The use of AI-generated content in education is significantly increasing, but its reliability for
teaching natural sciences and, more specifically, biodiversity-related contents still remains
understudied. The need to address this question is substantial, considering the relevance
that biodiversity conservation has on human sustainability, and the recurrent presence of
these topics in the educational curriculum, at least in Spain. The present article tests the
existence of biases in some of the most widely used AI tools (ChatGPT-4.5, DeepSeek-V3,
Gemini) when asked a relevant and objective research question related to biodiversity. The
results revealed both taxonomic and geographic biases in all the lists of endangered species
provided by these tools when compared to IUCN Red List data. These imbalances may
contribute to the perpetuation of plant blindness, zoocentrism, and Western centrism in
classrooms, especially at levels where educators lack specialized training. In summary, the
present study highlights the potential harmful impact that AI’s cultural and social biases
may have on biodiversity education and Sustainable Development Goals-aligned learning
and appeals to an urgent need for model refinement (using scientific datasets) and teacher
AI literacy to mitigate misinformation.
