  The structure of curriculum plays a vital role in our learning process, both
as children and adults. Presenting material in ascending order of difficulty
that also exploits prior knowledge can have a significant impact on the rate of
learning. However, the notion of difficulty and prior knowledge differs from
person to person. Motivated by the need for a personalised curriculum, we
present a novel method of curriculum learning for vocabulary words in the form
of visual prompts. We employ a reinforcement learning model grounded in
pedagogical theories that emulates the actions of a tutor. We simulate three
students with different levels of vocabulary knowledge in order to evaluate the
how well our model adapts to the environment. The results of the simulation
reveal that through interaction, the model is able to identify the areas of
weakness, as well as push students to the edge of their ZPD. We hypothesise
that these methods can also be effective in training agents to learn language
representations in a simulated environment where it has previously been shown
that order of words and prior knowledge play an important role in the efficacy
of language learning.
