The Economics of Algorithmic Personalization: Evidence from an Educational Technology Platform

Can personalized recommendations improve engagement in educational technology? We design, test, and scale a collaborative filtering system for Freadom, an English-learning app for Indian children. A randomized controlled trial (RCT) with 7,750 students shows that personalization, deployed in a single content section, increases engagement by 60% in that section and by 14% app-wide. We then exploit an eligibility threshold in a regression discontinuity design (RDD) to track effects over five months of deployment. For user cohorts receiving personalization during deployment, RDD estimates exceed RCT benchmark by a factor of 2.5, opposite of the “voltage drop" typically observed in policy scale-ups. This provides evidence that, for algorithmic interventions, RCT estimates may be lower bounds on scaled impact rather than upper bounds. However, personalization benefits are front-loaded. Gains concentrate in users’ first weeks, with diminishing returns thereafter. This pattern, combined with the sharp decline in predicted match quality as users exhaust their best content matches, suggests that content availability rather than algorithmic sophistication becomes the binding constraint.