Can Large Language Models Bridge the Gap in Environmental Knowledge?

TL;DR

This study evaluates the ability of large language models to enhance environmental education by assessing their knowledge accuracy and potential to bridge the knowledge gap among university students.

Contribution

It compares multiple LLMs' effectiveness in conveying environmental concepts and highlights the need for human validation of AI-generated environmental information.

Findings

LLMs possess extensive environmental knowledge.

AI models can support environmental education.

Human validation remains essential for accuracy.

Abstract

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.