The Mix-Up Machine: Helping Kids Discover What They Love to Learn

Every edtech company is trying to solve motivation. Most reach for extrinsic levers like grades, badges, or streaks. Outschool's bet is different: use live teachers and a learner's natural curiosity to drive engagement. But there is a fundamental flaw in that model. When you ask a kid "what do you want to learn?" most of them shrug and say "I don't know," or they default to what adults have told them to be interested in. This is the blank-page problem, and it was quietly undermining Outschool's core value proposition. For the platform to succeed, learners needed to figure out what actually made them light up, but there was no mechanism to help them do that. This was Outschool's first attempt in several years to create an engaging experience for learners outside of the classroom.

Outschool is an online learning marketplace connecting kids with live teachers across thousands of subjects, serving around 24,000 monthly active learners at the time. I was a PM on the team responsible for the learner experience, and my first task was identifying the blank-page problem as the core product challenge to solve. Our team was small: myself, one designer, and three engineers. We had a couple of months to deliver something meaningful. Because this was a greenfield exploration, we could not spend weeks on research before showing results. We needed to validate as we went. One rule we established early was that we should never presume something engaging for adults would be engaging for kids, or vice versa. Every idea had to be tested with actual learners as fast as possible.

With the core problem identified, I faced a key choice: build a native flow inside the learner app, or find the fastest possible way to test whether the concept was even interesting to kids. Building natively would have taken weeks before we had any signal, and we had no evidence the idea would resonate. So I chose a layered prototyping approach, starting with the cheapest possible test and increasing fidelity only as confidence grew.

When we instrumented the Loveable prototype and launched it to actual learners, we found something unexpected. Kids treated the quizzes as the starting point, not a chore to complete. After discovering a new subject, the first thing most learners did was take the quiz. They were curious, not anxious. This wasn't just a qualitative observation. Once we had data from real usage, the numbers confirmed it: 80% completion rate and a 41% retake rate. Kids were choosing to do more. We also saw learners returning to the Mix-Up Machine on multiple days when they had no classes scheduled, using it as a reason to come back to the app. The realization was clear: when a quiz is framed as exploration rather than evaluation, the entire dynamic flips. Kids stop avoiding it and start seeking it out. This finding changed how we thought about discovery on Outschool. The subject match alone was enough to drive intent, and learners readily favorited classes connected to their discovered subjects even when class names were minimal.

Building on these insights, we designed and shipped the Mix-Up Machine. The experience lets learners pick two subjects they are curious about and generates a blended topic at the intersection. From there, they can explore the new subject, take exploratory quizzes to dig deeper, and favorite related classes. The whole flow is built around play rather than instruction. There is no right answer, no evaluation, just a prompt to be curious and see what happens when two ideas collide. Our designer refined the Loveable prototype to align with Outschool's design system and component library, and engineering rebuilt the feature properly in about three weeks. We launched in time for the January semester. The Mix-Up Machine gave Outschool something it had not had in years: a reason for learners to come back to the app between classes, and a mechanism that helps them articulate interests they did not know they had.

As the PM on this team, I identified the blank-page problem as the core challenge, framed it for the team, and led the strategy for how we would solve and validate it. I built the initial ChatGPT prototype myself in two days, including the heuristics for subject mixing, and ran the first informal tests with employees and their kids. I developed the rules and principles for the experience that guided subsequent iterations. I also co-designed the instrumentation strategy and collaborated with engineers to build the analytics dashboard in Metabase. Throughout the project I drove the validation cadence, deciding when we had enough signal to progress from one fidelity layer to the next, and when to commit to the full build.

As a result of the launch, we saw a steady climb in learner engagement through the January semester. Among learners who engaged with the feature, approximately 1,000 out of 24,000 monthly active users at the time, the numbers were striking.

This project worked because five people trusted each other to move fast without cutting corners on learning. Our designer built the research plan, ran the unmoderated testing sessions, evaluated the results, wrote up findings, and continued iterating on the Loveable prototype based on what we learned. She also brought the final design into alignment with Outschool's design system. Our engineers figured out an inventive process to copy the Loveable prototype directly into production code and instrument it with analytics, giving us real learner data without waiting for a full rebuild. We collaboratively built the instrumentation dashboard in Metabase so the whole team could watch the data come in together. I am genuinely grateful for this team. The designer's instinct for turning research into clear design direction kept us honest, and the engineers' willingness to find creative shortcuts meant we never had to choose between speed and real data. Shipping this in under three months with three engineers, one designer, and one PM is something I remain proud of.