让编程助手主动教学，避免开发者技能退化。
Agents That Teach: Towards Designing Incidental Learning Back into AI-Assisted Software Development
- 设计六项原则，使AI助手在编码时自然触发学习机会。
- 提出多智能体系统SHIELD，利用推理过程发现教学时机。
- 适合关注长期能力成长的开发者与教育型AI研究者。
AI编程代理正迅速改变软件开发方式，开发者日益将大量编码任务交由自主代理完成以提升效率。然而，这种效率提升伴随着非正式学习的流失。开发者曾通过费力的问题解决积累隐性知识，这是工程能力发展的重要途径。过度依赖代理导致技能退化，形成类似技术债的‘知识债’——代理执行的变更逐渐超出开发者理解范围。本文认为，偶然学习不会自动回归，必须有意识地重新设计进人机交互中。我们提出六项设计原则，并构建了基于‘教学型代理’理念的多智能体系统SHIELD，利用代理自身的推理过程，在不打断开发流程的前提下，主动揭示上下文相关的、非核心任务的学习时刻。本工作展望了生产力与学习共存的开发环境。
原文摘要 · Abstract (English)
AI coding agents are rapidly reshaping how software is built, with developers increasingly delegating substantial coding tasks to autonomous agents in pursuit of higher productivity. While these gains are real, they come at the cost of incidental learning. Developers historically acquired informal knowledge through effortful problem-solving, and this has long shaped how software engineering expertise develops. However, with over-reliance on agentic coding, unpracticed skills could atrophy silently over time. As this learning pathway is short-circuited, developers risk silently accruing Knowledge Debt, a developer-level analogue of Technical Debt, where changes the agent executes that the developer cannot fully understand accrue over time. In this paper, we argue that incidental learning will not re-emerge on its own and must be consciously designed back into developer-agent interactions, and propose six design principles to guide such systems. We then present "SHIELD", a multi-agent system grounded in the notion of "agents that teach", that operationalizes these principles by leveraging the AI coding agent's own reasoning to surface contextual, out-of-band learning moments without disrupting developer flow. Through this work, we envision a path toward learning-aware development environments where productivity and learning are complementary, not competing.
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