ReadingQuizMaker: A Human-NLP Collaborative System that Supports Instructors to Design High-Quality Reading Quiz Questions
Xinyi Lu, Simin Fan, Jessica Houghton, Lu Wang, Xu Wang
CHI'2023 Best Paper Honorable Mention
Despite that reading assignments are prevalent, methods to en- courage students to actively read are limited. We propose a system ReadingQuizMaker that supports instructors to conveniently de- sign high-quality questions to help students comprehend readings. ReadingQuizMaker adapts to instructors’ natural workflows of creating questions, while providing NLP-based process-oriented support. It enables instructors to decide when and which NLP models to use, select the input to the models, and edit the outcomes.