AI 中文总结
该研究通过范围审查和定性综合，对90个基于大语言模型的编程支持系统进行分析，提出PEA三维分析视角，贡献治理手册与配置图，为分析现有系统及设计未来工具提供词汇表，以解决辅助治理决策隐含难比较的问题。
AI 中文摘要
随着大语言模型（LLMs）融入编程教育，面向学习者的系统在辅助的界定、实施和控制方式上日益不同。这些治理决策通常是隐含描述的，难以以具有教育意义的方式比较系统。为填补这一空白，我们对计算机科学教育中90个基于大语言模型的编程支持系统进行了范围审查和定性综合。我们通过三个维度分析辅助治理，统称为PEA：政策，涵盖允许或限制的帮助形式；执行，涵盖如何通过交互和系统行为实现这些界限；权威，涵盖在使用过程中谁可以配置、调整或覆盖它们。我们的研究结果表明，系统通常有相似的教学目标，但通过不同的执行机制来实现这些目标。同时，权威在系统逻辑中高度集中，较少系统给予学习者或教师运行时控制权。这项工作贡献了PEA作为一个三维分析视角、在这些维度内实证完善的治理手册，以及当前基于大语言模型的编程支持设计空间中未充分探索的配置图。通过使这些明确且可比较，PEA为分析现有系统和设计具有教学界限、可配置且可问责的未来工具提供了一个词汇表。
英文摘要
As large language models (LLMs) become integrated into programming education, learner-facing systems increasingly differ in how that assistance is bounded, enacted, and controlled. These governance decisions are often described implicitly, making it difficult to compare systems in educationally meaningful ways. To address this gap, we conduct a scoping review and qualitative synthesis of 90 peer-reviewed LLM-based programming support systems in CS education. We analyze assistance governance through three dimensions, which we refer to collectively as PEA: Policy, capturing what forms of help are allowed or restricted; Enforcement, capturing how those boundaries are operationalized through interaction and system behavior; and Authority, capturing who can configure, adapt, or override them during use. Our findings show that systems often share similar pedagogical goals, but implement those goals through varied enforcement mechanisms. At the same time, authority remains highly centralized in system logic, with fewer systems giving learners or instructors runtime control. This work contributes PEA as a three-dimensional analytic lens, a governance codebook empirically refined within these dimensions, and a map of underexplored configurations in the current design space of LLM-based programming support. By making these explicit and comparable, PEA offers a vocabulary for analyzing existing systems and designing future tools that are pedagogically bounded, configurable, and accountable.
