AI Engineer World's Fair 2024
The Multimodal Future of Education
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The Multimodal Future of Education
From children training their own classifiers to Gemini asking questions about handwritten equations, Stefania Druga explores AI that helps learners experiment without taking over.
From a talk by Stefania Druga
Children are already using AI. What are they learning?
How many children are already playing with generative AI? Stefania Druga opens by asking parents in the audience that question. The educational challenge begins with adoption that is already happening, rather than a hypothetical future classroom. Her opening slide invokes a Salesforce survey, but reverses its denominator: the September 2023 survey found that 70% of Gen Z respondents used generative AI, not that Gen Z accounted for 70% of users. It surveyed more than 4,000 people across the United States, United Kingdom, Australia and India.
That adoption sits alongside urgent educational needs. Druga points to early literacy, pandemic learning losses and gaps in mathematics and reading. The reading statistic needs a similar correction: the World Bank’s June 2022 estimate was that 70% of ten-year-olds in low- and middle-income countries could not understand a simple written text. Druga also cites roughly 60% of children and teenagers being left behind in reading and mathematics. That figure should not be treated as a measured COVID effect: the related UNESCO finding about widespread failure to reach minimum proficiency was published in 2017.
The need extends beyond K–12 to adults who must reskill. Meanwhile, Druga reports that students already use AI for homework, recommend it to peers and sometimes prefer it to human tutoring. These observations create a practical design question: how can an attractive, readily available tool support learning rather than simply become a source of completed answers?
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What children think an AI knows
Generative AI enters a world where children have already encountered voice assistants, chatbots and smart toys. Druga calls these children the first AI generation, situating their experience in the spread of household voice assistants and citing adoption in half of US households. Her research on children’s relationships with these devices began at MIT in 2015. The central observation is that a child’s perception of a device influences how they interact with it and what they learn from it.
Children generally saw the devices as friendly, but friendliness did not imply a uniform judgment of intelligence. Druga describes a striking age difference in her research:
| Children | Reported perception of voice assistants |
|---|---|
| Ages four to six and a half | More skeptical of how smart Google Home or Alexa was |
| Older children entering school | More likely to describe the devices as smarter than themselves |
These observations concern voice assistants, before the talk turns to generative AI. A learner’s mental model shapes both the questions they ask and how much they trust the answers. Teaching AI literacy therefore means helping children investigate how a system works, not merely teaching them to operate its interface.
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Make the AI something a child can change
To make that investigation possible, Druga built Cognimates, which she dates to 2016: a free, open-source platform extending Scratch’s block-based programming environment. One child’s program turns a robot into a hide-and-seek partner. Add a loop, and the child can run around the room while the robot repeatedly tries to find her. The behavior becomes something the learner constructs and revises.
Cognimates also lets children program smart lights and voice assistants, then move from controlling devices to training custom models. They supply image or text examples and use the resulting models inside their own games and applications. One student trains an image classifier to distinguish unicorns from narwhals. When a drawing is presented, the program exposes both the predicted class and its confidence. Druga points out that confidence on the drawing is low. The child can now inspect a model’s uncertain judgment instead of receiving a label that appears unquestionable.
The same environment supports very different projects: inspecting what is in food, playing rock-paper-scissors and making a robot speak like Shakespeare. Across these examples, the useful common structure is a short path from a child’s idea to behavior they can observe. Programming and model training become materials for play.
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Tinkering changes the judgment of intelligence
Druga reports that Cognimates was used internationally and translated into more than 30 languages. To study its educational effects, she conducted a longitudinal study in public and private schools. Children answered questions about AI before participating in learning activities, then answered the same questions afterward. The activities included text training, image training and smart-home programming. Afterward, children were less certain that an AI device or model was smarter than they were.
An embedded classroom video makes the activities concrete. A child describes playing rock-paper-scissors through a camera with a robot. Another participant compares learning through repeated attempts with the way people improve at unfamiliar tasks. Parents and teachers describe AI knowledge as a necessary skill, even while acknowledging that the technology can be intimidating. One participant offers a mechanical analogy: a father once took apart a car to understand it; children should similarly be able to investigate technologies that adults usually build for them.
Returning to the study, Druga describes a significant before-and-after change in children’s perceptions, without giving a numerical effect size. Her explanation is the learning process the platform enables: children form hypotheses, test them and iterate quickly. A suitable sandbox makes those actions accessible. Skepticism emerges from encountering the system’s behavior directly, including its mistakes and uncertainty.
Children are not the only learners in that sandbox. Parents and teachers also need to understand AI, and pandemic home learning created opportunities for families to explore it together. An early Cognimates tutorial illustrates another possibility: the programmable system itself helps the learner find the Ask block needed to ask a question. The thing being programmed becomes a collaborator in learning how to program it.
That interaction suggests a richer relationship with a chatbot. When an answer is unsatisfactory or inappropriate for a child’s age, the learner needs ways to influence the system. They may also want to bring their own language, culture or unusual interests into the interaction. The educational opportunity is to make that relationship understandable and adjustable, rather than leaving the learner to accept whatever the model produces.
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Help families create without taking over
Before building a programming copilot for families, Druga investigated what children and parents wanted from one. She describes a multiweek study involving families in ten US states. The closely matching AI Friends study used a researcher-operated assistant, so these findings establish design requirements rather than the autonomous capabilities of a model. One of the most valued roles was helping a family begin: an AI friend in Scratch could suggest coding ideas when a project stalled at the blank-page stage.
The next role was translating an intention into code constructs. A child might want a bear to jump over a hedgehog but not know how to express the movement. A small Scratch script for the bear illustrates the kind of construct a helper could introduce:
scratchblocks
when green flag clicked
repeat (10)
change x by (4)
change y by (5)
end
repeat (10)
change x by (4)
change y by (-5)
end
The two loops separate ascent from descent while maintaining forward motion. The learner can change the movement and place the hedgehog along its path. The larger design requirement is creative ownership: children should experience themselves as the people doing the coding, with the assistant helping them through a point of difficulty.
The assistant also has a social role. Druga describes it prompting a child to consider a parent’s opinion, encouraging turn-taking and helping the family work together. That third participant can make collaboration easier, but its presence must remain optional. Families need to be able to switch it off when it becomes distracting or when they want to work independently.
Some limits are outside the assistant’s reach. It cannot solve siblings fighting over a shared laptop. It also cannot always break a complex concept into manageable steps; parents remain important sources of explanation and support. A family copilot therefore needs to fit around human relationships and assistance, rather than assume that generating a response resolves the learning problem.
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Evaluate the help, then expand the medium
With those family requirements in view, Druga turns to evaluating generative models. She reports that leading models were useful for explaining Scratch programs, generating ideas and asking questions that helped families explore and test new games. She also describes a published benchmark; the matching Scratch Copilot Evaluation assesses assistance on bounded programming tasks. The relevant distinction is between the quality of that assistance and measured improvements in children’s learning. The talk provides no model-by-model scores.
Multimodality expands the possible working material beyond code. Druga sketches several directions:
- Games and simulations: A creative sidekick could participate in Minecraft, other games, or physics and science simulations.
- Physical making: Photographs of flowers and preferred colors could inspire 3D models that a learner prints and paints.
- Craft projects: A generative model could help someone explore ideas for knitting.
These are proposed extensions of the collaborative pattern: the learner brings an interest or an artifact, receives help developing it, and continues making.
A learning companion can also address a more specific instructional problem: identifying math misconceptions. Druga describes work with Nancy Otero on a benchmark intended to capture common K–12 difficulties and evaluate whether models recognize them in chatbot conversations. She presents it as a first benchmark of its kind and offers a download link. Its purpose is diagnostically different from checking whether a model can produce a correct answer: the system must notice what the learner misunderstands.
The broader invitation runs from Cognimates to tools people explore through Hugging Face: make AI tinkering available at every age. Druga then moves from research to three experiments built the previous night with the then-latest Gemini API. Their premise is to use AI for learning through play and making, with live success still uncertain. The first experiment works with science drawings.
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A drawing becomes the next science question
The envisioned setup is a webcam over a drawing surface, where a learner can draw and revise in real time. Without a table onstage, Druga uses prepared drawings instead. She presents a balance scale with a weight on each side. The system asks what would happen if another five kilograms were added. She then proposes a revised drawing with ten kilograms on each side. The drawing is both the input to the model and the medium through which the learner can respond.
The interaction has a simple instructional cycle:
- Present a drawing of a situation.
- Receive a question grounded in that drawing.
- Revise the drawing to explore a possible answer.
- Present the changed drawing and continue the exchange.
Druga next tries water and CO₂, asking what happens when they mix. She returns to the idea of drawing beneath a webcam: the value is the possibility of moving fluidly between physical marks and model-generated questions.
The Earth-and-moon example exposes the fragility of visual interpretation. The initial interaction describes Earth as being hit by something. Druga adds another arrow, and the system eventually recognizes the moon. That correction matters to the learning loop: before an assistant can ask a useful science question, it must interpret the learner’s representation well enough to discuss the intended situation.
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Prompt the learner’s next action
The math experiment applies the same pattern to successive pieces of written work. Druga presents an expression and receives a prompt to solve what is inside the parentheses. She shows a version with that step completed, then receives guidance about multiplication involving parentheses. With another example, the interaction moves to simplification and the rule that a nonzero number divided by itself equals one. The intended behavior is to offer a question or a next-step hint while leaving the learner to do the work.
The live interaction is uneven. Druga notes that she needs a better background, and a more complicated example produces an unhelpful response about four on an axis. She had hoped for a better question. Recognizing the input and choosing a pedagogically useful response are separate requirements; a visual tutor must do both to sustain the learning exchange.
The third experiment shifts from solving problems to encouraging curiosity. It asks about what a lady is doing, the colors of a flag and the shape of a star; Druga notes that it has asked about Jordan. With an apple, it asks about the kind of apple and then its smell. The smell question extends beyond information the camera can supply: it invites the learner to inspect the physical object and contribute an observation. An intended origami example does not happen because Druga cannot find it.
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From a handwritten equation to an adaptable template
Druga invites the audience to suggest another science, math or object example. Someone requests a system of equations, but the expression supplied for the demonstration is a single equation:
She writes it and presents it to the camera. The tutor prompts her to subtract seven from both sides. She attempts that step, after which the next prompt is to divide both sides by two.
The algebra behind those prompts is straightforward:
At this point, dividing by two is the next proposed learner action. The demonstration’s useful result is the transition from one piece of handwritten work to a specific next-step instruction, rather than immediately displaying the final value of x.
Druga says the demo template is open source and fewer than 100 lines. Her setup instructions are correspondingly small: create an API key and supply your own instructions. She describes the key as free at the time of the demonstration; that historical setup is not a statement about current Gemini access or pricing. Custom instructions are the adaptation point for changing what kind of help the learner receives.
The closing challenge is to think beyond chatbot interfaces and delegating questions. Put tools into young people’s hands so that they can tinker with AI and learn how it works. A child who programs a behavior, changes an example or redraws a problem has a way to investigate the technology while learning with it. Druga closes by pointing to her research website and inviting questions afterward.
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Resources
From the talk
Scratch-based interface for family activities involving games, robots and training AI models.
Four-country survey reporting generative AI adoption by generation, including usage among 70% of Gen Z respondents.
Further reading
- AI FriendsPaper
Three-week family study of a researcher-operated coding assistant, examining creative agency, parental support and idea generation.
Evaluation of language-model assistance for explaining Scratch code, debugging and generating project ideas.
World Bank explanation of pandemic-era reading difficulties among ten-year-olds in low- and middle-income countries.
Updates since the talk
Later paper documenting 55 middle-school algebra misconceptions and errors, 220 diagnostic examples, and model evaluations informed by educators.
Read the complete timestamped transcript
- 0:00
[upbeat music] Uh, hi everyone, I'm Stef.
- 0:16
I'm gonna talk about the future of education with multimodal AI. Uh, we are here at AI Engineering Summit, and AI engineering starts very early. So I'm curious, how many of you have kids?
- 0:29
How many people in the room have kids?
- 0:32
Okay. Wonderful. Uh, how many of your kids, uh, have played with generative AI so far?
- 0:40
Okay. So, um, you won't be surprised [REDACTED:age] see the next slide. Basically, seventy percent of generative AI users, uh, are from Generation Z. This is a recent study from Salesforce.
- 0:52
So it starts very early, and the reason I care about the future of education with generative AI is because edu- education needs a wake-up call. So we know that early litra- literacy rates, um, need [REDACTED:age] be improved around the world.
- 1:07
Only seventy percent of [REDACTED:age] actually can read and understand a simple story. At the same time, we've seen a big gap in learning that happened during COVID. Sixty percent of children and teenagers are left behind, in particular in math and reading.
- 1:23
And it's not just K through twelve. For older, uh, people, for adults, like, we need [REDACTED:age] do a lot of reskilling. So
- 1:33
multimodal AI has a potential [REDACTED:age] transform education. Uh, and we are-- we know that students are using these tools for their homework, they recommend it [REDACTED:age] their colleagues, and we also know that they prefer using these tools over a human tutor.
- 1:49
So this is not new. Uh, we are right now dealing with the first AI generation. These children have been growing up with AI since twenty fifteen. Half of households in US had some sort of voice assistant.
- 2:04
And I started researching this in twenty fifteen, um, at MIT, and basically showed that youth perception of voice assistants, chatbots, smart toys really influence how they interact with these devices, how they learn from them, and how they perceive them.
- 2:22
So overall, they perceive these AI devices friendly, um, but they also have [REDACTED:age] different perception of the intelligence of the devices, and age plays [REDACTED:age] hu- huge role. So younger kids, we're talking four [REDACTED:age] six and [REDACTED:age], are much more skeptical of how smart Google Home is or Alexa.
- 2:40
And older kids, like, the moment they start going [REDACTED:age] school, they all say, "Voice devices are smarter than I am." And we're just talking about voice assistants. We're not talking yet about generative AI tools.
- 2:51
So how can we change that and, and why does that matter? It matters because their perception of how this technology works influences why-- what they expect, and their mental models is influencing the type of queries they're gonna ask, how much they trust the answers.
- 3:07
So we need [REDACTED:age] cultivate AI literacy and critical understanding of this technology. [REDACTED:age] that end, I built this open source and free platform called Cognimates in twenty sixteen. It expanded Scratch, which is the largest platform for coding for kids, and it ba- basically allowed children [REDACTED:age] do programs like this.
- 3:26
Can someone guess what this program does? [muffled voices] Yeah, it's a game. It's a hide and seek game, right? So she's like, she programmed a hide and seek game with the robot, uh, and if she puts a loop, she can run around the room, and the robot is constantly gonna try [REDACTED:age] find her.
- 3:43
Now, this is the first step. Um, it's using, like I mentioned, block-based, uh, programming language, expanding Scratch. And at the time, like, it allowed kids [REDACTED:age] not only program their smart lights, their voice assistants, but also train their own custom models.
- 4:00
So they can train models with examples of images or examples of text, and then use those custom models in their own games and application. So for example, here, like, this student trained a model [REDACTED:age] distinguish between unicorns and narwhals.
- 4:16
And then not only it gets a prediction when it plays with the game, but it also gets the confidence level. How confident is his custom model that the drawing is a unicorn?
- 4:26
And we see the confidence is pretty low. So they made all sorts of things, like looking at what's in their food, uh, trying [REDACTED:age], like, uh, program games like rock, paper, scissors, uh, get, like, uh, the robot [REDACTED:age] talk like Shakespeare.
- 4:43
And this was used all over the world. It's translated in, uh, more than thirty languages. And, um, the good news is that we evaluated this [REDACTED:age] see how it increases that critical understanding of AI and how it helps with AI literacy.
- 4:58
So [REDACTED:age] do that, um, uh, I did a longitudinal study in public and private schools where we asked questions of what kids think about AI before, then we allowed them [REDACTED:age] engage in AI learning activities, and then we asked the same questions at the end.
- 5:13
And what we found after they learned how [REDACTED:age] do text training, image training, smart home programming, um, is that they became much more skeptical of the AI smarts. Like, in the beginning, they would say like, "Yes, you know, Google Home is smarter than me," or, "This model is much better than me."
- 5:31
And after they learn how it works and how [REDACTED:age] train it, they were not so sure it's smarter than they are. And I'll show you a quick video [REDACTED:age] see how that went.
- 5:42
So we were programming robots. You could play rock, paper, scissors.
- 5:50
You could rock, paper, scissors into a camera, and it will compute it for the robot's turn, and then the camera will go on the robot's turn. And it's, uh, rock, paper, scissors.
- 6:03
Shoot.[gentle music]
- 6:05
You do get better at practically anything. It's like us. We might not know everything at first, but as you keep trying, you get better.
- 6:15
Everyone has heard about machine-based learning or AI learning. And the sort of question [REDACTED:age] ask for a lot of the more tech-savvy parents was like, "Go for it."
- 6:23
Technology is gonna be a huge part of their lives, much more so than my life. If it's scary for some people, this AI technology, I totally get it. But as a parent and as a teacher, I thought it was really important because these are skills that twenty-first century kids need [REDACTED:age] have.
- 6:39
When my dad was young, he bought a car and took it apart [REDACTED:age] see how it worked. So you teach people that young how these things that grown-ups mostly program, how it works.
- 6:56
So as I was saying, AI engineers in the making. Um, and this, this is the, uh, significance difference, like, [REDACTED:age] their perception of the smarts of AI before and after doing these learning activities.
- 7:09
So how did they... Why did that happen, right? Like, why would-- why did they became more skeptical, more critical, and also more literate in how [REDACTED:age] read and write with AI?
- 7:19
Is because by providing this platform and allowing them [REDACTED:age] tinker and form hypothesis and test them, we basically allow them [REDACTED:age] engage in the scientific process, just like researchers do, just like we do, right?
- 7:32
But we needed [REDACTED:age] have the right sandbox, the right platform for them [REDACTED:age] be able [REDACTED:age] quickly tinker and quickly iterate. So kids are not alone in learning this.
- 7:41
Parents need [REDACTED:age] learn too. Teachers need [REDACTED:age] learn too. And we've seen during the pandemic, when kids were stuck at home with, with parents, a huge opportunity for them [REDACTED:age] learn together.
- 7:52
So I'll show you, uh, one of the early demos of Cognimates.
- 7:59
Oh, the audio is not working on this one. I'm not sure why. Basic-
- 8:04
There you go. You did it. No, I need you [REDACTED:age] help me ask a question. For that, we'll need the Ask block. See if you can find it. [popping]
- 8:20
Awesome.
- 8:21
So the thing that you're programming is kind of collaborating with you [REDACTED:age] teach you how [REDACTED:age] program it, right? Just imagine applying that [REDACTED:age] any of the chatbots we have today, right?
- 8:31
Like, when you're not happy with the answer, or maybe the answer is not age appropriate, or you wanna teach... You also wanna teach something [REDACTED:age] the model about your language, your culture, weird facts that you're interested about.
- 8:44
How do we do that, right? Um, so I did another study, uh, where I-- this was with kids and parents in ten different states in US over multiple weeks, where we wanted first [REDACTED:age] learn, how do we design a copilot for programming for families?
- 9:01
So before we start and build it, like, what do they want? What works and what doesn't? So what we found was that some of the things that kids and parents liked the most was [REDACTED:age] generate coding ideas with an AI friend.
- 9:15
Like, if they had a copilot in, in Scratch. Um, and this was very, very helpful. Here are some quotes. Um, because, like here, like, one of the participant says, "Most people would like coding with AI friends because one of the hardest parts of your project is when you start, you run into, into a wall because you're out
- 9:33
of ideas." So the AI friend helped with that. It also allowed them [REDACTED:age] express and elaborate their ideas in code. So if they had an idea for a game, like, "I wanna make, uh, the bear kind of jump over the hedgehog," but they didn't know how [REDACTED:age] do it, it would kind of help them, um, find the,
- 9:52
the right code constructs [REDACTED:age] do it. And more importantly, it supported their creative coding iden-identity. So it wasn't a bot that was making all the coding. They were doing it.
- 10:03
The bot was just helping them when they were stuck. So this was very, very important. It encouraged kids and parents [REDACTED:age] work together, which is not always easy, right?
- 10:13
Like, uh, one of the things I discovered, I've been working with kids and families for long time now, since two thousand fifteen. Um, it's not always easy. So actually having like a third moderator [REDACTED:age] be like, "Oh, what does mommy say?
- 10:25
What does daddy think? Uh, take turns. Try this," really help with family joint engagement. Also, it doesn't always work, right? Sometimes it's too distracting, and it was very important [REDACTED:age] enable families [REDACTED:age] shut it off.
- 10:38
Maybe they wanna do the game alone. They wanna do the coding alone, so they could stop it whenever they wanted. If you have multiple siblings that fight over the laptop, it doesn't really...
- 10:48
It cannot help with that. Uh, or if the compli-- if the concepts were too complex, it was not able [REDACTED:age] scaffold it always, like break it down, so parents were very helpful [REDACTED:age], [REDACTED:age] help.
- 10:59
So after understanding, like, what are the core things that families want from co-creating and learning how [REDACTED:age] program with an AI friend, I went and basically evaluated all the generative AI models [REDACTED:age] see if they could do that, right?
- 11:13
So Scratch, for Scratch, like, top generative AI models are pretty good at generating explanations, giving, like, ideas or questions [REDACTED:age] help, uh, kids and parents, like, explore, like, and test, like, new games.
- 11:27
And this was published. We created a benchmark as well for, for measuring this.
- 11:33
And this is just an example of what the future of education with multimodal AI could look like. Um, if it's applied [REDACTED:age] Minecraft, [REDACTED:age] games, [REDACTED:age] physics simulations, science simulations, it can become a creative sidekick, right?
- 11:47
There are a lot of people who love [REDACTED:age] build things with their hands. What if I could get ideas, like, by taking pictures of flowers I like and colors I like, and it gives me ideas and helps me, like, generate 3D models and that I could afterwards print and paint?
- 12:03
Um, or I'm into knitting, and I wanna use a generative AI model [REDACTED:age] inspire my knitting projects. It can also be a learning companion and a coach. It can help with math.
- 12:13
So together with Nancy Otero, we created the first benchmark for math misconceptions [REDACTED:age] show what are the most common math problems that kids have in K through 12 and evaluate how good are top of art generative AI models in identifying these misconceptions when kids talk with a chatbot.
- 12:32
And I put a link [REDACTED:age] it if you wanna download it. So I am here [REDACTED:age] invite you [REDACTED:age] think about AI engineering and AI tinkering for all ages, and how do we go from my experiments [REDACTED:age] Cognimates [REDACTED:age] things that people are doing and tinkering with Hugging Face and make sure, like, we open up the space
- 12:50
so we use AI not just [REDACTED:age] teach, but we actually use AI for people [REDACTED:age] learn how [REDACTED:age] tinker and learn by playing and learn by doing. So I like [REDACTED:age] do what I preach, and I'm gonna show what I tinkered with AI last night.
- 13:05
This is-- These are very fresh demos. So this is using the latest, um, Gemini API,
- 13:13
and I have three demos. Let's hope they work. Uh, let's start with the science one.
- 13:27
And I was hoping [REDACTED:age] draw in real time, but I don't have a table, so luckily I have some, some drawings, and we'll see, we'll see how well this works.
- 13:38
So I have a drawing. Oops. A scale with a weight on each side. What would happen if you add another five kilograms?
- 13:55
So it's asking me questions based off on my drawings, and then I can make a new drawing that has, like, ten kilograms and ten kilograms and see if that gets better.
- 14:03
Um, let's try another one. Water and CO2, what happens if it gets mixed? Oh, I need [REDACTED:age]... So imagine I have a webcam, and I'm like a table, and I'm drawing in real time, and we could play with it, but it's very interactive.
- 14:24
Let's try this one. The Earth is being hit by something. [laughs] Hopefully not.
- 14:34
Let's, let's add one more arrow and see what happens if we do that.
- 14:47
Oh. [laughs] Ah. [laughs] That was fun. So it finally understood it was the moon. Let's play with the math one.
- 14:56
And... Solve the
- 15:20
expression inside the parenthesis. Okay, so I have one where I did.
- 15:34
Okay. Solve the multiplication with the parenthesis. And let's assume I've done that too, and I have the next question.
- 15:45
I need a better background for this demo, that's for sure. [laughs] Uh, the first step is [REDACTED:age] simplify. No, no, no, go back. Okay, so any number divided by itself equals one.
- 15:54
But you see, it doesn't give me the answer. It just give me a question so I can keep trying, right? And learning. Um, let's try one more, more complicated.
- 16:06
So okay, four on the axis. Oh, I was hoping it would give me a better question. Yeah, so the last one is the one that is encouraging curiosity. So this one...
- 16:35
What is the lady doing? Okay. Uh, what are the colors on the flag? What shape is the star?
- 16:46
Oh, it asked me things about Jordan. Um, let's see what it does with apple.
- 16:55
It has... Can you-- Do you know what apple this is?
- 17:04
Uh, let's see. What does the apple smell like?
- 17:10
Uh, had a nice origami thing as well.
- 17:21
Okay. I don't know where the origami went, but...
- 17:27
So you sort of get the gist. Um, these, these are like... I don't know if... Do you want me [REDACTED:age] draw something, or do you want [REDACTED:age] ask one of a question of the science or math or objects?
- 17:37
Any, any requests from the audience? Don't be shy.
- 17:50
Yeah? What, what should we ask? No? Okay. Well, uh- You
- 17:59
gotta ask. [laughs] Can you ask it [REDACTED:age] solve a system of equations? Excuse me? A system of equations. A system of equation. Yeah. Can you tell me what [REDACTED:age] write?
- 18:09
Sure. Uh, two X plus seven- Two X plus seven-
- 18:15
Equals two ... equals two. Thank you. Let's try it.
- 18:32
Let's see if it does well with my, uh...
- 18:36
Subtract seven from both sides of the equation. Mm, not bad. And now if I do that... [laughs] [laughs]
- 18:57
Divide both sides by two and so on and so forth. Um, now, the cool thing about this, like, I made the code open source and template so you can play with it too.
- 19:06
It's less than a hundred lines. You just need [REDACTED:age] create an API key, which is free, and you can create your own instructions. And hopefully, I inspired you [REDACTED:age] think, like, beyond of chatbot interfaces and delegating instructions and delegating, like, questions and think more in, like, a tinkerer and think about how we could put these tools in
- 19:30
the hands of young people because they are the future, and they need [REDACTED:age] learn about this technology as well and how it works. Um, I think that's my time.
- 19:38
Uh, all my research is on my website, and I put a QR link for that as well, and I look forward [REDACTED:age] your questions afterwards. Thank you so much. [outro music]