Brilliant.org

#iykyk

When it comes to AI-generated learning, “almost right” is catastrophically wrong. Imagine a billiards game that teaches you momentum. Again and again, you line up the perfect shot—your physics intuition is sound, your reasoning is correct—yet the ball refuses to drop. The problem isn’t your understanding. It’s that the puzzle itself is broken. A broken learning game is more than just frustrating—it’s catastrophic. It erodes confidence, reinforces misconceptions, and makes learners doubt their own understanding. When building thousands of puzzles across dozens of STEM concepts, even a small percentage of errors means multiple broken puzzles in every course—each one a potential stumbling block for learners. At Brilliant, our AI evals framework ensures that every puzzle is solvable, visually clear, and pedagogically sound. Find out how we do it in our latest blog post: https://lnkd.in/edVH9i5a (Excited about the intersection of AI, learning science, and game design? Let’s chat. We’re hiring: https://lnkd.in/erU-_jvb)

When it comes to AI-generated learning, “almost right” is catastrophically wrong. Imagine a billiards game that teaches you momentum. Again and again, you line up the perfect shot—your physics intuition is sound, your reasoning is correct—yet the ball refuses to drop. The problem isn’t your understanding. It’s that the puzzle itself is broken. A broken learning game is more than just frustrating—it’s catastrophic. It erodes confidence, reinforces misconceptions, and makes learners doubt their own understanding. When building thousands of puzzles across dozens of STEM concepts, even a small percentage of errors means multiple broken puzzles in every course—each one a potential stumbling block for learners. At Brilliant, our AI evals framework ensures that every puzzle is solvable, visually clear, and pedagogically sound. Find out how we do it in our latest blog post: https://lnkd.in/edVH9i5a (Excited about the intersection of AI, learning science, and game design? Let’s chat. We’re hiring: https://lnkd.in/erU-_jvb)

Looking good in Apple’s iPhone reveal—someone’s getting smarter every day 💪

Learning products: “Incorrect. You pressed the jump button too early.” Super Mario: 𝘍𝘢𝘭𝘭 𝘪𝘯𝘵𝘰 𝘵𝘩𝘦 𝘱𝘪𝘵 𝘢𝘯𝘥 𝘥𝘪𝘦. Since Brilliant’s early days, we’ve been chasing that same visceral learning experience – the kind where understanding clicks through doing, not reading. In more recent years, we’ve been focused on the question: Can AI accelerate how we make interactive games for learning? When ChatGPT hit the scene, there was a massive wave of AI tutoring chatbots – but we decided to stay on the sidelines. Our internal product testing convinced us that this wasn’t the way forward, even though we were impressed with the explanations they were able to give. Hallucinations were certainly a problem, but there was something more that didn’t feel right… We kept coming back to Mario. Text explanations, no matter how perfect, just aren’t our goal. We want learners to develop intuition through interaction, to build understanding through experimentation, and to have fun. We want them to 𝘧𝘦𝘦𝘭 𝘵𝘩𝘦 𝘮𝘢𝘵𝘩. But the thing about building interactive learning is: it’s expensive. Designing the core gameplay loop – the part where learning feels like flow – that’s the fun part. But then you need a thousand carefully calibrated problems across dozens of difficulty levels. Every level needs to teach something new while keeping learners engaged. That’s where AI comes in, but not in the way you might expect. Instead of using AI to explain concepts to learners, we’ve been using it to help us build better learning games, faster. The capabilities have come a long way since our first attempts with GPT-2 in 2019, to ways that R1 is surprising us with what it can do just in the last 24 hours. Here’s a peek behind the curtain with some demos of the AI workflows in action: https://lnkd.in/eQeeCGRq If this sounds like your obsession, come work alongside a team of diverse talent – multiple IMO medalists, ex-Editor-in-Chief at The Onion, Cannes Lion winner, knitwear designer for Marvel, a top 50 book of the year on Amazon writer, and a large number of PhDs and dropouts from MIT/Caltech/Stanford/Harvard/etc, to name a few. Join us: https://lnkd.in/eMry6vTD

AI is terrible at programming. And better prompts alone won’t get you there. Why? Without a clear plan, you’ll end up with a patchwork of almost-right pieces that never quite fit together. That’s why our new CS curriculum teaches what really matters: breaking down complex problems into pieces that both humans and AI can understand. Through hands-on projects like building image filters, students learn to think systematically and design verifiable solutions. Our own Farita Tasnim and Benjamin Goldsmith explain how we’re preparing the next generation of programmers for an AI-first world. Read more here: https://lnkd.in/gVAdKyu3

Stop scrolling, join Juan Pablo.

Our VP of Design Peter Cho wrote a post on the fresh new look we launched this month at Brilliant, along with a new mascot, who is in fact very, very old (13.7 billion years old, to be precise). Read on for more details.