Agents That Teach: Towards Designing Incidental Learning Back into AI-Assisted Software Development
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
CAPED: Context-Aware Privacy Exposure Defense for Mobile GUI Agents
Screenshot-based mobile GUI agents can operate ordinary smartphone apps through the same visual interface as a human user, but this capability also turns every screen observation into a privacy boundary. During normal ta…
Mitigating the Effect of Incidental Correlations on Part-based Learning
Intelligent systems possess a crucial characteristic of breaking complicated problems into smaller reusable components or parts and adjusting to new tasks using these part representations. However, current part-learners …
DataEnvGym: Data Generation Agents in Teacher Environments with Student Feedback
The process of creating training data to teach models is currently driven by humans, who manually analyze model weaknesses and plan how to create data that improves a student model. Approaches using LLMs as annotators re…
MathSequential Decision MakingVisual Question Answering (VQA)Reflecting in the Reflection: Integrating a Socratic Questioning Framework into Automated AI-Based Question Generation
Designing good reflection questions is pedagogically important but time-consuming and unevenly supported across teachers. This paper introduces a reflection-in-reflection framework for automated generation of reflection …
Question GenerationMeasuring Interventional Robustness in Reinforcement Learning
Recent work in reinforcement learning has focused on several characteristics of learned policies that go beyond maximizing reward. These properties include fairness, explainability, generalization, and robustness. In thi…
Fairnessreinforcement-learningReinforcement LearningReinforcement Learning (RL)