Speaker A
How could we use AI to learn better? I mean, there's got to be some way to optimize learning with AI. I think it's the perfect tool for it. Uh, it is not entirely clear yet how. So, yeah. Uh,
Eero Alvar explores how AI can optimize personalized learning by addressing inefficiencies and demonstrating a live AI-powered learning system.
Key Takeaways
- Personalized AI teaching can overcome inefficiencies of traditional many-to-many learning relationships.
- A single AI interface can deliver multiple perspectives without overwhelming the learner.
- Trust in AI teaching is built through reliable verification and system design.
- Continuous feedback and active practice are critical components of effective AI-assisted learning.
- Optimizing both teaching content and mental resource allocation enhances learning efficiency.
What the video covers
- The video discusses using AI to optimize learning by addressing inefficiencies in traditional teaching methods.
- One inefficiency is that one teaching outlet serves many learners, which is not optimal for individual needs.
- Another inefficiency is that learners use many outlets, causing cognitive switching costs.
- AI can act as a single teacher interface, aggregating multiple perspectives while tailoring teaching to the learner's understanding.
- Trust in AI as a teacher is engineered into the system rather than built over time.
- The system runs on two principles: optimized teaching and optimized allocation of mental resources.
- The AI probes the learner’s current knowledge to teach at the edge of their understanding.
- Continuous feedback and practice problems are essential for calibration and deeper learning.
- A live demo showcases the AI teaching process, including planning, fact-checking, and presenting learning paths.
- The video emphasizes maximizing cognitive effort on material rather than logistics, using tools like Obsidian and Markdown integration.
Chapters
- 00:00Introduction to AI and Learning Optimization
- 01:28Inefficiencies in Traditional Learning
- 02:38Principles of the AI Learning System
- 04:22Trust and Verification in AI Teaching
- 06:03Feedback and Practice for Effective Learning
- 09:56AI Teaching Process and Demo Overview
- 12:03Detailed AI Probing and Planning Phase
- 15:03Applying AI to Complex Topics and Final Thoughts
Full Transcript — Download SRT & Markdown
Speaker A
this video will be me sharing my own current approach to this. First, uh I'll go through the reasoning behind the approach and the design, and then we'll do a live demo. All right. So, this is the standard way of learning that we're
Speaker A
this video will be me sharing my own current approach to this. First, uh, I'll go through the reasoning behind the approach and the design, and then we'll do a live demo. All right. So, this is the standard way of learning that we're
Speaker A
directions of it produce their own kind of inefficiency. First, one outlet teaches many. An outlet that's designed for many people cannot be optimal for any one of them. That's because optimal, quote-unquote optimal, teaching uh that would clearly depend entirely on
Speaker A
used to. Each outlet, uh, so, a teacher, a book, a course, whatever, uh, they all are designed to teach many students. And also, each student learns from many outlets. So, there's a many-to-many relation between the learners and the outlets, and both
Speaker A
the learner already holds, and also stuff that they can't yet understand. So, it would work exactly at the edge of their understanding. So, ideally, a teacher would have exactly one student.
Speaker A
directions of it produce their own kind of inefficiency. First, one outlet teaches many. An outlet that's designed for many people cannot be optimal for any one of them. That's because optimal, quote-unquote optimal, teaching, uh, that would clearly depend entirely on
Speaker A
material. But, I think there's a much deeper cost in this, which is trust. With an unfamiliar outlet, the brain I I think the brain kind of hedges. So, it won't fully commit to accepting a fact until the outlet has proven itself and
Speaker A
the learner's current understanding. And this includes both the whole teaching arc from their current understanding to their goal understanding, as well as each explanation along the way. An optimal teaching path, then, uh, would be one that minimizes teaching stuff that
Speaker A
and the other is on X posted by some random guy. Now, even though the explanations are identical, I think it is clear that we're going to learn a better from the familiar source. The brain is going to have a much easier
Speaker A
the learner already holds, and also stuff that they can't yet understand. So, it would work exactly at the edge of their understanding. So, ideally, a teacher would have exactly one student.
Speaker A
All right? So, ideally, a student would have exactly one teacher. Now, we've established that the ideal scenario is one-to-one in both directions, but there's an obvious objection to this, which is that having only one teacher means getting only one perspective. But,
Speaker A
All right. So, second direction, uh, one student learns from many outlets. This means many teaching styles to get used to, many notations, many levels of reliability, many interfaces, and the switching between them, uh, costs mental effort that's not gone into learning the
Speaker A
Uh instead, it aggregates all of them and delivers them through one interface. So, we don't lose many perspectives. Now, with AI being the teacher, trust isn't really built over time. Rather, it's engineered into the system. The reason we have to have reliable verification
Speaker A
material. But, I think there's a much deeper cost in this, which is trust. With an unfamiliar outlet, the brain, I think the brain kind of hedges. So, it won't fully commit to accepting a fact until the outlet has proven itself and
Speaker A
Now, the two inefficiencies that we went over, they give us the two principles that the system runs on. First, optimized teaching, the answer to the first inefficiency. And two, optimized allocation of mental resources, answer to the second one. And this does not
Speaker A
is familiar enough. I think a good way to illustrate this is with the following example. Let's say we want to understand the hairy ball theorem, and we have two identical explanations. But, the other one is in a 3Blue1Brown video,
Speaker A
planning, finding resources, verifying facts, figuring out what to learn and in what order. All of that is for the system to absorb. Next, the actual process, how this is implemented. First, optimized teaching, to teach optimally, whatever this might be very different
Speaker A
and the other is on X posted by some random guy. Now, even though the explanations are identical, I think it is clear that we're going to learn better from the familiar source. The brain is going to have a much easier
Speaker A
questions. It starts off with very broad of and then basically binary search is the edge on every possible strand that the lesson will depend on. So, it's going to get a very detailed map of the learner's understanding. So, that's phase one,
Speaker A
time internalizing the information when it trusts the outlet, even though the explanation is identical.
Speaker A
diagram. And okay, basically two reasons for why why it has to show a graph. One, it gives the learner a better sense of what's to come. And also two, the actual reason I implemented it is to actually force the AI to reason everything out.
Speaker A
All right? So, ideally, a student would have exactly one teacher. Now, we've established that the ideal scenario is one-to-one in both directions, but there's an obvious objection to this, which is that having only one teacher means getting only one perspective. But,
Speaker A
to install the system with your own learning philosophy and how you learn best. But one detail that I think is going to be important regardless of the exact way of learning is feedback. So, the AI is going to quiz you periodically
Speaker A
I think that the objection conflates a source with an interface. So, a teacher doesn't reduce the number of sources, perspectives.
Speaker A
important feedback for yourself. Two, the system needs continuous feedback to stay calibrated. And three, applying the material, doing practice problems, using the stuff that you learned, also just helps you learn better. It's It's a part of how the new
Speaker A
Uh, instead, it aggregates all of them and delivers them through one interface. So, we don't lose many perspectives. Now, with AI being the teacher, trust isn't really built over time. Rather, it's engineered into the system. The reason we have to have reliable verification
Speaker A
Now, let's Let's see it in action. All right, we're in the Pi Agent harness now in my learning directory. And yeah, this is where I've got everything set up. So, in the dot Pi folder, I've got the teach
Speaker A
and fact-checking is one, correct information. Obviously, we absolutely don't want the AI to hallucinate false information. But also, two, because it makes learning easier when we know that the system is reliable. So, one interface fitted to one mind over all sources.
Speaker A
Hopefully, we'll see those also. So, yeah. I think the only way to demo a system like this is actually to try to learn something. And to really go full circle, uh in my previous learning-related video, I mentioned the
Speaker A
Now, the two inefficiencies that we went over, they give us the two principles that the system runs on. First, optimized teaching, the answer to the first inefficiency. And two, optimized allocation of mental resources, answer to the second one. And this does not
Speaker A
obviously, I don't We're not going to get to this level, but uh I think this is a very good example to showcase the system with a an actual learning process. So, yeah. Uh let's begin. Let me pull up Obsidian here cuz this is
Speaker A
mean removing difficulty. Instead, it's about concentrating all cognitive work into the material itself. We want to maximize struggle. We want to learn difficult things, so struggling is very important, but it has to be in the material itself and not in logistics,
Speaker A
This is what the MD log extension is for. It lets me link a Markdown file to the session, and then everything is going to get printed right here. Just a nicer way to see things and also get the LaTeX
Speaker A
planning, finding resources, verifying facts, figuring out what to learn and in what order. All of that is for the system to absorb. Next, the actual process, how this is implemented. First, optimized teaching, to teach optimally, whatever this might be very different
Speaker A
And we're using Kimik 3 on Macs cuz I really found that the intelligence of the model really matters in teaching. The teaching instructions are quite They're quite specific.
Speaker A
from person to person, but in order to do that, the system obviously needs to know the person's exact current understanding. So, it has to measure, and it does this with a quiz tool, which lets it ask graded multiple-choice
Speaker A
Let me also I've got this note here where I can just yap into. Basically, just to give it more context and sort of talk through my reasoning.
Speaker A
questions. It starts off with very broad of and then basically binary search is the edge on every possible strand that the lesson will depend on. So, it's going to get a very detailed map of the learner's understanding. So, that's phase one,
Speaker A
The network done by the field on the particle. All right, next one. The divergence of a vector field at a point measures the net outward flux per unit volume at that point.
Speaker A
probe. Second, phase two is plan. Basically just reasons everything out. How do I teach this mind this specific thing? And here's also where it fires off its first verification and fact-checking sub-agents. And then presents the plan as a mermaid graph and
Speaker A
In this case, I gave you very little context, so we're going to get a long probing phase.
Speaker A
diagram. And okay, basically two reasons for why it has to show a graph. One, it gives the learner a better sense of what's to come. And also two, the actual reason I implemented it is to actually force the AI to reason everything out.
Speaker A
each event. They mix. A purely electric field in one frame has both electric and magnetic parts in another. Hold on. Only E changes. B stays fixed.
Speaker A
That's the real reason. So, it cannot just cheat and wing it. And then finally, the teaching itself. And this phase is going to be very different because it's going to depend entirely on how you want to learn. You want
Speaker A
They mix. All right, it's stopped with questions. Um so yeah, uh finally done with the probing phase.
Speaker A
to install the system with your own learning philosophy and how you learn best. But one detail that I think is going to be important regardless of the exact way of learning is feedback. So, the AI is going to quiz you periodically
Speaker A
questions and it handles all the logistics. Now we've got a researcher still going on fact-checking stuff.
Speaker A
to check whether you actually understood the thing. And this is important for three reasons. One, it's very easy to sort of gaslight yourself into thinking that you understood something, especially when learning with AI. So, actually testing your understanding is
Speaker A
I did start rendering the mermaid in the 2A, but don't know what happened. It was probably in the most recent release, I believe, added mermaids. But anyway, we get it in the obsidian. This is the plan. Let's go. A
Speaker A
important feedback for yourself. Two, the system needs continuous feedback to stay calibrated. And three, applying the material, doing practice problems, using the stuff that you learned, also just helps you learn better. It's a part of how the new
Speaker A
Okay, perfect. It's it's using the visualization skill. So we're going to get some visuals soon. All right, it's making an SVG. And the reason that these are done in sub agents is to obviously preserve context but also the sub agents will look at the image to
Speaker A
information and the new understanding locks in. So, yeah. Uh, that's the general idea.
Speaker A
Yes. So, we're going to get that in the next message. But now the first node. So, it's going to It's going to slowly walking down the path one reasoning step at a time. Because what usually happens if you're like talking to chat GPT or
Speaker A
Now, let's see it in action. All right, we're in the Pi Agent harness now in my learning directory. And yeah, this is where I've got everything set up. So, in the dot Pi folder, I've got the teach
Speaker A
co-vectors. Let me read through this. All right. So, now it's introduced co-vectors and also given a new perspective on the X.
Speaker A
skill, some visualization stuff, uh, the quiz extension, the MD log extension, which I'll show you in a bit, and two subagents to make visuals.
Speaker A
Now, yeah, it's going to incorporate the visual which will be all rendered into Obsidian and automatically here as an embed embedded file. Let's alpha be 3D X minus 2D Y and V this. What is alpha of V?
Speaker A
Hopefully, we'll see those also. So, yeah. I think the only way to demo a system like this is actually to try to learn something. And to really go full circle, uh, in my previous learning-related video, I mentioned the
Speaker A
Yeah, it does the job but it's definitely not the same as this. But yeah, cool features though. All right, so now we're extending the covectors to a covector field. All right, so now we get a new perspective on what the line integral actually is.
Speaker A
Maxwell's equations and how they can be expressed in just two equations using differential forms. And as you can see, uh, I don't really know anything about differential forms. So, I think this is what we're going to learn today. Now,
Speaker A
too many instructions to worry about. So I'm fine with these LLM-isms. It's not X, it's Y.
Speaker A
obviously, I don't, we're not going to get to this level, but, uh, I think this is a very good example to showcase the system with an actual learning process. So, yeah. Uh, let's begin. Let me pull up Obsidian here because this is
Speaker A
So when we're going to get I think this is the wedge product. But yeah, I like that it moves one reasoning step at a time. So if at any point I have questions, I can always ask.
Speaker A
what I use. It's sort of like the UI for everything. Learning differential forms.
Speaker A
assume that this continues, yeah. A K-form will be the kind of thing a K-dimensional surface can eat.
Speaker A
This is what the MD log extension is for. It lets me link a Markdown file to the session, and then everything is going to get printed right here. Just a nicer way to see things and also get the LaTeX
Speaker A
Oh no, we're getting it. Now we're getting the wedge products. That's cool. Machines that are bilinear and antisymmetric. Yeah, where do we get them? I'm sure this is not antisymmetric. The minimal fix is the oldest trick in the book. Aha. Yeah,
Speaker A
rendering. So, yeah, that's my solution. And yeah, it's also nice to have persistence artifacts from each learning session. So, yeah, let's begin teach. I want to get like a solid introduction to differential forms.
Speaker A
Um this is going to be this one, I believe. Yeah. But yeah, anyway, where did we get to in the DAG? Here.
Speaker A
And we're using Kimik 3 on Macs because I really found that the intelligence of the model really matters in teaching. The teaching instructions are quite, they're quite specific.
Speaker A
time in my previous learning related video. So, what this video has been really about is how to take a learning philosophy or way of teaching and implement that as an AI system. I think the main reason for why this has worked
Speaker A
Anyway, [snorts] let's see how this goes. We're going to get ques
Speaker A
understanding to our goal understanding, and two, the individual steps and explanations along the way.
Speaker A
Yes. Anyway, just some ideas for you for you to think about. I'd like to hear your your thoughts on this. And how how how could the system be improved? How do you use AI in learning? I'd like to
Speaker A
know. I'm very invested into refining this further. So, yeah, uh that's it.
Topics:AI learningpersonalized educationoptimized teachingmental resource allocationlearning efficiencyAI tutorfeedback in learningfact-checking AIcognitive loadEero Alvar
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