Purpose
To enhance student engagement, knowledge retention, and clinical decision-making in Undergraduate Medical Education (UME), this study explores the use of gamification powered by AI (ChatGPT-4) within the microbiology and immunology curriculum. By incorporating game elements such as leaderboards, badges, and quizzes, we aim to create an interactive and enjoyable learning experience that aligns with curricular goals and board exam preparation.

Methods
At the Burnett School of Medicine, TCU, we developed the "Microbiology Gold Cup Game," a gamified learning tool supported by AI-driven content creation using ChatGPT-4. The game includes 620 questions spanning microbiology and immunology topics and integrates spaced repetition and transdisciplinary content from clinical sciences such as pharmacology and pathophysiology. Students participate in pre-session, in-session, and post-session activities designed to reinforce learning in a dynamic, engaging environment. Additionally, the game incorporates board-style questions linked to foundational resources, aligning with exam preparation objectives.

Results
The gamified approach demonstrated high student engagement, with 85% of students participating and 51% completing all activities. Survey feedback indicated that 70% of participants found the game beneficial for board exam preparation, concept prioritization, and knowledge gap identification. Preliminary data also revealed a positive correlation between game performance and exam scores. Notably, the use of AI-driven question generation reduced manual effort and allowed for personalized, adaptive learning.

Conclusions
AI-driven gamification presents a promising method for enriching medical education, making complex topics in microbiology and immunology more accessible and engaging. The integration of spaced repetition and interactive gameplay supports deeper learning and long-term retention. However, continued refinement is necessary, including improvements to visual aids and contextual learning elements. This model offers a scalable approach for enhancing comprehension and clinical preparation in UME.