Can Large Language Models Bridge the Gap in Environmental Knowledge?

The investigation employs a standardized tool, the Environmental Knowledge Test (EKT - 19), supple mented by targeted questions, to evaluate the environmental knowledge of university students in comparison to the responses generated by the AI models. The results of this study suggest that while AI models possess a vast, readily accessible, and valid kno wledge base with the potential to empower both students and academic staff, a human discipline specialist in environmental sciences may still be necessary to validate the accuracy of the information provided. Keywords: En vironmental Education; AI Models; EKT - 19 1. Introduction Extreme weather events, increasing global temperatures, rising sea - levels, and changes to ecosystems and biodiversity are all consequences of climate change, which is mostly caused by anthropogenic greenhouse gas emissions ( Masson - Delmotte et al., 2018). Meanwhile, the loss of biodiversity due to habitat degradation, pollution, overexploitation, and invasive species threatens the resilience of society's ecosystems (Nature, 2021). These consequences pose questions regarding food security, public he alth, and socioeconomic stability. Thus, effective access to accurate environmental knowledge is crucial for developing sustainable solutions and informed environmental policies.