ReaderQuizzer: Augmenting Research Papers with Just-In-Time Learning Questions to Facilitate Deeper Understanding

Academic reading is a key component of higher education, and serves as a basis for critical thinking, knowledge acquisition and effective communication. Research shows many students struggle with comprehension and analysis tasks with academic texts, despite the central importance of academic reading to success in higher education. Undergraduates and researchers need to internalize dense literature to scaffold their own work upon it. This reading task is time-consuming and difficult to do. Oftentimes, students struggle to actively and critically engage and as a result attain merely a cursory understanding of a paper's contents, or worse, incorrectly interpret the text. How, then, can we provide a means to more easily digest a text while also facilitating meaningful, critical engagement and understanding? This paper locates itself within the broader field of augmented reading interfaces to implement an augmented reading interface that leverages the power of large language models (LLM) to intelligently generate and co-locate comprehension and analysis questions in an academic paper, thereby making the paper more digestible with the end goal of facilitating deeper understanding, and developing critical reading skills.