2025 ASEE Annual Conference & Exposition

Active recall and spaced repetition as study techniques have shown to improve comprehension and long-term knowledge retention. Active recall asks students to self-create review questions from learning material and gauge their ability to retrieve answers from memory during review sessions. Spaced repetition requires scheduling active recall review sessions systematically to maximize retention and understanding of learned material. Common software tools used by students to implement these techniques include Anki and Quizlet, which provide flashcard creation and scheduling capabilities. While these tools are effective, it can often be time-consuming for students to set up a thorough review set. When insufficient questions are provided, users often end up memorizing the set of answers, rather than focusing on learning the concepts.

Large-language models (LLMs) offer the ability to augment the active recall and spaced repetition learning process through the automatic generation and evaluation of similar content review questions. We introduce a tool which allows users to request additional LLM-generated review questions, which will be slightly varied questions on the same topics they have self-created. Students can also receive LLM-generated evaluations of their responses to questions, helping them assess their understanding of topics. By asking subtly different questions on similar topics, we hypothesize that our tool will reinforce a deeper understanding of core concepts, rather than encourage memorization of the same questions and answers. Additionally, by reducing the time spent on creating additional review content, students can spend more time on reviewing course material.

Upon completion of our tool, we plan to conduct a controlled study that compares students in a course who utilize our tool (test set) with students utilizing other flashcard applications or traditional study techniques within the same course (control set). Students will use our tool to create and review course questions over a defined time period. After this period, students will be prompted to review how much time was saved using our tool, their level of confidence with each course topic, and their satisfaction with LLM-generated questions and responses. Additionally, we can use in-app metrics such as user engagement and question review sessions to gauge student satisfaction. This methodology measures student confidence and engagement with course learning to observe the impact of our tool in streamlining the active recall and spaced repetition learning process.