标题： 考察入门物理中学生和人工智能生成的个性化类比 显示英文标题

标题： Examining Student and AI Generated Personalized Analogies in Introductory Physics

摘要： 通过类比将抽象概念（如电路）与熟悉的想法（如供水系统）进行比较，是物理教学和交流的核心。当代研究强调自我生成的类比，认为这比教授的类比更能促进学生的学习。"自发的"和"自我生成的"类比代表了学生构建个性化类比的两种方式。然而，在大班课程中促进这些类比仍然是一项挑战，而生成式人工智能（AI）的最新发展有望解决这一问题。在本项定性研究中，我们分析了大约800名学生的回答，探讨学生在用适合二年级学生的语言解释莫尔斯势曲线时，是否会自发地运用类比，以及他们在自己偏好的日常情境中自我生成类比的程度。我们还将学生生成的自发类比与学生提示生成的AI类比进行了比较。最后，我们探讨了在两种情况下学生在生成类比时所感知到的容易和困难的主题。结果表明，与AI的回答不同，学生生成的自发解释很少使用类比。然而，当被明确要求用日常情境来解释曲线的行为时，学生会使用多种类比情境。学科知识、生成定制解释的自主性以及个人特质往往会影响学生在两种情况下生成解释时的感知难易程度。讨论了这些结果对AI在促进学生个性化类比推理方面的潜力，以及类比在使学生注意到其理解中的空白中的作用的意义。 显示英文摘要

摘要： Comparing abstract concepts (such as electric circuits) with familiar ideas (plumbing systems) through analogies is central to practice and communication of physics. Contemporary research highlights self-generated analogies to better facilitate students' learning than the taught ones. "Spontaneous" and "self-generated" analogies represent the two ways through which students construct personalized analogies. However, facilitating them, particularly in large enrollment courses remains a challenge, and recent developments in generative artificial intelligence (AI) promise potential to address this issue. In this qualitative study, we analyze around 800 student responses in exploring the extent to which students spontaneously leverage analogies while explaining Morse potential curve in a language suitable for second graders and self-generate analogies in their preferred everyday contexts. We also compare the student-generated spontaneous analogies with AI-generated ones prompted by students. Lastly, we explore the themes associated with students' perceived ease and difficulty in generating analogies across both cases. Results highlight that unlike AI responses, student-generated spontaneous explanations seldom employ analogies. However, when explicitly asked to explain the behavior of the curve in terms of their everyday contexts, students employ diverse analogical contexts. A combination of disciplinary knowledge, agency to generate customized explanations, and personal attributes tend to influence students' perceived ease in generating explanations across the two cases. Implications of these results on the potential of AI to facilitate students' personalized analogical reasoning, and the role of analogies in making students notice gaps in their understanding are discussed.