindsets with parents when it comes to AI, and you could probably extend this to educators. 00:15:20.369 --> 00:15:25.369 But I'm gonna give you one, and I want you to tell me what your pitch would be to this type of parent. 00:15:25.509 --> 00:15:37.179 If I'm the type of parent that is fearful of AI and I haven't exposed my kid to it, I haven't talked to them about it, I don't want them using it, I don't want it a part of their educational experience, what would you tell me? 00:15:38.827 --> 00:15:43.427 Christine: Well, I mean, first and foremost, it is part of their education experience. 00:15:43.487 --> 00:15:45.367 Schools are utilizing it, you know? 00:15:45.447 --> 00:15:48.977 When parents sign their waivers at the beginning of the year, AI's in there. 00:15:49.347 --> 00:15:50.327 I hate to say it, but it is. 00:15:50.737 --> 00:15:52.027 So first it's being used. 00:15:52.077 --> 00:15:53.437 You might wanna talk to your school, 00:15:54.383 --> 00:15:55.603 Eric Makelky: That's a good first step 00:15:56.707 --> 00:15:57.877 Christine: so that's first step, right? 00:15:58.147 --> 00:16:03.967 But the other thing is, back to that invisible backpack, parents can help, right? 00:16:03.987 --> 00:16:09.147 And when you learn this process, it gives you power. 00:16:09.347 --> 00:16:17.627 When you have power and understanding about AI, how AI is used and how it's used within your specific family, you're gonna relax. 00:16:18.057 --> 00:16:20.307 We all know that with knowledge comes power. 00:16:20.307 --> 00:16:21.777 I mean, that's, you know, everybody says that. 00:16:21.957 --> 00:16:23.387 but it's truly the fact here. 00:16:23.507 --> 00:16:27.437 If you understand how an AI is built, it takes the mystery out of it. 00:16:28.457 --> 00:16:34.887 And then once you understand how it's built and you understand how your student utilizes it, that's really a key. 00:16:35.247 --> 00:16:36.547 How are they using it? 00:16:36.807 --> 00:16:42.897 It's not going to, it's not out there to, like, change, you know, like, their perspective on life or anything like that. 00:16:43.137 --> 00:17:01.787 You really need to say, "You need to take this, unpack it, and own it." And if the parents are using the same vocabulary at home as the educators are using in the classroom, that's really filling that student's invisible backpack to have a better future, right? 00:17:01.807 --> 00:17:19.567 That growth mindset of saying, "I can come in contact with any AI, generative, anything, and I know how to use it. And I know how, even better, to ask a question using the same language that the system developed it to have," right? 00:17:19.617 --> 00:17:24.377 Prompt engineering constructing you know, like garbage in, garbage out, right? 00:17:24.377 --> 00:17:28.217 That's a really big one, and that comes really big into unpack. 00:17:28.967 --> 00:17:32.457 You know, if you put a bad question in, you're gonna get bad information out. 00:17:32.967 --> 00:17:34.057 So let's unpack that. 00:17:34.067 --> 00:17:42.537 Parents use that same language, it's, it's going to deescalate, and it's going to give parents the power to work with the students and the schools. 00:17:43.147 --> 00:17:43.537 Eric Makelky: Yeah. 00:17:43.667 --> 00:17:53.877 And I think a lot of parents just have a hard time wrapping their, their minds around that because this wasn't something they had to learn or deal with, especially as they were being educated. 00:17:53.967 --> 00:17:55.467 And it, it can be pretty scary. 00:17:55.467 --> 00:18:00.697 So it's a, it's a normal reaction for parents to be fearful and not wanna expose their kids to it. 00:18:01.087 --> 00:18:02.887 What about the opposite, Christine? 00:18:02.887 --> 00:18:21.377 What if I'm a parent who knows that AI is important, I know that my s- my student's gonna be using it in school, and eventually, you know, in the workforce it's gonna be a skill, and I've just been giving them the iPad saying, "Have at it, figure it out." You've talked a little bit about this, but what would your message be to that type of parent? 00:18:22.651 --> 00:18:32.561 Christine: So for that type of parent, I would say you really need to understand, like you n- you need to help your student frame the question that they're asking because the information is biased. 00:18:33.261 --> 00:18:51.341 And if you just hand your child an iPad without them understanding that this is an entire community that they're speaking to, and it's an entire community that has built this system with these biases in place, that they may be truly believing that what the AI is saying is truth, right? 00:18:51.391 --> 00:18:53.401 Concrete, absolute truth. 00:18:53.651 --> 00:18:56.461 They can just believe this like they're talking to their parent. 00:18:56.781 --> 00:18:59.021 That's not, that's not how this works. 00:18:59.181 --> 00:19:00.401 That's not what an AI is. 00:19:00.681 --> 00:19:11.871 An AI is, you know, like if you went into like a, a, a football stadium or like a concert, and you started just asking questions, and everybody's giving you the answer, then it all comes with these biases. 00:19:12.081 --> 00:19:19.661 And if you teach the child that, you know, don't trust this, then you're, you're really filling that backpack for that child. 00:19:19.661 --> 00:19:23.851 You're giving them the ability to think critically, right? 00:19:23.891 --> 00:19:39.841 We want students to think critically, and if you say to them, "You need to unpack this information that you got, and you need to own that information," then they're going to be really flexing that critical thinking muscle, and it's of the utmost importance 00:19:39.933 --> 00:19:40.353 Eric Makelky: Yeah. 00:19:40.433 --> 00:19:47.633 And I really love my favorite part that you've brought up so far is the ownership, because I hear that a lot with adults and students. 00:19:47.643 --> 00:19:53.433 It's, you know, "Well, the AI said," or, "The AI told me." And it's like, well, did you really analyze it? 00:19:53.453 --> 00:19:54.913 Did you really evaluate it? 00:19:55.353 --> 00:20:05.353 Whether it was supporting your hypothesis or not at the end of the day, it's a tool, but it shouldn't be blindly taken at face value every time you get an output from it. 00:20:06.317 --> 00:20:06.927 Christine: 100%. 00:20:07.387 --> 00:20:17.037 100%. There, there is this idea when you're unpacking where it, I talk about counter-framing, right? 00:20:17.487 --> 00:20:25.617 And counter-framing is looking at it creatively and critically that output that you're getting, right? 00:20:25.767 --> 00:20:28.197 Can I look at this from a completely different perspective? 00:20:28.787 --> 00:20:29.667 That's really interesting. 00:20:29.707 --> 00:20:31.207 That's an interesting question to ask. 00:20:31.517 --> 00:20:47.787 And if you do that, if you look at it from a completely different perspective, the output that you got, then when you own it, you have two perspectives, and then that student has to analyze it to come t- and say, "This is what I think based on these two very different perspectives." 00:20:47.895 --> 00:20:48.265 Eric Makelky: Yeah. 00:20:48.895 --> 00:20:54.035 That reminds me when I was a social studies teacher and we had to teach debate. 00:20:54.165 --> 00:21:03.505 I would let kids pick their topic and pick their side and do their research, and then two or three days before the debate, I would flip them to the opposite side. 00:21:03.575 --> 00:21:13.815 And that was kind of my goal is like, I know you're really pro this or anti that, but now I need you to make an intelligent argument based on the facts for the opposite side of, of what you think. 00:21:13.815 --> 00:21:17.765 And I, and I think that's a important skill to have, especially with AI. 00:21:18.695 --> 00:21:27.925 One of my favorite things to do is you know, if it's something that I've formulated myself, I'll put it into an AI model and say, "T- tell me what I'm missing. 00:21:27.965 --> 00:21:39.095 Tell me-- Shoot holes in, in, in my theory." Or if I take an output from one AI, put it in a different model and say, you know, "Evaluate this, analyze this, and, and tell me what I'm missing." 00:21:39.315 --> 00:21:40.545 And that's a fun skill 00:21:41.253 --> 00:21:43.123 Christine: Yeah, 100% I do that as well. 00:21:43.173 --> 00:21:44.003 I have three. 00:21:44.063 --> 00:21:45.523 I'll go, I'll go into three of them. 00:21:45.523 --> 00:21:49.453 I'll have Chat, Perplexity, Claude, you know, I can use all of them. 00:21:49.583 --> 00:21:52.283 And I have my favorites, and I have reasons why they're my favorites. 00:21:52.583 --> 00:21:56.773 And then I'll also say, "Show me the research." Right? 00:21:56.853 --> 00:22:05.243 Because we know adults know that AI hallucinates, and it's, you know, it's like your, your nicest people pleaser right? 00:22:05.403 --> 00:22:06.113 Is what an AI is. 00:22:06.113 --> 00:22:09.413 It's like, "I'm gonna tell you this because this is what you wanna hear." No, it's not what I wanna hear. 00:22:09.653 --> 00:22:11.353 I wanna understand the research. 00:22:11.383 --> 00:22:19.223 I wanna understand, again, the bias, and I wanna understand where you're getting this from so I can research it. 00:22:19.273 --> 00:22:20.283 I wanna know. 00:22:20.573 --> 00:22:25.373 And if you a- if I, I usually do two or three, just like you, I, I pit them against one another. 00:22:25.383 --> 00:22:26.073 What am I missing? 00:22:26.073 --> 00:22:27.333 Where did you get this information? 00:22:27.573 --> 00:22:29.603 Because then I need to own it. 00:22:30.353 --> 00:22:40.133 And to your point, if you, if you have like a debate team, and they're all like you have to critically think from the other side's perspective, that's exactly what we need to do as adults. 00:22:40.493 --> 00:22:46.703 And when we teach that to our students, imagine how amazing they are going to be when they become adults 00:22:46.869 --> 00:22:47.199 Eric Makelky: Yeah. 00:22:47.789 --> 00:22:48.329 that's great. 00:22:48.359 --> 00:22:56.069 So if listeners have more questions especially about your framework or the human AI interaction piece, w- where would you point them? 00:22:56.069 --> 00:22:58.439 What are some good resources to learn more, Christine? 00:23:00.217 --> 00:23:01.047 Christine: Okay, reach out to me. 00:23:01.107 --> 00:23:01.847 I'll tell you all about it. 00:23:01.847 --> 00:23:01.967 No 00:23:02.047 --> 00:23:06.197 Eric Makelky: You just had a great post on LinkedIn, so start by following Christine on LinkedIn 00:23:07.419 --> 00:23:07.659 Christine: Probably on LinkedIn. 00:23:07.759 --> 00:23:08.899 That's where you can find the article. 00:23:08.899 --> 00:23:10.799 You'll find the article on the website as well. 00:23:11.049 --> 00:23:21.739 And you know, message me, but I mean, if you're really interested, I can put-- I'll put a bunch of books of where, where I'm coming from onto the website as well, and that's inquiry. 00:23:21.869 --> 00:23:23.859 "The More Beautiful Question," amazing book, right? 00:23:23.859 --> 00:23:25.629 That really hones in on inquiry. 00:23:25.929 --> 00:23:29.939 Systems thinking, and there's so many great books on systems thinking. 00:23:29.939 --> 00:23:31.259 I'll put my favorites up there. 00:23:31.559 --> 00:23:36.329 And then design thinking, of course, there's a lot out there, design thinking. 00:23:36.619 --> 00:23:44.519 And then the human-AI interaction, why I chose these steps, and these are for educators and parents to teach to children, right? 00:23:44.539 --> 00:23:49.139 It's not like we're ha- gonna hand this and say, "Okay, child, go figure it out." That's not what this is. 00:23:49.139 --> 00:23:50.869 This is a teaching tool. 00:23:51.079 --> 00:23:52.499 And then drive towards excellence. 00:23:52.499 --> 00:23:54.619 There's, there's a book about driving towards excellence. 00:23:54.909 --> 00:24:04.819 And the reason that we drive towards excellence at Labs is because when you're doing the design thinking process, it's rarely taught when to get off the prototyping reel. 00:24:04.829 --> 00:24:07.099 Students will iterate forever if you let them, right? 00:24:07.109 --> 00:24:16.079 I mean, if you think about it, if they're driving their own curiosity in AI, they're owning all of this information, they're coming up with new questions, they could, they could go around that prototyping wheel forever. 00:24:16.369 --> 00:24:31.829 But we, we are saying, especially for like perfectionists, you know, we have those perfectionist students, we wanna tell them, "This is good enough for now. Drive it towards excellence. Make it as good as you can possibly make it, then we'll answer that question again." 00:24:32.185 --> 00:24:33.145 Eric Makelky: Yeah, I love that. 00:24:33.485 --> 00:24:39.785 Well, it's been a lot of fun jumping into the, the framework at, at the Labs program, and I'm excited for our next episode. 00:24:39.785 --> 00:24:44.855 We're gonna dive in a little deeper on experiential learning and flexible thinking, Christine. 00:24:45.585 --> 00:24:45.885 Yeah. 00:24:46.595 --> 00:24:46.915 Great. 00:24:46.915 --> 00:24:47.365 Well, thank you. 00:24:47.385 --> 00:24:51.555 thanks for joining us today and, and sharing more about the framework at Crescando Labs 00:24:52.429 --> 00:24:52.979 Christine: You're welcome. 00:24:52.979 --> 00:24:53.769 This has been wonderful. 00:24:53.779 --> 00:24:54.669 Thank you so much