WEBVTT 00:00:00.000 --> 00:00:01.659 All right, so for our mission today, we're doing 00:00:01.659 --> 00:00:05.879 a really tight focus on one of the most dynamic 00:00:05.879 --> 00:00:09.279 areas of modern life, technology's role in education. 00:00:09.939 --> 00:00:12.589 We're drawing from a fascinating... stack of 00:00:12.589 --> 00:00:15.289 sources here. It's essentially a deep dive into 00:00:15.289 --> 00:00:18.329 an updated framework, something based on a 2015 00:00:18.329 --> 00:00:20.910 chapter, but now, you know, seen through the 00:00:20.910 --> 00:00:24.469 lens of late 2024, maybe even 2025 advances. 00:00:24.769 --> 00:00:26.789 And that new lens is critical. It is. And just 00:00:26.789 --> 00:00:28.670 to be clear, this isn't a conversation about, 00:00:28.670 --> 00:00:30.870 you know, whether schools should buy more tablets. 00:00:30.949 --> 00:00:33.369 It's about understanding how technology has fundamentally 00:00:33.369 --> 00:00:35.909 changed the cognitive pathways of learning. The 00:00:35.909 --> 00:00:38.149 how? Exactly. We need to pull out the foundational 00:00:38.149 --> 00:00:40.829 concepts. What is knowledge building? How critical 00:00:40.829 --> 00:00:43.570 is medical cognition. And I think most importantly, 00:00:43.950 --> 00:00:46.450 where does that uniquely human element fit in 00:00:46.450 --> 00:00:49.030 when machines are doing so much? Well, what's 00:00:49.030 --> 00:00:51.329 fascinating here is just the sheer velocity of 00:00:51.329 --> 00:00:54.229 the shift. I mean, for decades, edtech was mostly 00:00:54.229 --> 00:00:56.710 a substitute. A digital worksheet for a paper 00:00:56.710 --> 00:01:00.039 one. Exactly. Or a video lecture instead of being 00:01:00.039 --> 00:01:01.820 there in person. But if you look at the data 00:01:01.820 --> 00:01:06.140 now, technology has become this powerful lever 00:01:06.140 --> 00:01:09.480 for cognitive tasks. It makes new ways of learning 00:01:09.480 --> 00:01:11.799 possible that weren't just difficult before, 00:01:12.219 --> 00:01:14.260 they were physically and logistically impossible. 00:01:14.459 --> 00:01:17.180 OK. So that's the transformation we need to understand. 00:01:17.200 --> 00:01:19.400 That's it. Let's unpack that. The sources really 00:01:19.400 --> 00:01:22.219 hammer this point that tech has moved beyond 00:01:22.219 --> 00:01:24.700 just, you know, making information delivery efficient. 00:01:25.120 --> 00:01:28.420 It's now doing complex jobs that took huge amounts 00:01:28.420 --> 00:01:31.159 of human labor just a few years ago. That's the 00:01:31.159 --> 00:01:34.079 absolute key. Just consider the scale for a second. 00:01:34.239 --> 00:01:37.250 What can a networked classroom do now? Think 00:01:37.250 --> 00:01:39.230 about the barriers that have just been erased. 00:01:39.409 --> 00:01:41.549 You mean geographical barriers? Geographical, 00:01:41.689 --> 00:01:43.969 logistical, all of it. We can have a design team 00:01:43.969 --> 00:01:47.109 in Texas working in real -time sustained collaboration 00:01:47.109 --> 00:01:49.530 with a peer review group in Tokyo. I mean, that's 00:01:49.530 --> 00:01:51.689 global connectivity enabling authentic social 00:01:51.689 --> 00:01:53.890 learning. And beyond that? Well, the storage 00:01:53.890 --> 00:01:57.030 and retrieval capacity is. It's basically limitless 00:01:57.030 --> 00:01:59.290 now. You're not just looking at a few library 00:01:59.290 --> 00:02:02.049 books. You have instant access to specialized 00:02:02.049 --> 00:02:05.280 data aces, to primary source material. Instantly. 00:02:05.579 --> 00:02:07.620 Right. And the calculation and simulation power, 00:02:07.659 --> 00:02:09.099 that's something I think gets overlooked, but 00:02:09.099 --> 00:02:11.240 it fundamentally changes how we teach science 00:02:11.240 --> 00:02:14.120 and engineering. Oh, absolutely. Running sophisticated 00:02:14.120 --> 00:02:17.740 multivariable simulations, modeling complex climate 00:02:17.740 --> 00:02:21.020 systems, or processing massive data sets in biology. 00:02:21.520 --> 00:02:23.280 This stuff used to be for professional research 00:02:23.280 --> 00:02:25.900 labs only. And now students can do it. Now students 00:02:25.900 --> 00:02:29.479 can engage in this level of high fidelity inquiry 00:02:29.479 --> 00:02:32.300 on their own devices. The practical outcome is 00:02:32.300 --> 00:02:35.300 profound. Educators are freed up to design richer 00:02:35.300 --> 00:02:37.860 experiences, less constrained by the physical 00:02:37.860 --> 00:02:40.159 limits of time or resources. That makes perfect 00:02:40.159 --> 00:02:42.699 sense. So tech is handling the heavy lifting, 00:02:42.800 --> 00:02:44.960 the data crunching, but that immediately brings 00:02:44.960 --> 00:02:47.439 up the crucial question, right? If tech takes 00:02:47.439 --> 00:02:50.460 over the algorithmic work, what's left? What's 00:02:50.460 --> 00:02:52.879 the inherent limitation? What is that uniquely 00:02:52.879 --> 00:02:56.120 human gap we have to focus on in education? And 00:02:56.120 --> 00:02:59.300 if we only focus on automation and efficiency, 00:02:59.539 --> 00:03:02.810 we miss the human contribution entirely. The 00:03:02.810 --> 00:03:05.449 sources, they highlight two major areas where 00:03:05.449 --> 00:03:07.409 AI and computers really struggle to catch up. 00:03:07.770 --> 00:03:09.729 And these have to become the pillars of human 00:03:09.729 --> 00:03:12.289 education now. The first is something called 00:03:12.289 --> 00:03:15.629 tacit knowledge. So explicit knowledge is what 00:03:15.629 --> 00:03:18.530 you can write in a manual or code into an algorithm, 00:03:18.990 --> 00:03:21.250 right? The steps to solve a quadratic equation. 00:03:21.550 --> 00:03:24.590 Got it. Tacit knowledge is implicit. It's learned 00:03:24.590 --> 00:03:28.330 through experience, intuition, context. It's 00:03:28.330 --> 00:03:30.550 the ability to know how to approach a problem 00:03:30.550 --> 00:03:33.009 you've never seen before or the subtle judgment 00:03:33.009 --> 00:03:35.610 you need when the data is incomplete or conflicting. 00:03:36.189 --> 00:03:38.250 OK, can you give us a really concrete example, 00:03:38.270 --> 00:03:40.129 something that shows that contrast? Sure, absolutely. 00:03:40.490 --> 00:03:43.650 Think about a complex medical diagnosis. An AI 00:03:43.650 --> 00:03:46.610 can read a billion medical records and flag the 00:03:46.610 --> 00:03:48.969 10 most statistically probable diseases based 00:03:48.969 --> 00:03:51.349 on the data. Right, that's the explicit algorithmic 00:03:51.349 --> 00:03:54.789 part. Pure efficiency. But the experienced ER 00:03:54.789 --> 00:03:58.169 doctor uses tacit knowledge. They notice the 00:03:58.169 --> 00:04:00.409 subtle shift in the patient's breathing, the 00:04:00.409 --> 00:04:03.050 way they hold their arm, or an inflection in 00:04:03.050 --> 00:04:05.449 the family's voice that signals something critical 00:04:05.449 --> 00:04:08.330 that wasn't even in the data set. So it's pattern 00:04:08.330 --> 00:04:11.310 recognition beyond the data points. It's framing 00:04:11.310 --> 00:04:14.150 the problem correctly, strategizing solutions, 00:04:14.370 --> 00:04:17.310 and predicting outcomes in a really ambiguous 00:04:17.310 --> 00:04:19.889 high -stakes situation. That's a powerful distinction. 00:04:20.110 --> 00:04:22.610 The machine knows the rules, but the human knows 00:04:22.610 --> 00:04:25.170 the context. Exactly. And the second limitation 00:04:25.170 --> 00:04:27.370 is wisdom. Computers, you know, they just lack 00:04:27.370 --> 00:04:29.990 the capacity for wisdom. The sources define it 00:04:29.990 --> 00:04:32.050 not just as making smart decisions, but as the 00:04:32.050 --> 00:04:36.269 ability to evaluate information ethically, aesthetically. 00:04:36.529 --> 00:04:38.930 It involves making these complex, holistic judgments 00:04:38.930 --> 00:04:42.149 that go way beyond logic and data. So if tech 00:04:42.149 --> 00:04:44.310 gets better and better at the complex calculations, 00:04:44.629 --> 00:04:46.790 the educational mission has to pivot. It has 00:04:46.790 --> 00:04:48.810 to be about cultivating that tacit, implicit 00:04:48.810 --> 00:04:51.310 knowledge and, critically, the human judgment 00:04:51.310 --> 00:04:54.149 to navigate it all. Precisely. We have to teach 00:04:54.149 --> 00:04:56.250 the student how to be the expert who knows which 00:04:56.250 --> 00:04:58.389 calculation to run and, more importantly, why 00:04:58.389 --> 00:05:02.509 it matters. When the data gives you three valid 00:05:02.509 --> 00:05:05.850 options. But only one is culturally or ethically 00:05:05.850 --> 00:05:08.389 the right one for the situation. That's wisdom. 00:05:08.620 --> 00:05:10.800 OK, so now that we've defined that indispensable 00:05:10.800 --> 00:05:14.220 human element, let's pivot. What does high quality 00:05:14.220 --> 00:05:16.680 tech -supported learning actually look like? 00:05:17.279 --> 00:05:19.199 The sources keep coming back to John Dewey's 00:05:19.199 --> 00:05:22.779 idea of Educative Experiences. Why is a 100 -year 00:05:22.779 --> 00:05:25.699 -old framework still so dominant? Because Dewey 00:05:25.699 --> 00:05:28.220 was just fundamentally right about engagement 00:05:28.220 --> 00:05:31.060 and utility. He laid out these four characteristics 00:05:31.060 --> 00:05:34.389 that separate just, you know... doing an activity 00:05:34.389 --> 00:05:37.649 from having a truly educative experience. First, 00:05:37.750 --> 00:05:39.649 it has to build on what the learner already knows. 00:05:40.310 --> 00:05:42.230 Second, it has to serve as a solid foundation 00:05:42.230 --> 00:05:44.709 for future learning. Third, it needs to have 00:05:44.709 --> 00:05:47.709 genuine social or cultural value. And that fourth 00:05:47.709 --> 00:05:49.589 one, that's the one that really seems to click 00:05:49.589 --> 00:05:51.790 with modern motivation theory, right? Absolutely. 00:05:51.930 --> 00:05:53.629 It must be immediately relevant to the learner. 00:05:53.730 --> 00:05:55.329 If the student can't see the point, if they don't 00:05:55.329 --> 00:05:58.370 see the context, the learning is temporary. They 00:05:58.370 --> 00:06:01.230 might pass the test, but it won't be retained 00:06:01.230 --> 00:06:04.310 or applied. It doesn't stick. It doesn't stick. 00:06:04.670 --> 00:06:08.269 Dewey's framework forces us to move past just 00:06:08.269 --> 00:06:10.889 passive reception and toward purposeful engagement. 00:06:11.209 --> 00:06:14.189 Which brings us to this core tension in education 00:06:14.189 --> 00:06:17.230 that the sources talk about. How is this kind 00:06:17.230 --> 00:06:19.490 of deeper learning different from what most of 00:06:19.490 --> 00:06:21.790 us probably experience, that sort of traditional 00:06:21.790 --> 00:06:24.470 passive instruction? Yeah, the contrast is between 00:06:24.470 --> 00:06:26.730 what they call instructionism and knowledge building. 00:06:27.439 --> 00:06:29.560 Instructionism is the old model. The teacher 00:06:29.560 --> 00:06:31.860 is the authority, knowledge is fixed, and the 00:06:31.860 --> 00:06:33.959 goal is memorization to match that authority. 00:06:34.279 --> 00:06:36.699 It's a consumption model. Right. Knowledge building 00:06:36.699 --> 00:06:38.740 is fundamentally different. It's a creation model. 00:06:39.160 --> 00:06:41.680 It's about developing deep understanding by encouraging 00:06:41.680 --> 00:06:43.959 learners to apply their emerging wisdom, their 00:06:43.959 --> 00:06:46.740 judgment, to articulate generalizations, and 00:06:46.740 --> 00:06:49.540 crucially, to create new understanding with others. 00:06:50.040 --> 00:06:52.759 So knowledge is seen as a work in progress. Always 00:06:52.759 --> 00:06:55.449 a work in progress. constantly subject to improvement 00:06:55.449 --> 00:06:59.029 and, importantly, verified by evidence. So, okay, 00:06:59.069 --> 00:07:01.269 let's make that concrete. In an instructionist's 00:07:01.269 --> 00:07:03.129 classroom studying a local park, they memorize 00:07:03.129 --> 00:07:05.490 dates, names of founders, that kind of thing. 00:07:05.850 --> 00:07:07.670 What happens in a knowledge -building environment 00:07:07.670 --> 00:07:09.829 with the same park? Well, they wouldn't just 00:07:09.829 --> 00:07:12.310 learn the history. They'd immediately treat that 00:07:12.310 --> 00:07:14.750 history as an incomplete document. They might 00:07:14.750 --> 00:07:17.350 research the original land use, interview longtime 00:07:17.350 --> 00:07:20.610 residents, propose an addition to the park based 00:07:20.610 --> 00:07:23.110 on current community needs, and then justify 00:07:23.110 --> 00:07:25.689 that proposal with evidence. So the knowledge 00:07:25.689 --> 00:07:29.069 they create is their own? It's theirs. And it's 00:07:29.069 --> 00:07:31.670 constantly being revised by peer feedback and 00:07:31.670 --> 00:07:34.009 new data. And technology is essential there. 00:07:34.089 --> 00:07:36.850 It allows for easy collaboration, rapid revision, 00:07:37.209 --> 00:07:39.250 and access to all the diverse sources they need 00:07:39.250 --> 00:07:41.600 for verification. And that immediately addresses 00:07:41.600 --> 00:07:43.579 that relevance issue we just talked about. This 00:07:43.579 --> 00:07:45.240 is where naturalistic learning comes in, getting 00:07:45.240 --> 00:07:47.819 us away from those sterile abstract classroom 00:07:47.819 --> 00:07:50.560 exercises. Exactly. Naturalistic learning just 00:07:50.560 --> 00:07:52.660 insists that academic skills have to be applied 00:07:52.660 --> 00:07:56.100 to complex real -world problems. We move beyond 00:07:56.100 --> 00:07:58.620 the word problem about two trains leaving a station. 00:07:58.920 --> 00:08:00.949 Right. And we engage students in problems that 00:08:00.949 --> 00:08:03.329 mirror professional life, which are always messy, 00:08:03.649 --> 00:08:05.889 incomplete, and multifaceted. And the importance 00:08:05.889 --> 00:08:07.970 here isn't just that it's more interesting, it's 00:08:07.970 --> 00:08:10.569 that it's better preparation. When you tackle 00:08:10.569 --> 00:08:13.250 a real -world problem, you can't just use math 00:08:13.250 --> 00:08:16.810 or just use history. You have to synthesize everything. 00:08:17.329 --> 00:08:20.089 Precisely. Which requires that tacit knowledge 00:08:20.089 --> 00:08:22.980 we discussed. If students are asked to redesign 00:08:22.980 --> 00:08:25.300 a piece of local infrastructure, they're using 00:08:25.300 --> 00:08:28.540 geometry, government codes, material science, 00:08:28.720 --> 00:08:31.800 community polling, it provides context, relevance, 00:08:32.220 --> 00:08:34.860 and huge motivation. It sounds a lot more engaging. 00:08:35.059 --> 00:08:37.720 It is. And the degree of naturalism can vary, 00:08:37.720 --> 00:08:40.500 of course, but the core idea is preparing students 00:08:40.500 --> 00:08:42.299 for the messy challenges they're actually going 00:08:42.299 --> 00:08:44.200 to face. Okay, so we've talked about the right 00:08:44.200 --> 00:08:46.620 kind of learning experiences, the goal of knowledge 00:08:46.620 --> 00:08:49.200 creation, but all of this depends on the student's 00:08:49.200 --> 00:08:51.740 internal drive. What's the mechanism that makes 00:08:51.740 --> 00:08:54.399 a student better at being a student? The engine 00:08:54.399 --> 00:08:57.220 of self -improvement. That engine is metacognition. 00:08:57.340 --> 00:08:59.600 It's usually defined as thinking about thinking. 00:09:00.080 --> 00:09:02.700 And it's not a soft skill. It is a structural 00:09:02.700 --> 00:09:05.059 requirement for learning, especially in a world 00:09:05.059 --> 00:09:07.379 where information changes so fast. Well, what 00:09:07.379 --> 00:09:09.879 does that look like in practice? Metacognition 00:09:09.879 --> 00:09:12.379 means the learner has to become deeply aware 00:09:12.379 --> 00:09:14.980 of their own process. They have to ask themselves, 00:09:15.360 --> 00:09:18.039 do I know enough to solve this? What's the best 00:09:18.039 --> 00:09:20.850 strategy for me to study this topic? Why did 00:09:20.850 --> 00:09:23.549 I fail that assessment? And what specific part 00:09:23.549 --> 00:09:26.350 of my process needs to change? It's that constant 00:09:26.350 --> 00:09:29.190 self -assessment and strategic adaptation. That's 00:09:29.190 --> 00:09:31.669 what drives continuous growth. And technology, 00:09:31.990 --> 00:09:34.490 which can sometimes be a distraction, can actually 00:09:34.490 --> 00:09:36.970 supercharge the self -awareness. It absolutely 00:09:36.970 --> 00:09:38.870 should. I mean, if a student is just learning 00:09:38.870 --> 00:09:41.509 passively, they have no data on their process. 00:09:41.950 --> 00:09:43.889 But if they're working in a collaborative online 00:09:43.889 --> 00:09:46.269 environment or a digital portfolio, they can 00:09:46.269 --> 00:09:48.980 see data on time spent. on successful pathways 00:09:48.980 --> 00:09:51.620 peers have chosen on their own knowledge gaps. 00:09:52.259 --> 00:09:54.179 So the tech gives them a dashboard for their 00:09:54.179 --> 00:09:56.519 own learning. Exactly. It provides the tools 00:09:56.519 --> 00:09:59.120 for immediate reflection, self -assessment, and 00:09:59.120 --> 00:10:00.899 personalized pathways. The student moves from 00:10:00.899 --> 00:10:03.120 being a passive recipient to an active monitor 00:10:03.120 --> 00:10:05.669 of their own strategy. Okay, so let's zoom out 00:10:05.669 --> 00:10:08.809 a bit and try to synthesize these concepts. We've 00:10:08.809 --> 00:10:12.389 got Dewey's framework, knowledge building, naturalism, 00:10:12.629 --> 00:10:15.669 metacognition. If a learning environment is really 00:10:15.669 --> 00:10:18.710 succeeding at this deeper, active, authentic 00:10:18.710 --> 00:10:21.309 model, what are the common traits we should be 00:10:21.309 --> 00:10:23.870 able to see? Well, there's a remarkably consistent 00:10:23.870 --> 00:10:26.429 convergence across all the successful models. 00:10:26.850 --> 00:10:29.330 They all share several non -negotiable characteristics. 00:10:29.629 --> 00:10:32.610 Like what? First, they're always based on real 00:10:32.610 --> 00:10:34.710 -world problems or at least realistic scenarios. 00:10:35.210 --> 00:10:37.750 That eliminates the abstract exercises. Second, 00:10:37.870 --> 00:10:40.529 they focus heavily on skill development and competency. 00:10:40.909 --> 00:10:43.179 Can the student actually do something? over just 00:10:43.179 --> 00:10:45.460 rote content coverage. And the frameworks ensure 00:10:45.460 --> 00:10:47.820 the challenge is right for deep thought. Yes. 00:10:48.220 --> 00:10:50.019 They provide multiple entry points to higher 00:10:50.019 --> 00:10:51.940 levels of cognitive engagement, you know, what 00:10:51.940 --> 00:10:54.779 we often call Bloom's taxonomy. So the activities 00:10:54.779 --> 00:10:57.220 consistently push students beyond simple recall 00:10:57.220 --> 00:11:00.539 and toward analysis, synthesis, creation. And 00:11:00.539 --> 00:11:02.960 collaboration has to be front and center. Absolutely. 00:11:03.539 --> 00:11:05.659 They have to leverage expertise from various 00:11:05.659 --> 00:11:08.909 sources. peers, mentors, online experts, the 00:11:08.909 --> 00:11:11.990 community, and actively encourage sustained social 00:11:11.990 --> 00:11:14.970 interaction and debate. And finally, and this 00:11:14.970 --> 00:11:17.669 is maybe the most important, they prioritize 00:11:17.669 --> 00:11:20.389 reflection and focus on creating products that 00:11:20.389 --> 00:11:23.570 matter. Meaning the output has value beyond a 00:11:23.570 --> 00:11:26.070 grade. Exactly. It holds value outside the classroom. 00:11:26.450 --> 00:11:28.409 Maybe it solves a local issue or it's research 00:11:28.409 --> 00:11:31.129 presented to a public audience. These elements 00:11:31.129 --> 00:11:33.990 make the learning memorable and fully transferable. 00:11:34.110 --> 00:11:36.629 That's a great theoretical blueprint. But to 00:11:36.629 --> 00:11:38.610 make this really tangible for everyone listening, 00:11:38.929 --> 00:11:41.629 we need some concrete examples. Where are these 00:11:41.629 --> 00:11:43.850 ideas actually being implemented successfully? 00:11:44.090 --> 00:11:46.809 Right. Moving past simple substitution to genuine 00:11:46.809 --> 00:11:49.250 transformation. Yes. We can point to several 00:11:49.250 --> 00:11:52.090 established models where tech is truly transformational. 00:11:52.570 --> 00:11:54.789 Let's maybe focus on two of the most effective. 00:11:54.830 --> 00:11:57.769 Perfect. First, project -based learning, or PBL. 00:11:57.980 --> 00:12:00.799 This model is just ideal for fostering that tacit 00:12:00.799 --> 00:12:03.080 knowledge and naturalistic learning. These are 00:12:03.080 --> 00:12:05.679 extended, in -depth projects with a lot of student 00:12:05.679 --> 00:12:08.139 choice, originality, and often a public component. 00:12:08.399 --> 00:12:10.460 In tech's role, there is. Tech isn't just for 00:12:10.460 --> 00:12:13.879 research. It's the tool for complex data analysis, 00:12:14.220 --> 00:12:17.139 for collaboration, for the sophisticated presentation 00:12:17.139 --> 00:12:20.320 of the findings. A student might use mobile sensors 00:12:20.320 --> 00:12:23.759 and public data APIs to track water quality in 00:12:23.759 --> 00:12:26.279 a local stream, then use simulation software 00:12:26.279 --> 00:12:29.340 to model different remediation strategies. That 00:12:29.340 --> 00:12:31.259 really does take the student from being a learner 00:12:31.259 --> 00:12:34.360 to being a genuine investigator. It does. And 00:12:34.360 --> 00:12:36.679 the second major model is the upside -down classroom, 00:12:36.820 --> 00:12:39.659 or flipped learning. This one's so powerful because 00:12:39.659 --> 00:12:42.039 it addresses the scarcity of the teacher's time. 00:12:42.580 --> 00:12:44.480 Students engage with the foundational content 00:12:44.480 --> 00:12:47.139 lecture videos, simulations, readings at home. 00:12:47.230 --> 00:12:49.990 Reversing the traditional model where just delivering 00:12:49.990 --> 00:12:52.490 information took up all the valuable face -to 00:12:52.490 --> 00:12:55.009 -face time. Precisely. The classroom time is 00:12:55.009 --> 00:12:57.370 then freed up for what really matters. Interactive 00:12:57.370 --> 00:13:00.370 activities, complex problem solving, deep discussion, 00:13:00.850 --> 00:13:02.950 the areas where metacognition and knowledge building 00:13:02.950 --> 00:13:05.200 thrive. The teacher is transformed from a lecturer 00:13:05.200 --> 00:13:07.879 into a facilitator, a content as for guiding 00:13:07.879 --> 00:13:11.360 those crucial, real -time, wisdom -based discussions. 00:13:11.779 --> 00:13:14.240 So technology enables the content delivery outside 00:13:14.240 --> 00:13:16.399 class, so the transformation can happen inside 00:13:16.399 --> 00:13:18.879 the class. That's it. The teacher can finally 00:13:18.879 --> 00:13:21.139 dedicate their attention to analyzing student 00:13:21.139 --> 00:13:23.789 understanding, not just repeating facts. That 00:13:23.789 --> 00:13:26.789 distinction freeing up human time for human judgment, 00:13:26.970 --> 00:13:29.809 that really feels like the core message of this 00:13:29.809 --> 00:13:32.289 deep dive. Tech and education isn't about doing 00:13:32.289 --> 00:13:34.789 old things faster anymore. Not at all. It's about 00:13:34.789 --> 00:13:37.409 doing new things by building systems that support 00:13:37.409 --> 00:13:40.669 deep understanding, relevance, and that essential 00:13:40.669 --> 00:13:43.629 skill of strategic self -awareness metacognition. 00:13:43.850 --> 00:13:46.429 It supports the algorithm so the human can focus 00:13:46.429 --> 00:13:48.970 on the ambiguity. And that leads us right back 00:13:48.970 --> 00:13:50.750 to the most critical takeaway for the sources. 00:13:50.809 --> 00:13:53.210 We now have these incredibly powerful tools, 00:13:53.309 --> 00:13:56.669 AI, big data, simulations for algorithmic tasks 00:13:56.669 --> 00:13:59.809 and storing information. And yet, the core human 00:13:59.809 --> 00:14:03.169 capabilities remain irreplaceable. Tacit knowledge 00:14:03.169 --> 00:14:05.870 for framing the problem and wisdom for ethical 00:14:05.870 --> 00:14:08.549 evaluation. And this raises a really important 00:14:08.549 --> 00:14:10.889 question for you, for everyone listening, and 00:14:10.889 --> 00:14:13.389 for educators everywhere. Given the efficiency 00:14:13.389 --> 00:14:16.289 of our new technological partners, how deliberately 00:14:16.289 --> 00:14:19.250 and explicitly must we design education to teach 00:14:19.250 --> 00:14:21.649 the wisdom and human judgment that technology 00:14:21.649 --> 00:14:24.240 simply cannot provide. Because if we don't, if 00:14:24.240 --> 00:14:26.740 we don't prioritize teaching students how to 00:14:26.740 --> 00:14:30.360 make ethical and relevant choices, we risk training 00:14:30.360 --> 00:14:33.279 a highly efficient generation that lacks the 00:14:33.279 --> 00:14:35.759 necessary judgment to steer its own capabilities. 00:14:36.340 --> 00:14:39.399 That's the challenge ahead. A truly profound 00:14:39.399 --> 00:14:41.399 framework for thinking about the future. Thank 00:14:41.399 --> 00:14:43.019 you for diving deep with us today. We'll catch 00:14:43.019 --> 00:14:43.460 you next time.