用人工智能弥合早期科学教育差距：评估大语言模型作为幼儿科学教育工具的作用
Bridging the Early Science Gap with Artificial Intelligence: Evaluating Large Language Models as Tools for Early Childhood Science Education
AI总结：
本研究评估四款主流大语言模型生成学龄前科学教育内容的能力，发现Claude表现最优，所有模型均难处理抽象化学概念，为AI在幼儿科学教育的应用及模型优化提供参考。
摘要：
幼儿科学教育对培养科学素养至关重要，但将复杂的科学概念转化为适合年龄的内容对教育工作者而言仍是挑战。本研究评估了四款主流大语言模型（LLMs）——GPT-4、Claude、Gemini 和 Llama——生成适合学龄前儿童的生物、化学、物理领域科学解释的能力。通过30名幼儿园教师采用既定教学标准开展系统评估，我们发现这些模型在生成兼具趣味性、准确性且符合发展阶段的内容方面能力存在显著差异。出乎意料的是，Claude 表现优于其他模型，尤其在生物主题上，而所有 LLM 在处理抽象化学概念时均存在困难。本研究结果为教育工作者在早期科学教育中运用AI提供了实用见解，也为开发者优化 LLM 的教育应用提供了指导，凸显了利用 LLM 弥合幼儿科学素养差距的潜力与当前局限。
英文摘要：
Early childhood science education is crucial for developing scientific literacy, yet translating complex scientific concepts into age-appropriate content remains challenging for educators. Our study evaluates four leading Large Language Models (LLMs) - GPT-4, Claude, Gemini, and Llama - on their ability to generate preschool-appropriate scientific explanations across biology, chemistry, and physics. Through systematic evaluation by 30 nursery teachers using established pedagogical criteria, we identify significant differences in the models' capabilities to create engaging, accurate, and developmentally appropriate content. Unexpectedly, Claude outperformed other models, particularly in biological topics, while all LLMs struggled with abstract chemical concepts. Our findings provide practical insights for educators leveraging AI in early science education and offer guidance for developers working to enhance LLMs' educational applications. The results highlight the potential and current limitations of using LLMs to bridge the early childhood science literacy gap.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
