# Can Large Language Models Bridge the Gap in Environmental Knowledge?

arXiv: https://arxiv.org/abs/2508.03149 · HTML: https://mlsanity.com/paper/2508.03149/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 25 references that are in the map.
- First posted on arXiv 5 Aug 2025.

## Builds on (most cited first, 3 of 3)

- [Large Language Models: A Survey](https://mlsanity.com/paper/2402.06196/) (2024)
- [ClimateGPT: Towards AI Synthesizing Interdisciplinary Research on Climate Change](https://mlsanity.com/paper/2401.09646/) (2024)
- [New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution](https://mlsanity.com/paper/2305.18303/) (2023)

## Abstract (arXiv)

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.