There was a lot of previous work done in order to improve student learning. I will list here only some of the works that were provided in the research paper. The idea to use DRL to solve this problem is not brand-spark new. In 2014 several researchers developed "a reinforcement learning agent for an adaptive tutoring task in a challenging realworld educational game application, using an offline importance sampling-based method to select representations and hyperpameter settings without expensive online experiments on real students."

The Leithner system (the German science journal gets credit for it, this journal proposed efficient flashcard usage) "uses a network of first-in-first-out queues to coarsely prioritize items by novelty and difficulty." This system is quite old, it was developed in the 1970s.

Most of the previous works were closely related with machine teaching and knowledge tracing problems. The former one attempts to find optimal set for the provided algorithm and the latter one observes and measures how student's knowledge changes.