Enhancing student reading performance through a personalized two-tier problem-based learning approach with generative artificial intelligence - Changqin Huang; Yihua Zhong; Yongzhi Li; Xizhe Wang; Zhongmei Han; Di Zhang; Ming Liu (Humanities & Social Sciences Communications)
Abstract Reading ability plays a vital role in the academic success of students. Problem-based learning (PBL) helps develop deep engagement with the reading materials and higher-order reading skills. However, conventional PBL (C-PBL) activities ignore differences in students’ cognitive levels and fail to provide timely and targeted feedback and guidance to each student. As a result, many students are unable to actively engage in PBL-based reading activities. To address these problems, this study proposes a personalized two-tier PBL (PT-PBL) approach based on generative artificial intelligence (GenAI). It provides a more personalized and refined design for PBL activities to promote personalized reading learning for students. To examine the effectiveness of the proposed approach, 62 college students participated in a quasi-experiment, with the PT-PBL approach in the experimental group and the C-PBL approach in the control group. The results indicate that the PT-PBL approach significantly improves students’ reading performance and motivation. In addition, compared to students with lower engagement, this approach is more effective at improving the reading performance of highly engaged students. Interviews with students showed that those who used the PT-PBL approach focused more on reading tasks and reflected more frequently. The main contribution of this study is proposing a novel PT-PBL approach and providing empirical evidence of its effectiveness, while also creating opportunities for future research to further explore the positive impact of GenAI on reading.