Ontology-Based Automated Generation of Medical Digital Learning Content: Methodology and Prototype Development for Enhanced Medical Education

Abstract

This thesis presents the design and evaluation of a prototype application aimed at enhancing medical education through the use of ontology-driven feedback. The application generates medical case studies that simulate real-life scenarios, focusing on diagnosing ear diseases. The pilot study, conducted with a small group of medical students, demonstrated the potential value of ontology-driven feedback in guiding students through the diagnostic process. Detailed log analysis identified common mistakes and knowledge gaps among students, highlighting areas where the prototype can be tailored to address these issues in future iterations. The learning process observed, with participants refining their choices and improving their accuracy, underscores the application’s potential to enhance medical training. User experience feedback indicated that while the application was generally useful and informative, improvements in design and usability are needed to increase engagement and intuitiveness. Although the study did not show a significant improvement between pre- and post-test scores, the overall feedback from participants and their progression through exercises suggest that this approach effectively supports the learning process. The study faced limitations, including the specific focus on otology, time constraints in developing a user-friendly interface, and challenges in recruiting a sufficient number of medical students for evaluation. These factors highlight the need for future studies with larger participant pools to validate the findings and refine the application. Additionally, expanding the application’s capabilities to cover other ENT diseases will broaden its utility. Writing scientific publications and presenting at international conferences are planned.