All right, so for our mission today, we're doing
a really tight focus on one of the most dynamic
areas of modern life, technology's role in education.
We're drawing from a fascinating... stack of
sources here. It's essentially a deep dive into
an updated framework, something based on a 2015
chapter, but now, you know, seen through the
lens of late 2024, maybe even 2025 advances.
And that new lens is critical. It is. And just
to be clear, this isn't a conversation about,
you know, whether schools should buy more tablets.
It's about understanding how technology has fundamentally
changed the cognitive pathways of learning. The
how? Exactly. We need to pull out the foundational
concepts. What is knowledge building? How critical
is medical cognition. And I think most importantly,
where does that uniquely human element fit in
when machines are doing so much? Well, what's
fascinating here is just the sheer velocity of
the shift. I mean, for decades, edtech was mostly
a substitute. A digital worksheet for a paper
one. Exactly. Or a video lecture instead of being
there in person. But if you look at the data
now, technology has become this powerful lever
for cognitive tasks. It makes new ways of learning
possible that weren't just difficult before,
they were physically and logistically impossible.
OK. So that's the transformation we need to understand.
That's it. Let's unpack that. The sources really
hammer this point that tech has moved beyond
just, you know, making information delivery efficient.
It's now doing complex jobs that took huge amounts
of human labor just a few years ago. That's the
absolute key. Just consider the scale for a second.
What can a networked classroom do now? Think
about the barriers that have just been erased.
You mean geographical barriers? Geographical,
logistical, all of it. We can have a design team
in Texas working in real -time sustained collaboration
with a peer review group in Tokyo. I mean, that's
global connectivity enabling authentic social
learning. And beyond that? Well, the storage
and retrieval capacity is. It's basically limitless
now. You're not just looking at a few library
books. You have instant access to specialized
data aces, to primary source material. Instantly.
Right. And the calculation and simulation power,
that's something I think gets overlooked, but
it fundamentally changes how we teach science
and engineering. Oh, absolutely. Running sophisticated
multivariable simulations, modeling complex climate
systems, or processing massive data sets in biology.
This stuff used to be for professional research
labs only. And now students can do it. Now students
can engage in this level of high fidelity inquiry
on their own devices. The practical outcome is
profound. Educators are freed up to design richer
experiences, less constrained by the physical
limits of time or resources. That makes perfect
sense. So tech is handling the heavy lifting,
the data crunching, but that immediately brings
up the crucial question, right? If tech takes
over the algorithmic work, what's left? What's
the inherent limitation? What is that uniquely
human gap we have to focus on in education? And
if we only focus on automation and efficiency,
we miss the human contribution entirely. The
sources, they highlight two major areas where
AI and computers really struggle to catch up.
And these have to become the pillars of human
education now. The first is something called
tacit knowledge. So explicit knowledge is what
you can write in a manual or code into an algorithm,
right? The steps to solve a quadratic equation.
Got it. Tacit knowledge is implicit. It's learned
through experience, intuition, context. It's
the ability to know how to approach a problem
you've never seen before or the subtle judgment
you need when the data is incomplete or conflicting.
OK, can you give us a really concrete example,
something that shows that contrast? Sure, absolutely.
Think about a complex medical diagnosis. An AI
can read a billion medical records and flag the
10 most statistically probable diseases based
on the data. Right, that's the explicit algorithmic
part. Pure efficiency. But the experienced ER
doctor uses tacit knowledge. They notice the
subtle shift in the patient's breathing, the
way they hold their arm, or an inflection in
the family's voice that signals something critical
that wasn't even in the data set. So it's pattern
recognition beyond the data points. It's framing
the problem correctly, strategizing solutions,
and predicting outcomes in a really ambiguous
high -stakes situation. That's a powerful distinction.
The machine knows the rules, but the human knows
the context. Exactly. And the second limitation
is wisdom. Computers, you know, they just lack
the capacity for wisdom. The sources define it
not just as making smart decisions, but as the
ability to evaluate information ethically, aesthetically.
It involves making these complex, holistic judgments
that go way beyond logic and data. So if tech
gets better and better at the complex calculations,
the educational mission has to pivot. It has
to be about cultivating that tacit, implicit
knowledge and, critically, the human judgment
to navigate it all. Precisely. We have to teach
the student how to be the expert who knows which
calculation to run and, more importantly, why
it matters. When the data gives you three valid
options. But only one is culturally or ethically
the right one for the situation. That's wisdom.
OK, so now that we've defined that indispensable
human element, let's pivot. What does high quality
tech -supported learning actually look like?
The sources keep coming back to John Dewey's
idea of Educative Experiences. Why is a 100 -year
-old framework still so dominant? Because Dewey
was just fundamentally right about engagement
and utility. He laid out these four characteristics
that separate just, you know... doing an activity
from having a truly educative experience. First,
it has to build on what the learner already knows.
Second, it has to serve as a solid foundation
for future learning. Third, it needs to have
genuine social or cultural value. And that fourth
one, that's the one that really seems to click
with modern motivation theory, right? Absolutely.
It must be immediately relevant to the learner.
If the student can't see the point, if they don't
see the context, the learning is temporary. They
might pass the test, but it won't be retained
or applied. It doesn't stick. It doesn't stick.
Dewey's framework forces us to move past just
passive reception and toward purposeful engagement.
Which brings us to this core tension in education
that the sources talk about. How is this kind
of deeper learning different from what most of
us probably experience, that sort of traditional
passive instruction? Yeah, the contrast is between
what they call instructionism and knowledge building.
Instructionism is the old model. The teacher
is the authority, knowledge is fixed, and the
goal is memorization to match that authority.
It's a consumption model. Right. Knowledge building
is fundamentally different. It's a creation model.
It's about developing deep understanding by encouraging
learners to apply their emerging wisdom, their
judgment, to articulate generalizations, and
crucially, to create new understanding with others.
So knowledge is seen as a work in progress. Always
a work in progress. constantly subject to improvement
and, importantly, verified by evidence. So, okay,
let's make that concrete. In an instructionist's
classroom studying a local park, they memorize
dates, names of founders, that kind of thing.
What happens in a knowledge -building environment
with the same park? Well, they wouldn't just
learn the history. They'd immediately treat that
history as an incomplete document. They might
research the original land use, interview longtime
residents, propose an addition to the park based
on current community needs, and then justify
that proposal with evidence. So the knowledge
they create is their own? It's theirs. And it's
constantly being revised by peer feedback and
new data. And technology is essential there.
It allows for easy collaboration, rapid revision,
and access to all the diverse sources they need
for verification. And that immediately addresses
that relevance issue we just talked about. This
is where naturalistic learning comes in, getting
us away from those sterile abstract classroom
exercises. Exactly. Naturalistic learning just
insists that academic skills have to be applied
to complex real -world problems. We move beyond
the word problem about two trains leaving a station.
Right. And we engage students in problems that
mirror professional life, which are always messy,
incomplete, and multifaceted. And the importance
here isn't just that it's more interesting, it's
that it's better preparation. When you tackle
a real -world problem, you can't just use math
or just use history. You have to synthesize everything.
Precisely. Which requires that tacit knowledge
we discussed. If students are asked to redesign
a piece of local infrastructure, they're using
geometry, government codes, material science,
community polling, it provides context, relevance,
and huge motivation. It sounds a lot more engaging.
It is. And the degree of naturalism can vary,
of course, but the core idea is preparing students
for the messy challenges they're actually going
to face. Okay, so we've talked about the right
kind of learning experiences, the goal of knowledge
creation, but all of this depends on the student's
internal drive. What's the mechanism that makes
a student better at being a student? The engine
of self -improvement. That engine is metacognition.
It's usually defined as thinking about thinking.
And it's not a soft skill. It is a structural
requirement for learning, especially in a world
where information changes so fast. Well, what
does that look like in practice? Metacognition
means the learner has to become deeply aware
of their own process. They have to ask themselves,
do I know enough to solve this? What's the best
strategy for me to study this topic? Why did
I fail that assessment? And what specific part
of my process needs to change? It's that constant
self -assessment and strategic adaptation. That's
what drives continuous growth. And technology,
which can sometimes be a distraction, can actually
supercharge the self -awareness. It absolutely
should. I mean, if a student is just learning
passively, they have no data on their process.
But if they're working in a collaborative online
environment or a digital portfolio, they can
see data on time spent. on successful pathways
peers have chosen on their own knowledge gaps.
So the tech gives them a dashboard for their
own learning. Exactly. It provides the tools
for immediate reflection, self -assessment, and
personalized pathways. The student moves from
being a passive recipient to an active monitor
of their own strategy. Okay, so let's zoom out
a bit and try to synthesize these concepts. We've
got Dewey's framework, knowledge building, naturalism,
metacognition. If a learning environment is really
succeeding at this deeper, active, authentic
model, what are the common traits we should be
able to see? Well, there's a remarkably consistent
convergence across all the successful models.
They all share several non -negotiable characteristics.
Like what? First, they're always based on real
-world problems or at least realistic scenarios.
That eliminates the abstract exercises. Second,
they focus heavily on skill development and competency.
Can the student actually do something? over just
rote content coverage. And the frameworks ensure
the challenge is right for deep thought. Yes.
They provide multiple entry points to higher
levels of cognitive engagement, you know, what
we often call Bloom's taxonomy. So the activities
consistently push students beyond simple recall
and toward analysis, synthesis, creation. And
collaboration has to be front and center. Absolutely.
They have to leverage expertise from various
sources. peers, mentors, online experts, the
community, and actively encourage sustained social
interaction and debate. And finally, and this
is maybe the most important, they prioritize
reflection and focus on creating products that
matter. Meaning the output has value beyond a
grade. Exactly. It holds value outside the classroom.
Maybe it solves a local issue or it's research
presented to a public audience. These elements
make the learning memorable and fully transferable.
That's a great theoretical blueprint. But to
make this really tangible for everyone listening,
we need some concrete examples. Where are these
ideas actually being implemented successfully?
Right. Moving past simple substitution to genuine
transformation. Yes. We can point to several
established models where tech is truly transformational.
Let's maybe focus on two of the most effective.
Perfect. First, project -based learning, or PBL.
This model is just ideal for fostering that tacit
knowledge and naturalistic learning. These are
extended, in -depth projects with a lot of student
choice, originality, and often a public component.
In tech's role, there is. Tech isn't just for
research. It's the tool for complex data analysis,
for collaboration, for the sophisticated presentation
of the findings. A student might use mobile sensors
and public data APIs to track water quality in
a local stream, then use simulation software
to model different remediation strategies. That
really does take the student from being a learner
to being a genuine investigator. It does. And
the second major model is the upside -down classroom,
or flipped learning. This one's so powerful because
it addresses the scarcity of the teacher's time.
Students engage with the foundational content
lecture videos, simulations, readings at home.
Reversing the traditional model where just delivering
information took up all the valuable face -to
-face time. Precisely. The classroom time is
then freed up for what really matters. Interactive
activities, complex problem solving, deep discussion,
the areas where metacognition and knowledge building
thrive. The teacher is transformed from a lecturer
into a facilitator, a content as for guiding
those crucial, real -time, wisdom -based discussions.
So technology enables the content delivery outside
class, so the transformation can happen inside
the class. That's it. The teacher can finally
dedicate their attention to analyzing student
understanding, not just repeating facts. That
distinction freeing up human time for human judgment,
that really feels like the core message of this
deep dive. Tech and education isn't about doing
old things faster anymore. Not at all. It's about
doing new things by building systems that support
deep understanding, relevance, and that essential
skill of strategic self -awareness metacognition.
It supports the algorithm so the human can focus
on the ambiguity. And that leads us right back
to the most critical takeaway for the sources.
We now have these incredibly powerful tools,
AI, big data, simulations for algorithmic tasks
and storing information. And yet, the core human
capabilities remain irreplaceable. Tacit knowledge
for framing the problem and wisdom for ethical
evaluation. And this raises a really important
question for you, for everyone listening, and
for educators everywhere. Given the efficiency
of our new technological partners, how deliberately
and explicitly must we design education to teach
the wisdom and human judgment that technology
simply cannot provide. Because if we don't, if
we don't prioritize teaching students how to
make ethical and relevant choices, we risk training
a highly efficient generation that lacks the
necessary judgment to steer its own capabilities.
That's the challenge ahead. A truly profound
framework for thinking about the future. Thank
you for diving deep with us today. We'll catch
you next time.