The Effect of AI-Assisted Image Generation (GPT/DALL·E) on Academic Self-Regulation and Engagement of Midwifery Students in the Theoretical Physiology ۲ Course: A Single-Group Study

Background and Aims: The AI-assisted educational method can bridge the gap between theory and practice by providing realistic and interactive images of concepts related to pregnancy-associated diseases, thereby better preparing students for clinical challenges. This study aimed to investigate the impact of AI-based visualization on academic motivation and self-regulatory learning abilities among fifth-semester midwifery students in the Physiopathology II course (second semester of the ۲۰۲۳-۲۰۲۴ academic year). Given the complexity of both theoretical and clinical concepts in the Physiopathology course and the need for precise visual resources, modern technologies such as DALLE and ChatGPT were employed alongside the instruction of theoretical principles. Methods: In this quasi-experimental pretest-posttest study, ۲۰ fifth-semester midwifery students were purposefully selected. The educational process comprised eight two-hour sessions. During these sessions, after identifying the key aspects of pregnancy-related diseases—such as the effects of lupus on the kidneys, the joint damage caused by rheumatoid arthritis, and the impact of migraine headaches—detailed instructions were provided to the AI system to generate corresponding images. These images were used as supplementary educational resources in the classroom, and students analyzed and interpreted them to simplify complex concepts. The impact of this method was evaluated using standard questionnaires for academic motivation (Gonius and Kozo) and self-regulated learning (Buffard et al.), and the data were analyzed using SPSS version ۲۴. Results: The statistical analysis revealed that the overall mean academic motivation score significantly increased from ۱۹۳.۳۷ before the intervention to ۲۳۱.۶۸ after the intervention. Similarly, the overall mean self-regulated learning score rose significantly from ۵۴.۶۸ pre-intervention to ۶۴.۲۱ post-intervention, with the total changes in both variables showing statistically significant differences. Conclusion: The study demonstrated that providing accurate and interactive images to students enhances both self-regulated learning processes and academic motivation. This educational approach contributes to reducing the gap between theoretical instruction and practical application.

کلیدواژه ها:

Self-Regulated Learning ، Artificial Intelligence ، Education ، Midwifery