Martin Saraka
Supervisor(s): Ing. Martin Dubovský
Slovak Technical University
Abstract: Effective teaching of complex and specialized subjects,
such as histopathology, requires more than traditional
methods, which often lack interactivity and personalization necessary for deep understanding. The integration of
artificial intelligence (AI) into educational tools opens new
possibilities for enhancing the learning process through
adaptive support and interactive feedback.
We present an AI-assisted annotation tool for
histopathology education, designed to balance automated assistance with user control while fostering critical
thinking and expert oversight. Our system incorporates
manual editing, partially AI-driven suggestions, and
Wizard-of-Oz simulations to explore advanced tutoring
functions. Key features, including contextual textual
hints, visual overlays, interactive learning cards, and
curated study materials, aim to enhance diagnostic training by providing targeted guidance and reinforcing key
morphological concepts.
Through an iterative, user-centered design, we analyze
how AI-powered supportive functions influence the accuracy and efficiency of histopathological image annotations
among students and whether these features can aid laypersons in grasping fundamental histopathological principles.
By evaluating the impact of these AI-driven explanations
and interactive modules, we aim to identify strategies that
minimize cognitive load, improve retention, and optimize
the integration of AI in medical education.
Our findings contribute to the development of AIenhanced educational tools that support deep learning,
improve diagnostic skills, and create a scalable, adaptable framework for medical training. The system is now
prepared for extensive student-based evaluation to assess
its effectiveness in fostering a deeper understanding of
histopathology and its broader implications for AI integration in education.Keywords: Human-Computer InteractionFull text: Year: 2025