AI in Biodiversity Education: The Bias in Endangered Species Information and Its Implications

Luis de Pedro Noriega, Javier Bobo-Pinilla (), Jaime Delgado-Iglesias, Roberto Reinoso-Tapia, Ana María Gallego and Susana Quirós-Alpera
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Luis de Pedro Noriega: Facultad de Educación de Palencia, Universidad de Valladolid, 34004 Palencia, Spain
Javier Bobo-Pinilla: Facultad de Educación y Trabajo Social, Universidad de Valladolid, 47011 Valladolid, Spain
Jaime Delgado-Iglesias: Facultad de Educación y Trabajo Social, Universidad de Valladolid, 47011 Valladolid, Spain
Roberto Reinoso-Tapia: Facultad de Educación y Trabajo Social, Universidad de Valladolid, 47011 Valladolid, Spain
Ana María Gallego: Facultad de Educación y Trabajo Social, Universidad de Valladolid, 47011 Valladolid, Spain
Susana Quirós-Alpera: Facultad de Educación y Trabajo Social, Universidad de Valladolid, 47011 Valladolid, Spain

Sustainability, 2025, vol. 17, issue 14, 1-18

Abstract: 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.

Keywords: AI bias; biodiversity education; endangered species; sustainable development goals (SDGs); taxonomic bias; geographic bias (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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