Artificial intelligence-tutoring problem-based learning in ophthalmology clerkship

Dongxuan Wu, Yifan Xiang, Xiaohang Wu, Tongyong Yu, Xiucheng Huang, Yuxian Zou, Zhenzhen Liu, Haotian Lin


Background: Artificial intelligence (AI) is an increasingly popular tool in medical investigations. However, AI’s potential of aiding medical teaching has not been explored. This study aimed to evaluate the effectiveness of AI-tutoring problem-based-learning (PBL) in ophthalmology clerkship and to assess the student evaluations of this module.
Methods: Thirty-eight Grade-two students in ophthalmology clerkship at Sun Yat-Sen University were randomly assigned to two groups. In Group A, students learned congenital cataracts through an AI-tutoring PBL module by exploring and operating an AI diagnosis platform. In Group B, students learned congenital cataracts through traditional lecture given with the same faculty. The improvement in student performance was evaluated by comparing the pre- and post-lecture scores of a specific designed test using paired-T tests. Student evaluations of AI-tutoring PBL were measured by a 17-item questionnaire.
Results: The post-lecture scores were significantly higher than the pre-lecture scores in both groups (Group A: P<0.0001, Group B: P<0.0001). The improvement of group A in the part of sign and diagnosis test (Part I) was more significant than that of group B (P=0.016). However, there was no difference in the improvement in the part of treatment plan test (Part II) between two groups (P=0.556). Overall, all respondents were satisfied and agreed that AI-tutoring PBL was helpful, effective, motive and beneficial to help develop critical and creative thinking.
Conclusions: The application of AI-tutoring PBL into ophthalmology clerkship improved students’ performance and satisfaction. AI-tutoring PBL teaching showed advantage in promoting students’ understanding of signs of diseases. The instructors play an indispensable role in AI-tutoring PBL curriculum.