Integrating AI into Biology Education: Classifying Edible and Poisonous Mushrooms Using Machine Learning

As artificial intelligence (AI) becomes increasingly prevalent in various domains, its integration into education offers unique opportunities to enhance learning experiences. This paper explores the application of AI, specifically machine learning, in biology education through the classification of edible and poisonous mushrooms. Leveraging a dataset of gilled mushrooms from the Agaricus and Lepiota families, this study demonstrates how AI techniques can be used to classify species based on morphological features such as cap shape, gill attachment, and color. The use of AI not only improves students' understanding of species identification and biological classification but also introduces them to key concepts in data science, such as feature selection, model training, and validation. By implementing machine learning algorithms like decision trees and support vector machines, students can engage in interactive learning experiences that merge biology with modern technological tools. The results show that integrating AI into biology curricula can foster both scientific literacy and computational thinking, offering a more comprehensive and engaging approach to science education. This paper outlines a practical framework for educators to incorporate AI in biology lessons, preparing students for future academic and career challenges in an increasingly digital world.