Student-AI Question Cocreation for Enhancing Reading Comprehension
Ming Liu, Jinxu Zhang, Lucy Michael Nyagoga, Li Liu
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 student question generation 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 post-pandemic era. Thus, this article presents a student-Artificial Intelligence (AI) co-creation 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 pre-trained 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 shows 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 co-creation is beneficial to SQG training and educational assessment for reading comprehension, such as repeated practices.