Exploring the Role of Generative AI in Medical Microbiology Education: Enhancing Bacterial Identification Skills in Laboratory Students

The precise identification of pathogens in biological material is critical for appropriate medical diagnosis and therapy. Medical laboratory students must be proficient in laboratory skills since they play a critical part in the diagnostic process. Using appropriate microscope techniques, one must be able to identify a wide range of pathogens, including bacteria, viruses, parasites, and fungi. Traditional methods of skill development include on-campus practical lessons and field training in hospital microbiology departments. However, the emergence of generative artificial intelligence (AI) opens new avenues for educational enhancement. This study investigates the feasibility of using generative AI, specifically Gemini, to train medical laboratory students in bacterial identification using morphological traits seen in micrographs. The study assessed student learning results using Gemini-generated case studies and quizzes. The results showed that Gemini-generated quizzes helped pupils identify different bacteria based on the micrographs supplied. However, limitations were identified, such as the requirement for teachers to manually add photographs. Overall, the study highlights the potential of generative AI tools in educational contexts, arguing that they could supplement traditional teaching techniques and improve the learning experience for medical laboratory students. More research into generative AI's educational applications is needed to fully realize its potential in medical education.