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 analyze complex 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 by Ivanov V. G. and Petrov 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 of deep learning algorithms in 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.