Large-scale randomized experiment reveals machine learning helps people learn and remember more effectively
This work addresses the challenge of enhancing educational outcomes for learners in mobility-related apps, representing an incremental application of existing ML techniques to a new domain.
The paper tackled the problem of improving human learning and memory using machine learning, finding that ML-optimized study sessions increased retention by ~67% and boosted user return rates by ~50% compared to heuristic methods.
Machine learning has typically focused on developing models and algorithms that would ultimately replace humans at tasks where intelligence is required. In this work, rather than replacing humans, we focus on unveiling the potential of machine learning to improve how people learn and remember factual material. To this end, we perform a large-scale randomized controlled trial with thousands of learners from a popular learning app in the area of mobility. After controlling for the length and frequency of study, we find that learners whose study sessions are optimized using machine learning remember the content over $\sim$67% longer than those whose study sessions are generated using two alternative heuristics. Our randomized controlled trial also reveals that the learners whose study sessions are optimized using machine learning are $\sim$50% more likely to return to the app within 4-7 days.