Adapting for scale: Experimental Evidence on Technology-aided Instruction in India

Many interventions that “work” in small-scale trials often fail when scaled. This highlights the need to adapt promising interventions for scalability by addressing constraints that bind at larger scales. We do so in the context 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 0.22σ higher in Mathematics and 0.20σ 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 and its experimental validation have informed scale-ups now reaching over 250,000 students.