AI Applications in Digital Histopathology Education: A Case Study

Effective teaching of complex and specialized subjects, such as histopathology, requires more than traditional methods, which often lack the interactivity and personalization necessary for deep understanding. The integration of artificial intelligence (AI) into educational tools opens new possibilities to enhance the learning process through adaptive support and interactive feedback. The research field of human–AI interaction (HAI) is rapidly growing, bringing new challenges as AI becomes increasingly integrated into everyday life and work. We propose an innovative AI-assisted educational tool for digital histopathology, designed to balance the benefit of AI assistance with user control while fostering critical thinking and domain expert’s knowledge. Key features of proposed educational-annotation tool include contextual textual hints, visual overlays, interactive learning cards, and curated study materials, which aim to improve diagnostic training by providing targeted guidance and reinforcing key morphological concepts. An iterative, user-centered design approach was used to analyze how AI-powered supportive functions influence the accuracy and efficiency of histopathological image annotations among students, and whether these features assist students in understanding fundamental histopathological principles. By evaluating the impact of these AI-augmented educational features, the goal is to identify strategies that minimize cognitive load, improve retention, and optimize AI integration in medical education.