brilliant.org

At Brilliant, we help our learners achieve much higher levels of ability in STEM, in less time, with more purpose and joy. Our focus is on math and coding, and the problem-solving skills those subjects imbue. We read every piece of feedback. We measure everything. Knowing how many users practice every day, and whether people solve problems of increasing difficulty over time, allows us to get the learning experience just right. This is what’s driving our exponential growth. With millions of problem tries every day, we’re rapidly increasing the pace at which we’re able to test our pedagogical ideas. As an ethos, we teach people who take pride in having a well-trained mind. Our learners ask “how far can I go?” not “what will be on the test?”. They know the future belongs to people who don’t wait for permission to set their own bar. We’ve grown on the strength of being so good that you’ll voluntarily learn each day. This is very hard to do for math. But we’re demonstrating that it’s possible. After covering all concepts of Algebra, we’ll turn to building out our coverage of Geometry, Probability, Calculus, and beyond in 2026. We will cover all of foundational algorithm design and data structures by the end of 2026. This will bridge complete beginners from their first program all the way through the end of college-level introductory computer science courses. Brilliant’s tutor, Koji, is personalized, rigorous, and built to help learners prove to themselves they can do hard things. Koji is infinitely patient, asks instead of tells, builds confidence instead of dependency, and never, ever just hands you the answer. Koji can see what’s on your screen, so we know where you’re stuck and can intervene in the ideal way. Some of how Koji does this is: User knowledge modeling . Teaching well requires having an understanding of what the learner does and doesn’t know. We’re combining techniques from intelligent tutoring systems, classic ML recommender systems, and natural language conversations to identify exactly where a learner’s misconception lies. On-the-fly visual and interactive generation . Learners get lost in a wall of text. They want a visual or a manipulable interactive, problems that exactly match the question they have, and the ability to practice on near-neighbor problems just like it. Our content has been built over many years to enable precisely this functionality . Good tutoring makes itself unnecessary. Our goal is for Koji’s actions to gradually become things the student initiates themselves, until Koji is barely needed at all. The point is to make the student feel seen while they do the thinking themselves. Every lesson has associated practice sets. These practice sets are designed to feel like low-stakes quizzes. The frequency, timing, and composition of practice sets are an area of active experimentation, to maximize effective retrieval and automaticity. In practice sets, the scaffolding falls away — you’re being tested on your independent ability to answer the questions, so there are no more visual aids or hints. For constructing personalized practice, we predict the optimal next problem X to ask, so that you are adequately prepared to answer a future question Y. This is also a generalized testing umbrella within which we test spaced repetition (especially in weak areas), mixing practice problems from different concepts (so that the learner must identify what approach to use to solve each problem, rather than just applying the same procedure to every problem), determining level of effective automaticity (fast solving speed with no mistakes), and optimal length and difficulty per set. Review sets that combine problems from preceding lessons are currently being rolled out on a course-by-course basis (we human-review everything, which is why this has a gradual rollout). To learn math well requires many, many reps. The precise amount required varies among individuals, but everyone needs practice. We design a