A practice-based approach to teaching antimicrobial therapy using artificial intelligence and gamified learning.

Objectives: Scalable teaching through apps and artificial intelligence (AI) is of rising interest in academic practice. We focused on how medical students could benefit from this trend in learning antibiotic stewardship (ABS). Our study evaluated the impact of gamified learning on factual knowledge and uncertainty in antibiotic prescription. We also assessed an opportunity for AI-empowered evaluation of freeform answers. Methods: We offered four short courses focusing on ABS, with 46 participating medical students who self-selected themselves into the elective course. Course size was limited by the faculty. At the start of the course, students were given a questionnaire about microbiology, infectious diseases, pharmacy and qualitative questions regarding their proficiency of selecting antibiotics for therapy. Students were followed up with the same questionnaire for up to 12 months. We selected popular game mechanics with commonly known rules for teaching and an AI for evaluating freeform questions. Results: The number of correctly answered questions improved significantly for three topics asked in the introductory examination, as did the self-assessed safety of prescribing antibiotics. The AI-based review of freeform answers was found to be capable of revealing students' learning gaps and identifying topics in which students needed further teaching. Conclusions: We showed how an interdisciplinary short course on ABS featuring gamified learning and AI could substantially improve learning. Even though large language models are a relatively new technology that sometimes fails to produce the anticipated results, they are a possible first step in scaling a tutor-based teaching approach in ABS.