Learning is often inefficient because students are rarely taught how to learn effectively. Despite the popularity of methods such as rereading, highlighting and summarising, research indicates that these techniques offer limited benefits for memory retention and recall.

In contrast, Active Recall, which involves retrieving information from memory through self-testing, is one of the most effective learning strategies. However, currently, there is a lack of platforms that leverage this technique beyond flashcard systems.

This project presents a web application that integrates Active Recall techniques using Large Language Models (LLMs), enabling students to generate question decks based on text input. Background research explored the capabilities of LLMs in Question Generation and Automated Answer Evaluation which form the foundation for the underlying functionality of the website. An evaluation was then conducted to examine the quality of generation and automatic assessment tasks by the LLM.