评估大型语言模型为多样化中学年龄段学习者个性化科学文本的能力
Evaluating the capability of large language models to personalize science texts for diverse middle-school-age learners
AI总结：
本研究通过随机对照试验（n=23）评估GPT-4个性化中学科学文本的能力，发现与学习者偏好一致的文本显著受偏好（p=.059），证明LLM可有效定制教育内容，推动个性化学习技术发展。
摘要：
大型语言模型（LLMs），包括OpenAI的GPT系列，近年来取得了显著进展。这些模型以跨多个学科领域的专业知识和快速适应用户提供提示的能力而闻名，因此作为个性化学习（PL）工具具有独特潜力。尽管有这种潜力，它们在K-12教育中的应用在很大程度上仍未得到探索。本文介绍了首批随机对照试验之一（n = 23），旨在评估GPT-4在为中学生个性化教育科学文本方面的有效性。在本研究中，GPT-4被用于根据训练环节中做出的选择来描绘学生的学习偏好档案。对于实验组，GPT-4被用来重写科学文本，使其与学生预测的档案一致；而对于对照组的学生，文本被重写以与其学习偏好相矛盾。Mann-Whitney U检验的结果显示，当重写文本与学生的档案一致时，学生显著偏好（在0.10水平上）这些文本（p = .059）。这些发现表明，GPT-4能够有效解释并定制教育内容以适应多样化的学习者偏好，标志着PL技术的重大进步。本文还讨论了本研究的局限性以及在教育中使用人工智能的伦理考量。
英文摘要：
Large language models (LLMs), including OpenAI's GPT-series, have made significant advancements in recent years. Known for their expertise across diverse subject areas and quick adaptability to user-provided prompts, LLMs hold unique potential as Personalized Learning (PL) tools. Despite this potential, their application in K-12 education remains largely unexplored. This paper presents one of the first randomized controlled trials (n = 23) to evaluate the effectiveness of GPT-4 in personalizing educational science texts for middle school students. In this study, GPT-4 was used to profile student learning preferences based on choices made during a training session. For the experimental group, GPT-4 was used to rewrite science texts to align with the student's predicted profile while, for students in the control group, texts were rewritten to contradict their learning preferences. The results of a Mann-Whitney U test showed that students significantly preferred (at the .10 level) the rewritten texts when they were aligned with their profile (p = .059). These findings suggest that GPT-4 can effectively interpret and tailor educational content to diverse learner preferences, marking a significant advancement in PL technology. The limitations of this study and ethical considerations for using artificial intelligence in education are also discussed.
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。
