AI coding assistants have proliferated rapidly, yet structured pedagogical frameworks for learning these tools remain scarce. Developers face a gap between tool documentation and practical mastery, relying on fragmented resources such as blog posts, video tutorials, and trial-and-error. We present cc-self-train, a modular interactive curriculum for learning Claude Code, an agentic AI coding tool, through hands-on project construction. The system introduces five contributions: (1) a persona progression model that adapts instructor tone across four stages (Guide, Collaborator, Peer, Launcher), operationalizing Gradual Release of Responsibility for AI-mediated instruction; (2) an adaptive learning system that observes engagement quality through hook-based heuristics and adjusts scaffolding at two timescales, using streak detection for mid-module intervention and aggregate metrics for module-boundary persona changes; (3) a cross-domain unified curriculum in which five distinct project domains share identical feature sequencing, enabling transfer learning; (4) a step-pacing mechanism with explicit pause primitives to manage information overload in an AI-as-instructor context; and (5) an auto-updating curriculum design in which the onboarding agent detects upstream tool changes and updates teaching materials before instruction begins. A parametrized test suite enforces structural consistency as a proxy for pedagogical invariants across all 50 modules. A pilot evaluation with 27 participants shows statistically significant reported self-efficacy gains across all 10 assessed skill areas (p < 0.001), with the largest effects on advanced features such as hooks and custom skills. We discuss implications for the design of auto-updating educational systems.

翻译：AI编程助手迅速普及，但在学习这些工具的结构化教学框架方面仍然匮乏。开发者面临着工具文档与实际掌握之间的差距，他们依赖博客文章、视频教程和试错等零散资料。我们提出cc-self-train，一个模块化的交互式课程，通过动手项目构建来学习Claude Code——一种智能体式AI编程工具。该系统包含五项贡献：(1) 角色渐进模型，它通过四个阶段（引导者、协作者、同伴、启动者）调整教师语气，将“责任逐渐释放”理念应用于AI辅助教学；(2) 自适应学习系统，通过基于钩子的启发式方法观察学习投入质量，并在两个时间尺度上调整支架，利用连续行为检测进行模块内干预，以及使用聚合指标进行模块边界的角色变更；(3) 跨领域统一课程，其中五个不同的项目领域共享相同的功能序列，实现迁移学习；(4) 步进节奏机制，带有显式暂停原语，以管理AI作为教师场景中的信息过载；以及(5) 自动更新的课程设计，其中引导机器人在教学开始前检测上游工具变化并更新教学材料。一个参数化测试套件强制执行结构一致性，作为所有50个模块的教育学不变量的代理。一项包含27名参与者的试点评估显示，所有10个评估技能领域均报告了统计显著的自信心提升（p < 0.001），其中对钩子和自定义技能等高级功能的影响最大。我们讨论了自动更新教育系统设计的启示。