Generative AI (GenAI) can generate working code with minimal effort, creating a tension in introductory programming: students need timely help, yet direct solutions invite copying and can short-circuit reasoning. To address this, we propose example-based scaffolding, where GenAI provides scaffold examples that match a target task's underlying reasoning pattern but differ in contexts to support analogical transfer while reducing copying. We contribute a two-dimensional taxonomy, design guidelines, and CodeExemplar, a prototype integrated with auto-graded tasks, with initial formative feedback from a classroom pilot and instructor interviews.

翻译：生成式AI（GenAI）能以最小努力生成可运行代码，这在入门编程教学中引发了一个矛盾：学生需要及时的帮助，但直接给出的解答易诱发抄袭，且可能阻断其推理过程。为解决此问题，我们提出基于示例的支架方法——GenAI提供的支架示例虽匹配目标任务的基础推理模式，但通过改变上下文情境来支持类比迁移，同时减少抄袭。我们贡献了一个二维分类体系、设计指南以及原型系统CodeExemplar（与自动评分任务集成），并通过课堂试点与教师访谈获得了初步的形成性反馈。