Artificial Intelligence Assisted Histology Learning for High School students in Thailand by using Deep Learning Models as Tools for Interdisciplinary STEM Engagement

STEM education, especially when integrated with artificial intelligence (AI), offers a powerful framework for developing essential 21st-century skills such as critical thinking, creativity, and interdisciplinary problem-solving. While AI has shown promise in enhancing medical education and diagnostic accuracy, its application in high school biology-particularly in histology-remains limited. This study explores how combining AI-driven histological image classification with hands-on learning can enrich STEM education in Thailand by bridging gaps in traditional teaching methods and fostering deeper engagement with both biomedical science and machine learning. This project introduces students to thyroid tissue classification using a convolutional neural network (CNN) based on the VGG16 architecture, implemented on the Google Co-lab platform with Python. The model, trained on curated histological images of normal and Graves’ disease-affected thyroid tissue, achieved 100% classification accuracy. Students engaged with the model by inputting real histological images, receiving instant predictions, and comparing these with their own reasoning to develop visual diagnostic skills. A pretest and posttest design involving 24 high school students was used to evaluate the educational impact. Results revealed statistically significant improvements in post-test scores ($\mathbf{p}\lt.001$), indicating enhanced understanding of histological structures and diagnostic reasoning. The activity highlights the potential of AI as a cognitive scaffold in science education, promoting interactive learning and conceptual comprehension. Despite limitations including sample size and dataset scope, the study suggests strong scalability, with potential applications in higherlevel medical education and across various organ systems. This work contributes to the advancement of interdisciplinary STEM education and demonstrates the value of early exposure to AI tools in developing future-ready learners.