Hi, everyone. Welcome back to another episode of Grasp Talk. Today, we are excited to have Bojena Payonk with us. Bojena is a seasoned expert in language learning and cognitive science, currently serving as the VP of learning at Duolingo. With nearly a decade at Duolingo, she has held many roles, including director and staff learning scientist, shaping the learning experiences for millions worldwide. Beyond Duolingo, Bojena holds a PhD in linguistics

from UC San Diego and is a mentor at Texas and serves on the cognitive sciences advisory board at UC San Diego, reflecting a deep commitment to advancing education and language research. Today, we will dive into her insights into the science of language learning and her vision for the future of education technology. And thank you for joining today. Thank you for having me. Very excited to be here. Thank you. So, first of all, you've had a fascinating journey from academia to leading

the learning team at Duolingo. And so, we are interested in what inspired your passion for language learning and linguistics, first of all. Well, what inspired my passion, I think it started really when I was little and I grew up in Poland. In Poland, everyone tells kids that they need to study languages because that's really the way to improve your career prospects. In Poland, well, we speak Polish and basically nobody else outside of Poland, very few people want to learn

Polish. And so, in Poland, we need to learn other languages. So, that's something that I've heard a lot throughout my life and something I heard growing up. And so, when I was little, I just tried learning languages and I really liked it. And so, I even had this idea that every year I would start learning a new language. And I did that for some time. Of course, I didn't continue for very long. You can't, it's hard to add too many languages. But I tried that

and so I enjoyed learning languages when I was in Poland. And then I taught languages as well. And then when I ended up going to the US to do my PhD in linguistics, I ended up specializing in how people learn languages. This is something that I was doing myself, I did for many years. And so, I wanted to research this topic. I wanted to understand how this works in our minds. How do we learn and how can we make learning work better? And so, in my research, I did a lot of

experiments, teaching people many languages or just parts, small parts of a language. And then trying to figure out in what condition people learn better so that we can improve our methodology. And so, that's something I got to learn a lot about during my PhD and after when I was doing research in the academic world. Wow, so fascinating. But was your research, a PhD research and postdoc research related to what you do today at Duolingo or is it different

or similar? It's very related. So, what I did in my research, I really learned a lot about how people learn languages, how we should be teaching. And then I basically decided to leave academia and I got this Duolingo job, which itself I can tell more of the story how I got it. But the job essentially meant that I was using everything I learned before. All my experience doing research, understanding how we learn, my experience learning languages on my own,

my experience teaching languages, it all really started coming together in my job at Duolingo, where I've been able to apply it and build a product that teaches languages. And I've been trying to apply all my knowledge on this topic. Wow. But you were the research associate and lecturer at the time, right, at Northwestern University, and then you joined Duolingo in 2015. But Duolingo started in 2011 and 2012, around that time. And did you know Duolingo at the time?

I'm curious, how did you join Duolingo? Yeah, so I like to say that it was written in the stars because it just all felt a pretty incredible experience. So yes, I was working at Northwestern University. I was a researcher. I was a lecturer. And I really thought that I would have this academic career, that I would stay at a university. But I was having doubts whether I actually wanted this kind of job. I really liked research, but I felt like I really wanted something

where I can feel the immediate impact of my work. And I wanted to do work that moved a little faster. Research, unfortunately, goes very slowly. It's very hard to move quickly because it takes time to run studies, to write papers. And so for me, it was a little bit frustrating. And so I was thinking about what else I could do that would really use my expertise. And I had a lot of conversations with my husband about it at the time. And one day, we were talking about it,

and he asked me, well, if you were to work anywhere else outside of academia in whatever company you wanted, what company would that be? And Duolingo immediately came to my mind because I was using it. I knew about it. A friend recommended it to me. And so I was using it just to practice the languages I knew, learn some new languages. So I was already familiar with the product. And I thought, oh, yeah, that sounds like a company where I could actually use

my expertise. And so in that conversation, my husband and I just checked Duolingo's website, and we saw what kinds of jobs they were hiring for. And then there was that job basically written for me. So at the time, so yes, that was 2015, Duolingo was looking for someone with expertise in learning and teaching. At the time, they didn't really have any experts with this kind of skill set. So they wanted someone with a PhD in linguistics or a related field, someone with

experience teaching languages. So basically, everything was right for all the requirements. And so I decided that I just need to apply for that job. And so I sent my application, I think the same day. I heard back the next day. And I was invited to go to Pittsburgh, to the office. And then at the end of my interview, Luis Vona, the CEO, basically told me, well, we like you. We want to hire you. So that day, yeah. I mean, then we had more follow-up

conversations. But yes, it was a fast process at the time. At the time, Duolingo was pretty small. It was still a small startup. I think we were maybe about 45 people when I started. So things moved faster in terms of hiring as well. But yeah, so I didn't even have that much time to think about it. I decided that I need to take this opportunity. And I moved to Pittsburgh, and no regrets. Wow. So do you remember your first impression on the Dewey's and the team

at the time? Or was there a duo at the time? Sorry, no question. Yeah, yeah. I mean, so the office was, we switched offices as we grew. So back then, it was a pretty small office. I remember that everyone was very friendly. I it just felt immediately so like such a nice community. I remember that I spent a whole day there and we all had lunch together. That's when I learned that a Duolingo every day everyone had

had lunch together sitting at the same table and there were just great conversations and we still do this to this day. Duolingo there is an hour where everyone just has lunch and we want people to to be able to have those conversations. So it felt like a like a great place very very friendly and very creative. People were very curious, very interested, constantly trying to to solve some problems and come up with something new.

So it's I immediately loved the whole the whole company, the environment, the culture because it felt like people there were a lot of smart people and everyone wanted to move really quickly to solve problems to build something useful and that that was very appealing to me. Yeah really interesting and you mentioned when you joined Duolingo so there was the team was around like 45 people, 45 members but there were like other learning scientists as well or you

were the only one? No no I was the only one I was the only one so Duolingo you know for those first few years didn't have any learning scientists. Oh I see and what is a team then so there are many engineers mainly? Yeah there were mostly there was mostly engineers, there were some product designers, there was one person who actually a couple of people who were more like machine learning engineers, there was one product manager yeah but mostly mostly engineers.

So meaning you needed to set up the how you work and then how you collect data and to analyze data and set up and what I was I'm curious what what you did at the beginning and yeah. Yeah good question actually took me a lot of a long time to to really figure out what I should be doing a long time to to really figure out what I should be doing because the company existed already for a few years and so they had their own way of doing things and they didn't have anyone in in my kind

of role a learning scientist so so they they hired me because they they felt like they they should have someone with this kind of expertise but when I arrived they didn't quite know what to do with me and so I had to I had to figure out where I could I could actually be useful and so I did I did different things I at the beginning I just I just tried to learn a lot of different things about what what was happening there some of the work was just improving our courses and so initially I was

even going into some of the courses and actually rewriting the content and trying to run some experiments with tweaking the content and and changing changing how we were teaching certain things and testing it through our the framework that the company was already using of A-B testing so I didn't have to set that up that was already something we we had but I I was able to to set up my own my own experiments that were more about the the content

of our courses so the sequence in which we were teaching certain things I also did studies that were more kind of academic style trying to evaluate the effectiveness of of our courses and so actually recruiting people from our learners from Duolingo users and asking them for example to take take a test in a language take like a proficiency test to see how much they've learned and and so that also gave us some insights about how well

our courses were working and that that is something we continue doing to this day actually both things those experiments with modifying improving the course content and evaluating the effectiveness of our courses now we have a whole team of learning scientists who who do just that they just run research studies and so initially there was a lot of a lot of that and also collaborating with others on just coming up with new things that we wanted to build so

working with with the product designers with engineers thinking about okay what what should our lessons look like what what kinds of exercises should we have what what other types of experiences should we be building to to make sure that we teach effectively and just generally I was there to to provide guidance on what is it that how how do people learn languages what are what are the different pieces that are important and what are the things

that we already cover that we do well and what are the gaps and so I was there to really provide a framework for what what are the things that we should be focusing on to teach better see since since you have academic background and I I was curious to know like how did you balance like a scientific rigor with the practical aspect of building a widely used language learning product I mean do you I mean when in choice when you have choice oh should we

did you follow your science how to say backed a path or like if the metrics is correct if the user preferred that way I mean scientifically it's wrong but user prefer this way and to choose that way I was curious how to balance that I'm sure there has ever been like a like a tension of this type exactly I would say it's always been both trying to apply the knowledge I have about the research what we know about about learning about teaching

but then also looking at our experiments and a lot of a lot of what we are building a duolingo still we're trying to apply research but it's not always clear how to apply it because the research generally is done in different settings so maybe there are research studies done in like the types of experiments I did where I had students come into the lab and they and they learned you know they didn't learn in an app they just had some lessons that they

they went through or research is done in the classroom and people evaluate certain methods looking at how students learn in a classroom setting what we're building a duolingo it's it's different it's an app and it's an app that people can quit at any point so we also can't force people to go go through the experience like you know when I when I was setting up my experiments people well people came in and they had to finish it and or at least

you know they were they were paid for it so so they finished it and in the classroom it's kind of hard to quit you're there so so a duolingo we really had to and still have to innovate in in just how we even apply research findings and and be very creative about it so we we might know for example that you know something works but how do we actually how do we actually implement it so so there are many just to give you some some concrete examples what really works the best

for us is try to apply research that's more about the general principles around learning like we know for example that people learn well when when they practice through recall or retrieval so instead of reviewing things by just just kind of looking at let's say your notes or a textbook you try to bring out things from your memory try to remember on your own kind of like test yourself like I know okay well how how do I say cat in Spanish and I try to remember it on my

own oh gato so that's really the most effective way to to practice to review that's that's just known from research it's been very well studied and so we need to come up with our own way of implementing this kind of recall in the app and so we need to come up with exercises that actually help learners bring things out of their memory and so we might give you exercises where you need to um we give you a sentence let's say in English and when you're learning Spanish and we

tell you okay say this sentence in Spanish but you you kind of have to formulate it on your own. But that exact exercise, the way we implemented on Duolingo, you know, it doesn't exactly exist elsewhere. We had to figure out how to apply it in a way that would be intuitive to people to do. And so that's what's useful for us when we apply those more general principles, more general findings about learning. It's much harder for us to apply some research findings

that are specifically about, let's say in a classroom, okay, this specific activity maybe works well. Because then we, well, we don't know, well, is it about the environment in which people are learning? Is it about the teacher? It's not clear how exactly to apply something this specific. So I would say we're always just taking the research that exists and then combining it with the insights we have from implementing it in our app,

seeing how learners react to it and learning a lot through that and figuring out how to motivate people to actually do the kinds of exercises that are well-supported by research. I see. But in that sense, do you read academic papers nowadays? And then also in that sense, I think a lot of researchers at universities or research institutions want to collaborate with Duolingo to apply their research results to see if it works in the real world or not.

And does that happen? Oh, yeah, yeah. So yes, we, well, I try to read research papers, my whole team. We try to stay up to date on what the research is. So we go to conferences and we try to make sure that we just know what the developments are so that we can apply any new findings. We also try to stay in touch with the research community in different ways. For example, we invite researchers, professors to our colloquium series.

So they come and give a talk and we can have meetings with them. So that's also a great way for us to see what research is getting done and then interact a little bit with people to see what ideas they have, what, give them some insights about how it's working on our side, how it, what happens when you try to apply those findings. People reach out to us to collaborate and we collaborate sometimes. So we even offer some research grants

to, on specific projects. So researchers have done this and they often study people learning on Duolingo. We can't unfortunately support so many collaborations. So we collaborate some, but we definitely get a lot more requests for collaboration than we can support. But how do you choose? So the collaboration, yeah. Any criteria? So usually what we choose to do is through research grants. So we might have like a specific project in mind.

For example, we want to evaluate how well Duolingo teaches, let's say, speaking skills. And so we might then create a call for proposals like within this topic, the researchers can propose their ideas, how they would assess this. And then we evaluate those proposals. So it's basically like other research grant applications. We look at the team, we look at the research plan they propose, and then we pick the strongest one.

So that's generally what we have done. I see. And if you can choose any institute or if you can choose any project or research, so what would you like to collaborate or research? Do you have any, yeah, did you get my question? Yeah, like anything I would collaborate on. Ah, there are so many interesting topics. Yeah, I don't know if I have like a favorite one. I mean, something that's definitely on my mind a lot

is how well learning on Duolingo applies to the real world. Something we think a lot about, we want people to learn a language on Duolingo and then actually use that language somewhere in some other context. And so that's something that's very interesting to us, like a study that evaluates how Duolingo learners can use their languages in different real-life contexts. Interesting, but I guess you already did

some kind of research around that, right? Not yet? On this topic? Yeah, on that topic, yeah. We haven't done that much, a little bit, a little bit. See, okay, maybe our audience will reach out to you. Hey, yeah, we would like to collaborate with you. So yeah, I hope. I'm curious, what was the core metrics or KPI before you joined Duolingo? What were they pursuing and how it's changed after you joined? So before I joined and when I joined,

mainly we were, it was very simple. We were really just paying attention to daily active users. That was really the main metric. And then after I joined things, I mean, we generally expanded the types of metrics that we've been looking at. So definitely a big one has been looking much more at retention. So how many people come back the next day and a week later, two weeks later? And that is something that really tells us a lot

about just the quality of the experience. Is it good? Is it intuitive? Also, is it just not too frustrating, not too difficult? So whenever we make changes to improve how we teach, we often see big gains in retention because learners can actually progress more easily through our courses. They don't get stuck, which can get frustrating if you're learning a language and then it becomes suddenly too hard. And so those are the kinds of things

we've been trying to improve. We also started adding a lot of other kinds of metrics that we track, like for example, time spent learning. We want to make sure that people spend time in the app, but they actually mostly spend time on the educational pieces of it. So actually in lessons, learning things, we also later added time spent learning well. So you can be learning, but there's also really the most effective way

of going through the content, which is really going down the Duolingo path instead of maybe doing some other quests or going back and review the material. So we want to also optimize time spent learning well, how much time people are spending really on the next thing that's on their path. We also look at, for example, different subscores. For example, time spent speaking, time spent listening. We want to make sure that people are spending enough time

on the different parts of language that we're trying to teach them. We look at content difficulty. So we want to make sure that what we serve to our learners is at the appropriate level of challenge. Duolingo personalizes the experience. And so in general, that's something we pay a lot of attention to. We want to make sure that what we give you as a learner is at the right level. So if you're struggling, we'll give you a little bit easier exercises

if you're answering everything correctly, we'll give you slightly harder exercises. And overall, we track the difficulty of the content. And on average, we want to make sure that we are pushing people to do more difficult things with the changes that we're making. Um, so those are just some examples. I see interesting. And then, by the way, I, I, I like the mascot duo and he or she sent me like an interesting, funny, he, he, he sent me funny notification.

It's time to Spanish or Banish and it's something like that. And that's so funny. And do you think that mascot duo, uh, influence impacted on, on the metrics or, and I'm curious how much that mascot impacted on the metrics and retention and activation? Yeah, that's a, that's a good question. I mean, we think that, uh, the duo, uh, has a big impact. Um, initially, you know, we didn't, we didn't really know how much of an impact he could have,

but it was just a, a little design, a little owl. It was an owl because education owl is kind of a symbol of wisdom education. Uh, so we, we picked an owl, uh, but we didn't really know how much it would develop. Uh, but gradually we, we started leaning more into it, um, duo appearing in different places, uh, in the app, uh, and people just really reacted very positively to duo. Uh, and so we, uh, we started doing much, much more with him, uh, on social media, uh,

on TikTok and then duo sending different notifications. Uh, so at some point, uh, Duolingo became, uh, famous for passive aggressive notifications. Like, oh, you know, these, um, these reminders don't seem to be working. Uh, so we'll stop sending them for now. Uh, and, uh, people really liked that it felt, it felt funny to them. Uh, it felt passive aggressive, but the people actually came back after those types of notifications. Um, so, uh, so we started playing

much more with what else duo can do. And now, yes, now, now we have, well, duo has been on red carpet, uh, in Hollywood. He's been in many different places and, uh, and yeah, so we're, we're just continuing to expand, uh, what he does. But duo was dead, right? Back in February, I saw the Twitter post duo was dead, but is he alive? Did he come back? He's alive. So it turns out that he, he faked his own death. Um, so if you go to, yeah. Yeah. If you go to Pittsburgh,

I can meet you. Yeah. Yeah. He's there. Definitely. Okay. But was duo exist? Yeah. But did duo exist when you joined? Yeah. Yeah. Duo existed. It's just, uh, it was a much smaller part of the app, but Duolingo always had, uh, or since very early on, it had the little icon of, of duo, the owl, and he was there in the lessons, but it wasn't nearly as prominent as it is now, especially with, uh, the whole, the whole kind of, uh, costume of a duo that, that started later.

I see. And as a research perspective, I was, I'm curious, you know, how much that kind of mascot impact on the, on the people's learning or language learning it does. Did someone do this research? That's a, that's a great idea for research. Actually. I, I don't think, well, we haven't done, uh, this kind of study and I I'm not sure anyone else has. Um, but yeah, that's, that's interesting. I mean, we do know that, um, people, people really like duo. Uh, and so that's,

that's part of why they like the app and they come back and they, um, they want to engage with, with the exercises because duo is there and, uh, cheering them up. So he's very supportive. And I mean, sometimes he's, uh, he's a little mean, so he has a lot of different emotions. So, so we know that people, people really enjoy that. Um, and, and that's partly at least why they're coming back because it's, it's part of this whole fun experience, but we haven't

studied it in a, in a rigorous way. Maybe we should apply to a during a grant program, I think. So, uh, yeah, then what, uh, you know, during, during the 10, almost 10 years, a decade experience. And did you find any surprising findings from your research or product development, uh, at Duolingo? I mean, you expect, uh, I mean, let's say you expect something, but it turns a different results or something like that.

Yeah. I would say for me, the most surprising things have been just around learner motivation, because we've had a lot of ideas and even knowing that certain things, you know, should be effective and we should implement this. And then, um, maybe we would implement some of those things, um, but learners weren't engaging with it. And so our, our engagement metrics, like the active users or retention sometimes were down. Um, and,

and we often would realize, oh, well, we implemented something that was just too difficult. And to me, that was, uh, that was surprising at the beginning that it mattered so much, um, that people may, maybe retrospect, that's not so surprising. Um, people don't like to do hard things. Um, but that's, that's definitely, that, that was something that surprised me at the beginning that, oh, people really didn't want to do something that's a little bit harder.

Some people do, but the majority, um, didn't, that maybe they didn't want to come back, uh, as often when, when we made lessons more difficult and when we included more exercises, for example, with, uh, the recall I mentioned, uh, where we want people to, to actually recall information on, on their own. And those types of exercises are, are hard. They are kind of, they can be unpleasant because you need to think very hard. Um, and, and, and some of those

experiments, uh, with these types of exercises, well, they would just hurt, um, our engagement metrics. Uh, so yeah, that was, that was surprising to me at the beginning. Now I feel like it's, uh, I've learned over time what, what to expect. Um, but initially that was definitely a learning. Interesting. And there's a daily streak, the numbers and users can see, and we were talking before starting the conversation, but how, what, do you know, what's the longest daily streak

that user has? I mean, it's been started in 2011, 2012 means it's been 12 or 13 years, right? Does someone have, I don't know, over 10,000 days or more? I don't know. Yeah. That's a good question. I don't know. Yeah. I don't actually know what's the longest streak, but people definitely have, you know, at least over a thousand, uh, maybe even a couple of thousand. So some people have been on it for a long time, but yeah, I don't, I don't know what the longest

streak is. And I think you should ask the CEO, the founder, right? So what's your streak? What's his streak? Yes. He, he uses Duolingo every day. So I don't know if he, maybe he doesn't like any day. Yeah. That's, I would not be surprised. He uses it for multiple languages and now also, um, learning math, music, chess, other subjects that we started adding. Did you see Luis speaking 10 languages or so? Because if he learns, you know, every day,

some languages must be, must be able to speak many languages. He does. He does. And he's been, um, he's been focusing a lot on, um, actually for many years on French and he only studied French on Duolingo. And that's, it's actually pretty amazing. He, um, he's, he said he traveled to France. He, he, he was able to communicate. Um, so he's, he's, he's able to watch movies in French. Um, so yeah, that's, that's a language that he's been focusing on a lot, but he's,

he's studied others, um, often testing things like Japanese, as we were trying to improve that course. Um, we're trying to learn Portuguese or Swedish. He's, I think, German. He's trying German these days. So he, he tries to test different courses. Amazing. And since 2022, ChachiPT came out, and I mean AI, Eraser Engaged Models came out. And I'm curious how AI impacted on education or language learning. Did it change completely, or did it improve how people learn?

I'm curious, yeah, the impact of AI. Well, definitely impact has already been huge. And generally on education, I mean, still, I think we're all still figuring out how to really use it in the best possible ways in general for education. What does it mean for classrooms and so on? But I can tell you that at Duolingo, we've been really trying to take full advantage of generative AI, and it's been transformative.

So for example, we immediately started using generative AI in two different ways to improve our product. So one way was to help us generate content. So before, all the content was written manually, and it was taking a long time to build new content, to improve content. And we're constantly trying to improve our courses. We want to refresh the content. We want to build more advanced content. And that was just taking a long time.

And we have a lot of courses that we just haven't been able to improve in many years. And since generative AI came out, we've been setting up just new processes to now generate content instead of writing it manually. And then, of course, we have a lot of checks to make sure that the content follows our guidelines and is of good quality. But all of it is done through AI. It's just so much faster. So it's allowing us to really create a lot more content.

So for example, a month ago or so, we released 148 new courses, which we created in the past year. And it was thanks to AI. Before, it would have taken us multiple decades to create this amount of content. And so that's something that's helping us a lot. So we can move much faster in our improvements and just adding more courses. And then another thing where generative AI is helping, it's allowing us to build just new functionality,

new features that we just couldn't offer before. So for language learning, something that's very important is actually speaking, having a conversation. And before, the technology wasn't there to really have this kind of conversation. And now, it's there. And so we started developing immediately a feature where you can have a conversation with a bot. So maybe you've seen a video called With Lily, where you talk with this teenager with purple hair.

She's kind of unimpressed by anything you do. And so we've created this feature. We continue improving it. But it's already been great to give our learners practice having a conversation at different levels of proficiency. So we try to make it appropriate for you, regardless of where you are in your journey. But it's particularly useful for people who are a little bit more intermediate, more advanced, to actually practice talking.

So that's something that generative AI has enabled us to do. And so I feel like, in general, when we think about education, generative AI has a lot of potential. It's just a matter of finding the right ways to use it. Like for languages, it's, like I said, for Duolingo, it's been great for content generation. It's been great for all those conversational features. I think generative AI is just going to be really

great for personalization. So that's something that I think will be the future, where everything will be more and more personalized, because we'll be able to adjust how we teach much more to each learner. Because right now, or before, having a teacher with a large classroom, it's much harder to personalize the instruction for every single person. But with this kind of technology, I think we'll be able to create content,

to create lessons for each person much more easily. And so that's something I'm very excited about. Yeah, totally. Yeah. I totally agree with the AI input, like the personalization, and so that people can learn better. Because learning by doing is a better way to learn something effectively. That's what I know, what I understand. And I'm not a scientist, but that's what I know. And yeah, AI is AI. People can speak with a lady, and so that they

can practice and more. And I think it's a better way to apply what they learn to actual situation. I think it's similar environment, I think. And another question is, in 2016, you published which countries study which languages, and what can we learn from it? And I love the blog. But it's fascinating to see how history, immigration, geography, and culture influence which languages, I mean, different countries choose to learn.

But do you think AI will change this? I mean, the language people choose to learn, eventually? Because with AI, eventually, do we really need to learn language? That's the ultimate question, I think. Yeah, that's a good question. Maybe it will change it. But what we see is that, at least when we look at people who learn languages on Duolingo, people learn for two reasons. One big bucket is people just learning as a hobby.

They are just trying to do something productive with their time. And maybe they have some, maybe they live in the US, and they have some heritage. Maybe their grandparents are Italian, so they're just trying to learn Italian. But maybe they want to speak a little bit. But a lot of it is just trying to connect with some cultures better, and to spend your time productively, just do something that's enriching.

And that's just not going to change. People don't, people in those buckets, they don't really need to learn a language. It's just a way for them to improve themselves. And so we think that that's not going to go away. People will still want that, to really connect in the same way. And then another big bucket is people learning English. And I was in that situation growing up in Poland, having to learn English.

That was one of the languages that I knew was very important. I think both of you learn English. For many people who live in countries where English is not spoken, English is a language where it's important for career development. It's just a language that you want to actually speak, or at least understand. And so even if translation works really well, that might not be enough for English. Would we be doing this podcast through some kind

of translation? It would be much harder. There's always a lag with translation. You can't actually that easily use the language, have a conversation, do business, interact with people. And with English, well, it has become this global language. And it seems like that's also not going away anytime soon. So generally, I might change it a little bit. But I think a lot of this need to learn languages will stay the same.

Yeah, totally, yes. Yeah, I totally agree with that. And so about the future, so what's the future vision? And what's next for you? And I'm curious, would you keep working at Duolingo maybe? But do you have some future vision for your career or life and so on? I definitely. happy to continue working at Duolingo. I feel like, especially now with AI, things are changing so quickly. It's all very exciting. I think there's a lot of potential

to really do what we're trying to do, provide education for people. So I think I would like to be part of that. And so I definitely hope I'll continue doing that. But I'm also at this stage in my career where I'm excited to give back and to really help others. That's why I started advising other startups, just to pass on some of the learnings I've had and to help others. So that's also something I hope I'll continue doing more of

because it's also very, very satisfying for me to see others succeed. Thank you, yeah, I love that. And yeah, in that sense, you are the mentor at Texas and also advisory board at UC San Diego, right? What, as a learning scientist, what do you do, let's say, for Texas as a mentor, do you advise startups from the learning perspective? Yeah, from the learning perspective or just advising from anything. I've been at Duolingo for a long time,

so I have a lot of experience generally thinking about product development, how to build products that are intuitive, motivating for users. And so that's something I also just advise the startups on, just kind of generally giving them this perspective on building intuitive products. And do you publish those learnings through website or newsletter, Devlog or X? So do you do like media? So to tell your lessons to the people?

Sorry, can you repeat the question? Oh yeah, just do you publish your learning through website, newsletter? Wow. Well, I try to post on LinkedIn a lot of my learning, so that's what I try to do. I actually write some things and post there. Maybe one day I'll write in some other way. Maybe I'll write a book one day. Oh yeah, I would like to read it. And you're just personal learning, so you have been learning a lot of things,

not only language, but also business, product development, so yeah, as a mentor, where do you keep those notes or learnings? Like do you write on like paper book, yeah, paper note or do you use Apple? No, on my computer, on my computer. So I just use Google Drive and I just use some electronic notes. Interesting, Google notes, but this is gonna be, yeah, huge notes, right? So if you keep adding, right? Yeah, if I keep adding, yes.

Okay. Is that how you learn something new? I mean, besides language? Adding notes to somewhere and review like once a week or so. Sure, that's how I learn. That's, I think, how I process information and that's how I, often when I write and I rewrite and restructure, that helps me think and helps me clarify my own thinking about things. It's not exactly learning new things, but I think it's helping me maybe getting deeper insights about some topic.

Thank you. So I wanna ask you like this question and about advice and to someone who started learning something, not only language, but something new and what's your advice to them from the learning scientist standpoint and also like a Duolingo master perspective? Well- What can be done better? Yeah. How can we- Definitely, if you're learning, if you're trying to learn a language, I would say the most important thing

is to make sure you stay consistent. You actually do it every day or almost every day. That's the most important thing. What exactly you do every day is less important. As long as you do something very regularly and it doesn't even have to be that much time, after many days, you will actually learn a lot. So that's the most important thing. And then there are, of course it matters exactly what you do, how much time you spend on different things.

But if you're not consistent, it's just not, you're not going to learn anything. So I would say that's my main advice. Do you use time spent learning? Yeah, for your learning as well? Sorry, sorry. Yeah, do you use time spent learning well for your learning as well? Yeah, for my own learning. Yeah, I try to, definitely. Okay, thanks. Yeah, compounding. Oh, yes, compounding. Yeah, yeah, yeah. I think we're over time.

Yeah, so the last question, sorry. And so about the legacy. So since Grasp is a platform where people share what they're reading, learning, and we want to ask this question, and what impact or legacy you want to leave behind for future generations? I think what I care about is something I've talked about earlier. I'm really passionate about educating people. I want to make sure people really have opportunities

to learn new things. I grew up in a country where education gave me a lot of new opportunities. And so I hope that everyone can have access to those kinds of opportunities, and they can learn, and really realize their potential. So that's what I'm hoping I'm doing with my work at Duolingo. I'm really helping create those opportunities so that people can succeed, regardless of the background they come from, the country they're from.

If they have access to good education, they'll be able to do a lot. Beautiful, yeah. And thank you. And thank you so much for joining today. Yeah. Thank you very much. This was a lot of fun.