Junping Yi
English中文
Junping Yi

Hi, I'm Junping Yi.

Over the past few years, my work and research have been primarily focused on Artificial Intelligence:

Back in 2015, I enrolled in an MIT computer science course on edX. I was still a regular high school student without my own laptop, so I spent my lunch breaks writing code and doing assignments on my phone. One assignment was particularly tough, and I was running solutions through my head even while eating. When I finally got the code to compile and run correctly on that tiny screen, the thrill was unbelievable.

For a regular high schooler to teach himself a hardcore curriculum from halfway across the world on a phone—and get better feedback and fulfillment than in a traditional classroom—the stark contrast made me question how we actually teach. This became a question that would stick with me for years.

Scientifically, teaching systems should align with how our brains work. Yet reality is still dominated by rote memorization, force-feeding, and bad textbooks. I spent a lot of time looking into different educational models: from Montessori and Jean Piaget’s Constructivism, to Seymour Papert’s Constructionism and the Flipped Classroom, all the way to How People Learn. These were all great ideas, but they often felt like just concepts. They didn't really convince me.

I didn't find my answer until I read Stanislas Dehaene’s How We Learn. One line resonated deeply: "the brain learns only if it perceives a gap between what it predicts and what it receives." This gave me a clear scientific footing: all educational methods eventually come down to how the brain works. Endlessly debugging code on my phone just to get it to run was a perfect example of this "prediction error" in action.

But the reality of education is messier. Seeing the unequal access to resources, I used to think the internet was the ultimate fix. But Neil Postman’s The End of Education posed a much more fundamental question: Why do we learn at all? The truth is, no matter how a country packages its educational system, its bottom line is almost always to "produce a workforce for society."

A trip to Cambodia made this incredibly obvious. One night the power went out, and I realized the building next door was a school. In the pitch black, kids were studying by the faint glow of candles. The next day, I asked my tour guide how he picked up so many languages. He told me their schools don't teach science in the senior years. They strictly learn languages—English, French, Spanish, Chinese—simply because that’s what the local tourism industry demands.

It made me realize that just giving people better online resources doesn't automatically make them want to learn. One-way info dumping doesn't solve the core problem of motivation.

Eventually, I found that I mostly just care about solving the first problem: the cognitive challenge.

So, what does someone who is deeply interested in Cognitive Science, and also understands Computer Science, do next?

Artificial Intelligence, naturally. I've spent the last few years experimenting with combining AI and education, and Generative AI has shown incredible potential. I plan to keep exploring this space to see if I can find some real breakthroughs.

Before this, I spent nearly a decade working in EdTech companies, doing everything from product to growth. Eventually, I decided to try something different. I quit my job, became a digital nomad, and traveled across China to see the country firsthand. During that time, I shifted my focus entirely to AI and independent exploration, writing several indie apps just for fun.

If you want to connect, don't leave it for "whenever." Now is the best time.