大模型能补足大学生环保知识短板，但需专家把关准确性。
Can Large Language Models Bridge the Gap in Environmental Knowledge?
- 用标准化测试对比学生与6大AI模型的环保知识水平。
- 多款大模型知识量大且准确，但部分答案需人工校验。
- 适合教育工作者和想自学环保的学生参考。
本研究探讨人工智能模型在弥合大学生环境教育知识差距方面的潜力。聚焦GPT-3.5、GPT-4、GPT-4o、Gemini、Claude Sonnet及Llama 2等主流大语言模型，通过使用标准化的环境知识测试（EKT-19）结合定向问题，评估大学生与AI模型在环境概念理解上的差异。结果表明，尽管这些大模型具备广泛、可获取且可靠的环境知识库，有助于提升学生与教师的教育效果，但仍需环境科学领域的专业人员对AI输出内容进行准确性验证。
原文摘要 · Abstract (English)
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.
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