Abstract
This article examines the role of artificial intelligence (AI) tools for data visualization and interpretation in teaching the course"Biodiversity and sustainable development goals". Modern biological education requires the integration of advanced digital tools to analyzecomplex biological data, enabling a deeper understanding of ecosystems and biodiversity. AI provides methods for efficient data processing, visualization, and accurate modeling, assisting both researchers and students in identifying patterns and correlations within biological systems.Studies byIvanov V. G.andPetrov A. S.demonstrate the application of machine learning and neural networks in ecological data analysis, allowing for more precise predictions of ecosystem changes. For instance, Petrov emphasizes the role ofdeep learning algorithmsin processing genomic data, which facilitates the identification of links between genetic variations and environmental factors.The integration of AI into the curriculum enhances students’ analytical skills and fosters their readiness to address modern challenges related to biodiversity and sustainable development. Data visualization techniques make complex biological processes more accessible and improve learning outcomes.