Many interventions that "work" in small-scale trials often fail at scale, highlighting the centrality of effective scaling for realizing the promise of evidence-based policy. We study the scaling of a personalized adaptive learning (PAL) software that was highly effective in a small-scale trial. We adapt the PAL implementation for scalability by integrating it into public school schedules, and experimentally evaluate this adaptation in a more representative sample over 20 times larger than the original study. After 18 months, treated students scored 0.22[sigma] higher in Mathematics and 0.20[sigma] higher in Hindi, a 50-66% productivity increase over the control group. Learning gains were proportional to student time on the platform, providing a simple, low-cost metric for monitoring implementation quality in future scale-ups. The adaptation was cost effective, and its key design features make it widely scalable across diverse settings. [This project was executed in partnership with the Government of Rajasthan and Educational Initiatives. Additional funding provided by the Global Innovation Fund.]