Can Large Language Models Bridge the Gap in Environmental Knowledge?
This addresses the problem of environmental education gaps for university students, but it is incremental as it applies existing LLMs to a new domain without novel methodological contributions.
This research investigated whether large language models (LLMs) like GPT-3.5, GPT-4, GPT-4o, Gemini, Claude Sonnet, and Llama 2 can bridge environmental knowledge gaps in university education by assessing their effectiveness using the Environmental Knowledge Test (EKT-19) and targeted questions, finding that while LLMs offer a vast and valid knowledge base, human specialists are still needed to validate accuracy.
This research investigates the potential of Artificial Intelligence (AI) models to bridge the knowledge gap in environmental education among university students. By focusing on prominent large language models (LLMs) such as GPT-3.5, GPT-4, GPT-4o, Gemini, Claude Sonnet, and Llama 2, the study assesses their effectiveness in conveying environmental concepts and, consequently, facilitating environmental education. The investigation employs a standardized tool, the Environmental Knowledge Test (EKT-19), supplemented 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 knowledge 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.