发表机构
University of Michigan; University of Toronto（密歇根大学; 多伦多大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过访谈和出声思考，发现学生对兴趣个性化GenAI类比持矛盾态度，提出双向类比审计，强调系统应关注学生知识而非仅兴趣。
AI 中文摘要
动机：本科计算专业学生越来越多地使用生成式人工智能（GenAI）工具，通过类比来理解抽象概念。类比将不熟悉的概念与熟悉的事物进行比较，但判断这种比较是否成立需要对两者都有所了解。GenAI 也可能嵌入关于学习者是谁的假设。GenAI 教育研究侧重于输出正确性，导致学生对类比的批判性接受在很大程度上未被研究。方法：我们调查了学生如何评估 GenAI 生成类比的准确性、适当性和假设，以及他们对兴趣个性化与通用技术解释的看法。十名具有 CS2 经验的学生参与了一项预调查、一项包含链表和递归解释的出声思考任务，以及一项基于 Paul-Elder 框架的半结构化访谈。他们分别判断了准确性、清晰度、参与度和信任度。结果：大多数参与者认为兴趣个性化的类比比通用技术解释更具吸引力或更令人难忘，而信任度则褒贬不一。一些人更信任定制化的类比；另一些人则更仔细地审查它们或不信任这种定制。具有深度源领域知识的参与者识别出了需要该知识才能发现的结构性缺陷。由于个性化和解释格式同时不同，这些发现并未单独分离出个性化的效果。意义：熟悉的源翻转了学生的角色。在概念上他们仍是学习者，但在熟悉的源上他们是专家，而这正是判断类比的立场。我们称之为双向类比审计。GenAI 系统应询问学生知道什么，而不仅仅是对什么感兴趣，并将有缺陷的类比视为需要检查和修复的东西，而不是接受。
英文摘要
Motivation: Undergraduate computing students increasingly turn to generative AI (GenAI) tools to understand abstract concepts through analogies. Analogies compare an unfamiliar concept to something familiar, but judging whether the comparison holds requires knowledge of both. GenAI may also embed assumptions about who the learner is. GenAI education research centers on output correctness, leaving students' critical reception of analogies largely unexamined. Method: We investigate how students evaluate the accuracy, appropriateness, and assumptions in GenAI-generated analogies, and their perceptions of interest-personalized versus generic technical explanations. Ten students with CS2 experience participated in a pre-survey, a think-aloud task with linked-list and recursion explanations, and a semi-structured interview grounded in the Paul-Elder framework. They judged accuracy, clarity, engagement, and trust separately. Results: Most participants described interest-personalized analogies as more engaging or memorable than generic technical explanations, while trust was mixed. Some trusted the tailored analogies more; others scrutinized them more closely or distrusted the tailoring. Participants with deep source-domain knowledge identified structural flaws requiring that knowledge to recognize. Because personalization and explanation format differed together, these findings do not isolate an effect of personalization alone. Implications: A familiar source flips the student's role. On the concept they are still learners, but on the familiar source they are the expert, and that is the position from which an analogy can be judged. We call this two-sided analogy auditing. GenAI systems should ask what students know, not just what interests them, and treat a flawed analogy as something to inspect and fix rather than accept.
Comments12 pages, 2 figures, and 2 tables. Accepted to the 26th Koli Calling International Conference on Computing Education Research (Koli Calling 2026)
