The purpose of AI here is to create a student model to personalize learning, increase students' motivation, and increase their speed of learning.

It’s hard for students to know what and for how long to study, and when they’ve mastered something. By analyzing a student’s performance, we estimate their current knowledge, and use it to determine factors such as the best repeat frequency, the type of question, and the difficulty of the question.

For example, studies show that students stop studying when they don’t fully know the material, and they don’t study material they think they’ve already learned. By guiding them through a study plan and using spaced-repetition, we can improve the speed of learning and reduce the effects of forgetting.

Our model accounts for forgetting, guessing, the order of answers, and a student’s baseline knowledge to predict their current knowledge. While we are currently training the model for the NY Regents exam, we have tested it on EdNet, the largest publicly-available education dataset available, and it has the highest predictive accuracy among competing models, with an AUC of 0.7892.