[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84884-en":3,"doc-seo-84884-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},84884,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",6,"Technology","Agents That Teach: Designing Incidental Learning Back into AI-Assisted Software Development","AI coding agents are transforming software development by enabling developers to delegate substantial coding tasks to autonomous systems, boosting productivity through tools that support debugging, refactoring, review, and documentation. Despite these measurable gains, incidental learning is reduced when developers stop performing effortful problem solving, risking silent skill atrophy over time. The paper introduces Knowledge Debt as a developer-level analogue of Technical Debt and argues for consciously designed developer-agent interactions. It proposes six design principles and presents SHIELD, a learning-aware multi-agent system that surfaces contextual out-of-band moments without disrupting flow.","Agents That Teach: Towards Designing Incidental Learning Back into AI-Assisted Software Development  \nRohit Mehra†, Samdyuti Suri†, Prithviraj K Tagadinamani†, Kapil Singi†, Vikrant Kaulgud†, Adam P.  \nBurden*  \n†Accenture Labs, India *Accenture, USA  \n{rohit.a.mehra,samdyuti.suri,p.k.tagadinamani,kapil.singi,vikrant.kaulgud, [adam.p.burden}@accenture.com](adam.p.burden}@accenture.com)  \narXiv :2607 .06 10 1v 1 [ cs . SE] 7 Jul 2026  \nAbstract  \nAI 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.  \nKeywords  \nAI-assisted software development, knowledge debt, incidental learning, cognitive offloading, skill atrophy  \n1 Introduction  \nAI coding agents have moved from experimental tools to core components of modern software engineering workflows. Tools such as Claude Code, GitHub Copilot, and Cursor are now used not only for code generation, but also for critical activities such as debugging, refactoring, code review, and documentation, increasingly through autonomous, multi-step workflows [3, 8, 12]. According to industry reports, approximately 42% of all committed code is AI-generated or AI-assisted, with this share projected to reach 65% by 2027 [28]. This shift has translated into measurable productivity gains for developers, with recent studies documenting improvements in task completion rates and per-task development velocity in AI-assisted settings [7, 25] . Yet, despite these gains, a more subtle but consequential shift is underway in how developers engage with these agents, one that suggests that while problems are increasingly solved, understanding/learning may not always follow.  \nWhile developers benefit from increased productivity and speed, this shift reflects a recurring pattern in human-tool interaction. As  \nhumans increasingly offload cognitive tasks to tools, the underlying capability tends to diminish over time. For example, sustained reliance on GPS has been linked to decline in spatial memory and navigation ability, spell check and autocorrect to weakened spelling ability, and smartphones to reduced memory recall ability [2, 9, 13] . In each case, the tool absorbs the effort, and the human reduces active engagement with the underlying skill, leading to its gradual decline. AI coding agents appear to be following a similar trajectory, with emerging evidence suggesting erosion of core software engineering capabilities such as conceptual understanding, code reading, and debugging [5, 27] . This reflects a broader pattern of skill atrophy, where unpracticed capabilities do not simply remain dormant, but gradually deteriorate.  \nBefore AI coding agents or assistants, a developer encountering a problem in code would s","cbCaivgueAtQsQi2","https://ap.wps.com/l/cbCaivgueAtQsQi2","pdf",2858697,1,5,"English","en",105,"# Introduction\n## Human-to-tool offloading and skill atrophy\n## Incidental learning and Knowledge Debt\n# Design principles and SHIELD system","[{\"question\":\"What problem does the paper identify with AI coding agents?\",\"answer\":\"It argues that increased reliance on agentic coding can suppress incidental learning, leading to gradual skill atrophy even when productivity improves in the short term.\"},{\"question\":\"What is “Knowledge Debt” in the paper?\",\"answer\":\"Knowledge Debt is described as a developer-level analogue of Technical Debt, where agent-executed changes the developer cannot fully understand accumulate over time.\"},{\"question\":\"How 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