Prepform is built on the idea of a study plan, where you: 1. Take a diagnostic test 2. Set a target score 3. Create a study schedule 4. Identify mistakes, and finally, 5. Review mistakes.

I wanted to take this structure and personalize it for each student using machine learning, spaced repetition, and data visualization.

Students first take practice tests, then analyze their mistakes using the data visualization tools. Then they log their mistakes, where they answer “What did I do wrong?”, “What should I have done instead?”, and “What is the clue in this question that I missed?”. This is the key here, since reviewing mistakes is where the bulk of learning happens.

Lastly, the mistakes will be shown again at appropriate intervals, and included in future practice tests.

The app is currently training the AI model, which will be used to predict the odds of a student answering a question correctly.