Student-AI Question Cocreation for Enhancing Reading Comprehension

Abstract

Student question generation (SQG) is an effective strategy for improving reading comprehension. It helps students improve their understanding of reading materials, metacognitively monitor their comprehension, and self-correct comprehension gaps. Internet technologies have been used to facilitate SQG process through intensive peer support. However, the availability, level of task commitment, and capabilities of student peers have emerged as significant concerns, particularly in light of the global pandemic and the subsequent postpandemic era. Thus, this article presents a student-artificial intelligence (AI) cocreation tool called CoAsker for supporting question generation. Following recent human-computer interaction (HCI) research in human-AI collaborative writing, CoAsker first allows students to provide question clues and answers and then uses a state-of-the-art pretrained language model, T5-PEGASUS, to generate questions. Finally, the student can use this AI question directly or perform reflection by comparing his or her questions with the AI question. An empirical study was conducted to examine the quality of AI questions and the effect of this tool on student engagement and reading comprehension. The results of the study show that students using this tool (treatment) were more engaged in generating low-level cognitive questions and performed better in acquiring knowledge than those using a traditional online question generation tool (control). These results indicate that student-AI question cocreation is beneficial to SQG training and educational assessment for reading comprehension, such as repeated practices.