  The individualization of learning contents based on digital technologies
promises large individual and social benefits. However, it remains an open
question how this individualization can be implemented. To tackle this question
we conduct a randomized controlled trial on a large digital self-learning
platform. We develop an algorithm based on two convolutional neural networks
that assigns tasks to $4,365$ learners according to their learning paths.
Learners are randomized into three groups: two treatment groups -- a
group-based adaptive treatment group and an individual adaptive treatment group
-- and one control group. We analyze the difference between the three groups
with respect to effort learners provide and their performance on the platform.
Our null results shed light on the multiple challenges associated with the
individualization of learning paths.
