Code of Curiosity: How AI Can Unlock Human Wonder
📑 Chapters
02:30 - Barbara introduces herself
03:28 - Barbara’s background
08:02 - The neurological process behind curiosity
12:35 - Different types of curiosity
17:18 - Activating curiosity in learning
19:59 - Harnessing curiosity to learn faster
23:36 - The future for Barbara
35:44 - Jiani’s recap of the episode
37:52 - Barbara’s passion growing up
38:44 - The role of childlike wonder in Barbara’s life
Watch the full episode here.
💕 Story Overview
Welcome to S4E8 of the @MAGICademy Podcast, where we give a warm welcome to Barbara Oakley. She is a professor and the master brain behind the course “Learning How to Learn”, available in Coursera, which has been taken by more than a million people making it one of the more successful open learning courses on the platform. We talked about the curiosity in all of us, and how it’s necessary for everyone who wishes to learn a new concept, language, or anything they need to know.
Barbara tells us how she developed the course after seeing the disconnection between what was being taught and what actual, modern students need to be taught. She found out that the most important part was to focus on the way of learning before diving into the subject per se, so we can use our curiosity to interest ourselves more in what we need to learn. Barbara also shares some insights on the neurological process behind learning and curiosity and gives some advice on how to use tools at hand to prepare us to learn something new.
🌼 Magical Insights
The Neurological Process Behind Curiosity: Curiosity is a complex cognitive phenomenon that significantly influences learning, memory, and overall brain function. The neurological processes underlying curiosity involve several key brain regions and neurotransmitters, particularly dopamine, which plays a crucial role in the brain's reward system. The activation of the dopaminergic circuit not only reinforces the desire to seek out new information but also improves memory encoding and retention for both the information sought and incidental details encountered during the exploration.
Different Types of Curiosity: We all have curiosity but, depending on the situation, our motivation is different. Sometimes it is a simple curiosity to write a paper or an article, for example. We just interest ourselves enough to carry on with the task, but if the subject is not appealing to us, that information will leave our brains as soon as we’re done with what we’re doing. The key is to motivate ourselves to learn and awaken our deep curiosity to involve ourselves in the subject and get a deeper understanding of what we’re learning.
Keeping Things New: To spark curiosity in people, they have to be attracted by something that looks appealing, interesting, and, most of all, new. Once our minds see something they don’t know we instantly crave to learn a little more about the subject. If that curiosity can be harnessed, talents will remain interested in the concept at hand and it will most likely stick to them
Using AI to Spark Curiosity: While it’s impossible to learn an entire subject through AI, it is possible to rely on it to spark curiosity in ourselves and to get ideas on how to spark it for others. Due to its narrative nature and its great ability to present a concept, AI can be used to motivate us to learn new things or help talents develop themselves in subjects they might not be as interested in, by presenting the concepts in an attractive way. Metaphors are a good example of this, and AI is great at creating those.
⭐ What’s Barbara’s Magic?
Barbara's unique magic lies in her ability to transform complex learning concepts into accessible strategies that resonate with a diverse audience. Her innovative approaches integrate insights from neuroscience and cognitive psychology, making learning more effective and enjoyable for the talents learning it.
Conclusion
In conclusion, Barbara emphasized the role of curiosity in effective learning. She highlighted how understanding the neurological mechanisms behind learning can empower individuals to approach new concepts—whether they be languages, subjects, or skills—with confidence and enthusiasm. By focusing on learning strategies before diving into content, we can cultivate a deeper interest and engagement with the material.
We hope this episode inspires you to explore your curiosity and apply Barbara’s strategies in your learning endeavors.
If you would like to stay tuned with our future guests and their magical stories. Welcome to join us.
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Barbara Oakley's Magic- The Power of Understanding
Finding Magic in Childhood- Barbara's Childhood Memories
Generative AI in relation to the Brain Pathways
Harnessing AI as Your Ultimate Learning Partner
Learning How to Learn as a Person with Dyslexia, Autism, or Neurodiversity
The Importance of Critical Thinking When Using AI
The Key to Faster Learning by Unlocking Curiosity
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Kenett, Y.N., Humphries, S., & Chatterjee, A. (2023). A Thirst for Knowledge: Grounding Curiosity, Creativity, and Aesthetics in Memory and Reward Neural Systems. Creativity Research Journal, 35, 412 - 426.
Murayama, K. (2022). A reward-learning framework of knowledge acquisition: An integrated account of curiosity, interest, and intrinsic-extrinsic rewards. Psychological review.
Louie, W., Chklovski, T., Jaris, M., & Torres, A. (2018). Curiosity Machine. Connected Science Learning.
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Dr. Barbara Oakley is a prominent American engineer, educator, and author, best known for her work in the fields of learning and education. She co-created the massively popular online course "Learning How to Learn," which has reached millions of students worldwide through platforms like Coursera. Oakley has authored several influential books, including A Mind for Numbers and Mindshift, which explore effective learning strategies and how to overcome obstacles to education.
Dr. Oakley's innovative approaches to teaching have made significant impacts across various educational levels, helping learners from diverse backgrounds enhance their understanding of complex subjects.
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Jiani (00:03)
Welcome to Magic Academy podcast. Today we have a special guest, Barbara. You may know her. You probably know her. She is the master brain and professor behind the world's most popular open-source learning courses from Coursera called, you probably know it, Learning How to Learn.
And this course is one of the most successful courses and one million, at least one million people took it and I'm one of them.
Barb Oakley (00:44)
you
Jiani (00:46)
And I took it many times and I just appreciate so much the wisdom Barbara brought to us and she's able to explain everything in such a simple, dynamic and engaging way and that just makes learning not as a chore but as like an activity and like an addictive activity for your brain and your heart. I was like, yes.
And she's also teaching engineering in Stanford. So it's like a great, great person for us to connect. So thank you, Barbara, to come to us today.
Barb Oakley (01:29)
thanks so much, Gianni. It was such a pleasure creating the massive open online course, Learning How to Learn. So Terry Sinowski, who is the Francis Crick Professor at the Salk Institute, and I created that course. And right now we're updating it with generative AI. So it will have even more.
exciting and fun things. think that's part of it is you think, I'm going to give you a neuroscientific foundation on how you can learn more effectively. And you're like, boring. But there's so much fun in the course. And we simplify and use metaphors so you can actually go very deep into learning. And it's just so fun.
Jiani (02:23)
Yes, I had fun. And I think people in our audience group, they adore what you're doing and they see you as a source of truth in terms of learning how to learn. And for me as well, it's like learning how to learn Barbara.
Barb Oakley (02:46)
Well, I'm an imperfect conveyor of ideas, but I do my tiny best.
Jiani (02:54)
I love that. And so let's get started. I have a first question waiting for you. Beep beep beep. In front of you went a spaceship. Out walks a little cute friendly alien. And if you were to introduce yourself to him, her, what word or movement?
that you would say or do to introduce yourself.
Barb Oakley (03:29)
gosh, I think I would cock my head a little to the side, kind of like you see puppies do when they're really curious about things. And I would just be in that way and my open body language conveying the fact that I am friendly and I would like to say hello. So, and then I would, especially if I knew it was already friendly.
If I wasn't so sure, I would probably be a little bit more opaque in my greeting.
Jiani (04:07)
before we understand their intentions. I love that. I love that. Beautiful. And why learning how to learn? Why coming from an engineering perspective, your curiosity kind of led you to the space of helping people to learn how to learn.
Barb Oakley (04:10)
Exactly.
think that, so right out of high school, I enlisted in the army and went to the Defense Language Institute and picked a language of random. I picked Russian, you know, as being sort of far away from English, but not so far like Chinese that I could study my entire lifetime and still be just kind of a novice with it. And I learned a language and I learned how
Jiani (04:54)
you
Barb Oakley (05:03)
to learn a language because that was at the Defense Language Institute, which is one of the preeminent institutions of language learning. And the thing is, when you're learning a language, there is always, I mean, you know whether or not you can speak that language because you're interacting with people who are speaking that language and they either understand you or they do not.
What happened was when I was getting out of the military when I was 26 years old, then I decided to try and see if I could start learning in math and science, which I never thought I could do before. And when I was, so I went to the university, I started at the lowest possible level of math, which is remedial high school math.
But what I did that I hadn't done in high school or before was I applied some of these same learning techniques that I had used to learn a language to learning math and science. And the thing is for teachers of STEM topics like science, technology, engineering, and math.
It's not like a language where you are testing out what you're teaching and seeing whether your students are actually being successful. You can teach what you're teaching in your class about geometry or whatever, but all you can do is just, if you don't have a background working professionally in a science or engineering field,
All you know is whether you've taught something that is in the textbook for high school students, for example. And a lot of the, the problem is that there was no closure in secondary schools and even primary schools. There's not an interaction with actual practitioners of that field.
Jiani (07:04)
Hmm.
Barb Oakley (07:28)
in the same sense that sort of a Spanish teacher would have an interaction with a Spanish speaker to see whether what they taught was actually viable and working and so forth. So there's this disconnect between what is taught in conventional primary and secondary schools and what
Jiani (07:37)
you
Barb Oakley (07:55)
people actually need to know and how people actually really learn. Because a lot of times, teachers are taught in a way that is kind of similar to how people were taught 40, 50 years ago. And so they teach in this way and they don't know anything about neuroscience. They don't really understand how the brain works. And even in fact, many
Psychologists who inform educators are also not cognizant of what's going on in neuroscience. So, sorry, this is too long of an answer, but I, for odd reasons, I became aware of what was going on in neuroscience research and in how people really think about things. And I realized that there was a disconnect.
Jiani (08:32)
Yeah.
Barb Oakley (08:53)
between what educators were doing and how people really learn. And that's why Terry and I created that course. Long winded answer, sorry.
Jiani (08:58)
Mm.
I really appreciate that because there is, and it's hard to not only have experiences, but also teach in a relevant context. And the connection itself is very challenging. And I appreciate that by seeing the disconnection, you're going to the root causes of like, yes, no matter what you wanted to learn, learning how to learn is kind of...
the foundational skill everybody needs to have no matter if you are in the stand field or beyond and interestingly there's not a curriculum a traditional curriculum about it and we all have like curriculums on subject matter topics but it's not about like wait I wonder
So with all this knowledge, how do people actually digest and process and internalize those messages that they've learned? appreciate, it's a very important high impact work that you're doing for all of us. so following on that thought, let's...
dive deeper onto the role of like curiosity and wonder and most recently with the technology artificial intelligence. So we're curious, what is the foundational neurological process behind curiosity? When we are curious, what is really happening here?
Barb Oakley (10:49)
That is such a great question. research on artificial intelligence and especially generative AI is really beginning to help clarify what happens when we're curious. Because, for example, how do you program a computer to be curious about something? Well, there's a way to do that. And I have to...
remark on a great book called The Alignment Problem by Brian Christian. And this book has excellent explanations of how curiosity is manifested in computer systems. And ultimately, when they really begin thinking about, what is curiosity? It's people's, or
It's the willingness or the, it's the desire to see things that you have not seen before. It all relates to that. And so what they did was they went to a lot, there was a library of old video games and they were trying to program, to create programs that would
solve those video games that would allow you to successfully go through some of these video games. And they created programs where if you got a reward, which we often think of as learning is a reward affiliated activity, where we're learning for a reward of some sort, even if it's only to feel good about something. And they found that they could solve most games
that these bots they created could win most games, except there were a few games that that bot could not even get off the ground. And then they realized that to solve these games, you needed to have, or that bot needed to have curiosity. And so, and it's rather complex, I'll, so I'll...
Fuzzily attempt to simplify it here. But in essence, if what you see on the screen is something you've already seen before, right, the computer has already seen that visual scene, they program it so that what the computer really wants is to see something it hasn't seen before, more novelty. And as it turns out,
Programming for novelty to get like the computer equivalent of little dopamine drops of happiness. If you see something that looks newer, that's what helps you get these dopamine reward hits. so they programmed this. And as it turns out, this is...
very akin to human curiosity. Just sort of that drive to be looking for new scenes, exploring things you haven't seen before. Of course, there were problems in the programming because let's say you have a screen that's filled with static white, you know, and that it's kind of blinking on and off with stuff. The bot would just get fascinated with it and it would stick.
on this static screen. So they had to do a little bit of overcoming of that kind of thing. human curiosity is an intriguing phenomenon, but it is beginning to be replicated now, or it is more than beginning. It's being replicated in intriguing ways that help us understand curiosity even better by...
what is happening with research in AI and generativity.
Jiani (15:16)
That's beautiful and the moment when you're when you're saying that the bot was staring at the white Screen and get fascinated now resonates like in the in the art space People call it like the white space. The white space is the birthing place for all possibilities And it's interesting the machine is picking that up too Beautiful and for
Is there a difference between different kind of curiosity? Say for example when we were a little kid and We get curious we get wonders we get like it's kind of like our like nature of exploring this world and versus as we become adults and Sometimes, we also have that like wondrous and curious experiences and are these the same or are these different?
Barb Oakley (16:15)
Well, children definitely have some different ways of processing things, but this is just my opinion. In some sense, I think there's a very, that that childlike sense of curiosity is similar between some grownups and some kids. Research is showing that there are indeed,
different types of curiosity. And they seem to manifest differently in the brain. One type of curiosity is curiosity simply to gain closure. The other is curiosity for knowledge. You know, like you're really curious about something and you just are kind of compelled to learn more about it because you're so intrigued.
Before, this kind of different, those two different types of curiosity were observed, but people really didn't know what was going on in the brain, that there might be such a difference. Because professors are always like, you should take my class and learn my stuff for the pure joy of it. But they'll observe that some of their classes just
take it because they're fulfilling a requirement and they're not really curious about things. So this closure type of curiosity where you're like filling in the blank because you want to, let's say that someone is asking you to write a report, you need to get some data.
on a particular topic. you get that data, you put it in its closure. But you're not, you haven't, I mean, you're curious about it because you want to fill in that blank, but you're not really curious in the sense of where did that data arise? I really kind of want to know what's going on here. And those who are
Jiani (18:22)
Excuse me.
Barb Oakley (18:36)
curious from an epistemic sense, that is, they really just are curious about what's going on instead of just filling in the blank. They often seem to learn more deeply and so forth. So professors often think, well, I want everybody to have this epistemic curiosity that they're not just learning the answers for the test and so forth.
But the reality is if you went and looked at that professor's background, you just can't be epistemically curious about everything. it's a virtual certainty that there would have been some courses that that professor took that they weren't really caring about. They were just kind of filling in the blanks. And I think it's perfectly normal that sometimes people are
curious from the perspective of, I've got to take this class because it's a requirement and so forth. And that's not necessarily all bad because sometimes this filling in the blank can actually spark real epistemic curiosity and then you begin pursuing it more deeply.
Jiani (19:41)
Mm.
Barb Oakley (19:56)
So you're bringing up very interesting questions here and neuroscientists are beginning to explore this situation much more deeply.
Jiani (19:57)
Hmm.
I like that. think the closure-based curiosity brings sort of like curiosity baseline for everybody. At least everybody was given a chance to pursue anything of interest deeper. And that's the opportunity for them to grow and transcend their current perspectives and knowledges so they can pursue whether deep or deeper and deeper and deeper. So
That's a very interesting concept. And I think that means curiosity is accessible to everyone. And even if they are not really, really wanted to become a next professor about that particular topic, the closer -based curiosity can at least get them through the learning experiences that they need to go through and potentially open up doors for them. that's very interesting.
Barb Oakley (20:41)
You
Jiani (21:04)
and hopeful discovery. so how do we activate that? Like, let's not shoot for like a pure deeper and explorer type of curiosity, but the closure driven curiosity. How do we activate that in a learning experience or experience in general?
Barb Oakley (21:29)
Well, that's a great question and I have a great and easy answer for you. And that is to go to Generative AI, go to ChatGPT or Claude or Gemini, whichever one is floating your boat or all three or more, and ask it to help you become curious about what you are trying to study. Give it a little background about yourself and then prompt it to say,
Can you help me be more curious about, you know, I'm really bored with this thing and I don't want to study it. Can you help me to become more curious? And it will give you some great ideas.
Jiani (22:12)
that is helping us to get, maybe they will help us to see things from a different perspective and that will give us something that we've never seen before and that taps into the originality of curiosity is something that we see we've never seen before and we get rewarded and then that starts. Pleasant surprises.
Barb Oakley (22:36)
And another thing is just ask for a metaphor for something that is difficult for you to understand. So if you're trying to grab something and you're like, hate this, I don't understand, or just I am confused, I can't understand this concept, ask for a simple metaphor that will help you understand the concept. And you'll be amazed at
Jiani (22:42)
a metaphor.
Barb Oakley (23:04)
what it can give you, especially if it knows a little bit about your background. Say, I love soccer, can you give me a metaphor that helps me understand main guard, the concept of main guard in Python programming or whatever.
That's amazing. Yeah, or I like to dance. Can you give me a metaphor of dancing? that's very smart. And we'll...
Barb Oakley (23:31)
Yes.
Jiani (23:39)
closure driven curiosity potentially speed up our learning experiences. Because sometimes the reason that I behind that is that the world is changing faster than ever. And there's many compounding things happening and feels like we not only need to learn, but we also need to learn
more effectively and even faster, not on a superficial level, but like on a very high quality, deep level, can curiosity be one of the potential keys to help us learn faster or build an environment where people can potentially learn faster and better.
Barb Oakley (24:33)
That's such a perceptive question. Yes, it can help you. It's not going to make you like if you're a driver, you know, you can drive a car. It's not going to make you into a one of the world's fastest race car drivers, but it will help you to drive that car more effectively and help cut off.
some of the unnecessary detours so you can get to your destination more quickly. And what do I mean by that? When you have curiosity, what you, so you become, let's say you're just sitting in a class and you're not curious about it. You're drudging through it. But if you are curious, if your professor has a little hook at the beginning of the class that makes you curious about
what she or he is going to be teaching, then you get little, you get curious and that there are dopamine neurons in the brain. And what they do is they can send out dopamine and that dopamine, little, they're like little signaling neurotransmitters. Sorry. And they help.
to make connections between neurons. So when you're curious about something, you get these dopamine neurotransmitters that are sprinkling around, they're like potentiated. And then when you get that little aha of, my goodness, I figured it out because I was curious and I got the answer. That, those little...
dopamine neurotransmitters, they go into that most recent pathway of neurons that were trying to be connected. They go in there and they help in some metaphorical sense, they help seal those connections between the neurons. And then that means you have learned it and you remember it.
If you don't get those connections sealed in between neurons, then you haven't created a set of neural links that you can pull in later on, and you haven't learned it very well. But if you get those connected neurons, it's really wonderful.
Jiani (27:27)
Take your time, take your time.
Barb Oakley (27:27)
Sorry about that. I get so excited sometimes about some of these things that...
Jiani (27:33)
No problem, no problem.
Barb Oakley (27:41)
But anyway, so once you've got those connected neural links in long -term memory, you can pull them much more easily to mind. So think about it. What happens is if you are curious about something, once you figure it out, then you actually make those connections much more strongly than if you weren't curious, and you can much more easily retrieve those ideas.
Jiani (27:50)
Mm.
Barb Oakley (28:08)
when you do need to use them. So it's not a total magic bullet. It doesn't say, you know, for the most part, it's not gonna make this set of links you can always retrieve from then on. You do need to continue to practice with it. But it's like an artist that sketches more deeply, you know, so there's a darker line there that you can see more easily. And that's what curiosity
and these sort of hooks and making yourself curious about things can do for you in your learning.
Jiani (28:46)
I love that. So it's not a substitute. It only works if you put the work. So we still need space repetition. We still need practice. We still need to do everything that we're doing and just adding a little joy and deeper connection to that. if we were to take a walk and eat.
in the neural networks, it's like every step that you step is stronger or all the connection that we make, it's stronger, more effective as it builds the connections between new pathways, between your neural networks. And that's how we learn is to build from your courses to new neural pathways. That's what transcendence really means. It's like new neural pathways.
and curiosity is there to help.
Barb Oakley (29:43)
Exactly right. Yes.
Jiani (29:47)
I love that. And moving to the future. what would be...
a bad, one of the best versions of future you can ever envision, coming from the learning science and perspective with all the technologies. Now we have AI and we also have virtual realities and we also have web 3 .0 and Neural Links where we put the chip and supposedly something new capabilities will be developed. how do we make.
all the sense of all that and what could the best future be like?
Barb Oakley (30:37)
It's, to my mind, mastery learning, allowing students to see it's really hard for schools and teachers right now to differentiate between different students who are learning in different ways and learning at different speeds and have different interests and different curiosity. And so,
You do want to have a minimum speed or, you know, like some level at which all students should be moving forward, particularly in critical topics like learning how to read and learning basic math. But having schools where mastery learning is taking place, in other words, students can go.
kind of at their own pace once they meet that acceptable level and differentiating so that someone who is more interested in this topic can move ahead much more swiftly and go even to advanced levels. Right now we have a situation, for example, in California, you can have a genius level child in mathematics who is still forced
to when they're in third grade and they're already at graduate level at the university in mathematics, they will be forced to justify each step of why they do what for a division problem when they're already doing advanced vector calculus.
Can you imagine how boring and what a turnoff it is for students to be forced through this regimented kind of approach to teaching that's basically just check mark? So I think that in my ideal world, there'll be much more differentiation and
Half of all teachers are below average. So that means students, as they're encountering all these teachers, are going to have some problematic teachers. But AI can lift all boats. It could, even if you have a teacher that is not knowledgeable about the subject that might be teaching, AI can help you.
with learning it. So I think there's lots of potential, lots of good things that can come out of what's arising from Gen. AI. Not to mention that Generative AI has pulled the rug out from all teachers in that suddenly you can't write essays or take tests on your own, go off and do homework and so forth. But it's also, so we'll have to figure those things out.
but it also, for students who are really curious and actually really want to learn, it has opened phenomenal doors.
Jiani (34:01)
And so in that sense, maybe an AI companion or I don't say I can't say companion because it feels like humanize it. An AI learning partner or AI coach, AI tutor. Do you think that's kind of where if we really put AI to work from the student centered or learner centered perspective, that's where
things are moving toward.
Barb Oakley (34:34)
It's hard to tell. Certainly in China, there's been experiments done with teachers who are AI teachers. And these experiments have been very successful, at least from what's been reported. So there is that capability that...
Done.
having generative AI might be very useful, particularly in classrooms where you can't get teachers very easily on that topic, or your teachers might not be very knowledgeable. So we'll have to see what unfolds in the future, but from my perspective, we always have to think of the students first.
and not think of, well, yeah, but this might affect teachers as well. You know, that's true, but who is ultimately more important? Students and student learning or teacher jobs? So that's something that society will argue out.
Jiani (35:46)
student.
Beautiful. And are there any things we need to be mindful? There are some common hesitations moving toward AI is like hallucination and lack of diversity. So in terms of moving forward, there, what do you think are something that we need to be mindful and guardrail?
our real it.
Barb Oakley (36:33)
okay. you had mentioned that, well, one thing that we have to keep in mind is that there is so much care being taken for guardrails in generative AI. And that's a good thing, except that sometimes the people creating the guardrails
are unaware of their own biases. so actually generative AI is often deeply, deeply biased as a consequence of the bias of the creators. So for example, I am working with a team at Carnegie Mellon and this team has like, they have developed a bot that allows you to
allows people to interact on controversial topics. And they can, you know, like talk to one another, but if they say something that is either really a not good thing to say, or that could be rephrased in a better way, there's a bot sort of guardian that watches out for this and it interjects. So it's a wonderful, wonderful
new way to get people who are on opposing sides of controversial issues to actually talk with one another and see what other people's... See the complexity of the issue because often when you're really biased on one way of an issue, you're not really talking to other people and seeing the complexity of that.
issue and what the kind of considerations that that person with the opposing viewpoint has. And what they found is that when you go into generative AI, they can't actually use current platforms because they're so deeply biased that they will not allow people to talk about in an even -handed way controversial topics. They weigh in very strongly on one side or another.
So they had to go to a deeper level of some of these platforms in order to be able to allow people to simply talk without the guardrails actually putting up artificial perspectives on topics. there's, think that generative AI could really create some fantastic.
new ways for people to interact well with one another, to be able to talk. And for me even, I will take my emails sometimes and I'll go, you know, here's how I want to respond to this person. can you rephrase my response so that it sounds a little bit, you know, and it'll come back and I'll be like, that's so much better.
I really, that's what I wanted to say, but I didn't realize that I had some push button responses that weren't so, weren't as nice as I wished they would be. So anyway, there's so much there. So I kind of went off on a tangent here, but there's, I think there's lots to be learned in this new revolutionary new era of generative AI.
Jiani (40:25)
And I think it also taps into the user's ability to make good judgment. When you're using artificial intelligence, you can really tell, you can really kind of maximize what the tool can give you. And for folks who are still yet learning about it,
It may be challenging for them to be more objective and evaluative when they're using the tool.
Barb Oakley (41:03)
And that's why it's still so incredibly important to actually learn things yourself. Because you can't, the deepest level of critical thinking that people often take for granted is just that you know something about the topic or the whole field and you know different perspectives.
Because as I mentioned, generative AI platforms can be deeply biased. They can, as you mentioned, hallucinate. In the brain, there are like two different pathways of learning. One is declarative through the hippocampus. The other is procedural through the basal ganglia. That's experiential learning pathway when you do things. even solving lots of problems and so
And generative AI is like that procedural pathway. The other pathway of our thinking, though, that we're conscious of is that declarative pathway. generative AI doesn't have the equivalent of that. generative AI is kind of like a human that has had its prefrontal cortex knocked out.
And so it will confabulate things just like humans do who have their prefrontal cortex deeply affected by a stroke or by brain damage or of some sort. So what that means is they can't consciously look at what's coming out and say, hey, does that make sense? That's something still that is left to us humans to, you know, we still have that prefrontal cortex.
But so this is why it's so important for us to actually know about some things before we even start using generative AI so that we don't just take for, yeah, know, Chad GBT came up with this answer. And so it's 100 % correct. And we kind of know that, but when you were talking about the AI assistant or the AI teacher,
Jiani (43:13)
You
Barb Oakley (43:20)
You want to kind of keep in mind that it's like it's the AI assistant and teacher that's super good, except for that small percentage of time when it is a little bit psychopathic or wacky, you know, and that's that hallucination that that arises. So the more we can just be prepared for that kind of thing. But sometimes there'll be things that just look like they've got to be correct and you'll take it for granted.
and they're not. So that's again why you have to be so aware and think critically about whatever you're getting when you're interacting with generativity.
Jiani (44:03)
Beautiful. Thank you, Barbara, for such an insightful conversation. And before we move into the magic portion of our talk, I would like to give a brief recap for everyone. So we talked about Barbara's stories behind why she developed her deep expertise and interest and curiosity into learning how to learn coming from an engineering perspective, because you saw that disconnect.
between the deep context, actual practice of the knowledge versus just teaching the knowledge and learning how to learn really kind of bridge the gap and helping the students to build their own power and agency to learn across all contexts and have that sense of curiosity. I also talked about, you know, there's the...
a two types of curiosity. is like results driven curiosity. One is a closure driven curiosity. One is like a deep kind of explorer type of curiosity. And that means curiosity can really be accessible to everyone. If we want to accomplish your goal, we can activate that sense of outcome driven curiosity to help us learn. And we also learn about the neurological benefits and
of curiosities and how when we see something different, our brain gives us motivation and dopamines and other chemicals that helps us to bridge the new neuron pathways in a much stronger way, paired with hard practice, repeated practice, and all the hard work that we do when we learn new things. And then we also talked about
in the future, the possibilities of artificial intelligence to be a potential teaching partner, learning partner is definitely possible. However, it does require us to be more critical thinkers and be able to differentiate good quality of output from AI versus the other hallucinated output from the AI.
The bottom line is we still need to learn. The better that we are a learner, the better we know how learning how to learn, the more power we have when we are interacting with artificial intelligence. Such a great, wonderful talk. So Barbara, moving to the magic portion of it. What did you enjoy creating so much that time disappeared for you when you were a kid?
11, 5, 16, whichever age group that you want to associate with.
Barb Oakley (47:00)
It's funny because I don't think I was creating, but a time of real magic for me was just being on horseback and riding out in the open and watching the sunlight glisten off of my horse's back. And it was, I still have such fond memories of
riding out in open space, you know, on the plains of Texas and so forth. So maybe it has to do with that sense of, haven't seen this before. I'm curious.
Jiani (47:42)
a new, a new perspective, pleasantly new perspective, new, something new.
Beautiful. And horses are interesting. It can be a totally different topic. They're magical creatures.
Barb Oakley (48:05)
They are indeed magical.
Jiani (48:05)
beautiful. They are. Yes. Yeah.
So what role do you think that sense of wonder or childlike wonder or curiosity play in your life?
Or does it? Play a rule.
Barb Oakley (48:31)
That's a good question. I think.
For me, learning languages and then learning how to learn in math and science, for decades I wondered what is the commonality? Why could I take the skills I learned for language learning and use them effectively in learning in math and science? Why? What was going on in the brain that could
that one very different field could help so much with another field. so that, it's kind of like that was gnawing at me, that question, in such a way that I couldn't even formulate that question. I just was wondering about that. But I understand now that I was
Jiani (49:13)
Mm.
Barb Oakley (49:36)
really just grappling with different aspects of how we learn, all towards being able to understand some of these relationships between learning in very different fields. It was actually, it was stumbling across the work of preeminent neuroscientist Michael Ullman of George Washington University.
who has done a lot of work in what he calls a declarative and procedural theory of sort of how the brain learns. his research is pointing towards very interesting phenomena, such as those with dyslexia, for example, seem to have problems with their
procedural basal ganglia system. And that can vary, it's a very complex system. But it means that you have trouble with that very fine, automatic...
sort of knowledge of, you know, what's a B when you see or what is the sound of a B as opposed to the sound of a D. And if you can't hear the difference between those two, you can't read the difference between a B. You know, you can't see a B and a D. You can reverse them. And they thought it was a visual thing, but they think now it's more of an auditory thing. But that could relate.
You know, it's like problems with that basal ganglia procedural system. And some forms of autism seem to relate to problems with the declarative hippocampal system. So you can also use this, those two different pathways to help you get a little bit better sense of the neurodiversity of, of
even neurotypical individuals. So we like people to be able to learn using both pathways. And it can help for some people to, you know, bring, you know, a little extra practice on one pathway versus another. But some people really have trouble with a certain pathway. And that means that you want to, you know, it's kind of like an individual who is, their eyes aren't working.
they're blind. So you don't like put a bunch of things in front of them and say, here, practice seeing anyway. You find other ways to teach. And in that sense, you can find other ways to teach individuals who, for example, have dyslexia that often. So if that impacts your procedural way of learning, often what it can do is enhance
your declarative way of learning. So this is why those with dyslexia or, contrary with autism, they may seem, they do struggle with some things that a neurotypical person wouldn't struggle with, but they can be really, really good at certain things. And so this can be affiliated with this
You know, when one system isn't working very well, you tend to practice more with the other system. It's just like an athlete who has challenges with one leg. So their other leg gets stronger as a consequence. I think it was Bill Wallace was, he, I think it was, he was called like super kick or something. He had a damaged knee. his other, his super foot.
Jiani (53:23)
overcome.
Barb Oakley (53:46)
So his other foot was really, he got really good with it because he had to use that other foot more. sometimes having challenges with one system can actually just be really helpful for the other. And there are Nobel Prize winners, of course, with dyslexia, with autism, know, who...
won the prize for work that grew out of some of the exceptional ways they had of learning about things that used some of that hyper -strengthened system that arose because the other system was having troubles functionally.
Jiani (54:39)
I love that. It's like cultivated superpower.
Barb Oakley (54:46)
Yeah, yeah.
Jiani (54:48)
That's great. And what do you think is your magic, Barbara?
Barb Oakley (54:55)
magic. I'm a very slow learner. I struggle to understand things. I have to get a picture in my mind a lot of times to understand things well, but my magic is that that helps me so that I can explain things well to others once I do understand something.
I have a broad, broad background, so sometimes I put things together that are from very, very different backgrounds and that can make things more interesting.
Jiani (55:35)
And more opportunities for you to create experiences that activate people's curiosities because of the broadness and the depths and the cross -disciplinary and the visuals and...
That's great. And I think that's the magic that powers learning how to learn and why millions and millions of us find it very helpful. You make difficult knowledge super, super accessible and fun and visual. Interesting.
Barb Oakley (56:20)
I try my best. But that's, think, also related to what makes you a superstar.
Jiani (56:29)
Yes, yes. So I hope everyone who is listening to this podcast think and be curious about themselves first and see are there any opportunities for ourselves to develop that sort of superpower, that magic or maybe you already have one and you just didn't really look and start practicing that more.
And that would be a beautiful world where everybody knows exactly where their magic is and just make the best out of it and to serve our to self -serve our human beings and everybody
beautiful. And for folks who wanted to get into connecting with Barbara, her information is in the show notes. Please connect her. She's super busy, though. Please connect with her and create magic, create partnership, create collaborations and create just new and curiosity intriguing things for the world.
And thank you, Barbara, for sharing your magic, your wisdom, your insight with us. And it's such an honor and pleasure to have you with us today.
Barb Oakley (58:03)
Thank you for your wonderfully curious questions.
Jiani (58:10)
Beautiful. And thank you.
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