知识标记：一种无AI偏见的概念，用于编程课程设计
Knowledge Markers: An AI-Agnostic Concept for the Design of Programming Courses
AI总结：
本文提出知识标记，一种轻量级、无AI偏见的课程设计方法，通过标记学习单元的侧重领域，帮助课程设计更明确学习意图。
摘要：
生成式AI使学生能够快速生成合理代码。生成可运行代码不再可靠地反映理解程度。这在非计算机科学课程中尤为严重，因为时间限制使平衡概念基础和足够应用实践变得困难。AI导师、教育聊天机器人和代码辅助系统的研究报告了有用的但往往具体案例的发现，而学习理论仍过于抽象，无法直接指导课程设计。因此，教师缺乏一种简单、可重用的方法，以明确学习意图并将其转化为具体的教学结构和学生学习行为。本文贡献了知识标记，作为课程级别的轻量级、无AI偏见的操作化方法用于课程设计。标记通过学习单元的主要重点进行标签：（A）应用知识（实施），（S）结构知识（概念和心理模型），或（P）过程知识（系统方法、决策和验证）。我们展示了如何在开放教学 artifacts（交互式网站、PDF脚本和笔记本）中以细粒度嵌入标签，配以沟通元素和可选的AI使用指导。我们通过分析、重设计和描述性评估一个入门编程课程，使用从目录中导出的标记分布来演示该方法。本文是设计和artifact导向的，不声称有测量学习收益；实证评估是未来的工作。
英文摘要：
Generative AI enables students to produce plausible code quickly. Producing working code is therefore no longer a reliable indicator of understanding. This is particularly problematic in non-computer-science programmes, where time constraints make it hard to balance conceptual foundations with sufficient application practice. Empirical studies of AI tutors, educational chatbots, and code-assistance systems report useful but often case-specific findings, while learning theory remains too abstract to directly guide course design. As a result, instructors lack a simple, reusable way to make learning intent explicit and translate it into concrete teaching structures and student learning behaviour. This paper contributes knowledge markers as a lightweight, AI-agnostic, course-level operationalisation for course design. The markers label learning units by their primary emphasis: (A) Application knowledge (implementation), (S) Structure knowledge (concepts and mental models), or (P) Procedure knowledge (systematic methods, decision making, and verification). We show how the labels can be embedded at fine granularity in open teaching artifacts (interactive website, PDF script, and notebooks), paired with communication elements and optional AI-usage guidance. We demonstrate the approach by analysing, redesigning, and descriptively evaluating an introductory programming course using marker distributions derived from the table of contents. The paper is design- and artifact-oriented and does not claim measured learning gains; empirical evaluation is future work.
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。
