发表机构
Carleton University（卡尔顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文通过对14名IT从业者的半结构化访谈，揭示GenAI压缩系统管理传统专业能力路径、重置绩效预期引发双速文化与生产力愧疚感等意外影响，引发相关领域对专业能力发展等问题的思考。
AI 中文摘要
尽管行业讨论常强调即时生产力提升，并将生成式AI（GenAI）主要视为自动化工具，但其融入系统管理领域可能带来尚未被充分理解的职业实践深层转变。本文基于对14名IT专业人员的半结构化访谈，探究GenAI融入故障排查、脚本编写及系统验证日常工作的实际情况。通过归纳主题分析，我们发现两项意外的社会技术发现：其一，存在“传统专业能力路径压缩”现象，GenAI兼具类似导师的指导功能与“缩短进阶阶梯”的工具属性，虽能支持从业者在陌生领域更快完成任务，但可能减少其接触基础实操环节的机会——这些环节（构建、试错与调试）曾是技术专业能力培养的核心途径；其二，存在“绩效感知转变”，AI辅助工作的速度开始重置组织与个人对生产力的预期，可能在团队中形成“双速文化”，并引发“生产力愧疚感”：即便安全或验证所需的必要手动工作，也被逐渐视为效率低下或能力不足的表现。研究结果引发了关于GenAI如何影响专业能力发展、高风险技术环境中职业价值评估方式，以及复杂技术环境下人类判断作用的更广泛问题。
英文摘要
While industry discourse often emphasizes immediate productivity gains and frames GenAI primarily as a tool for automation, the integration of GenAI into system administration may involve deeper shifts in professional practice that are not yet fully understood. Drawing on 14 semi-structured interviews with IT professionals, this paper explores the lived reality of embedding GenAI into daily routines of troubleshooting, scripting, and system verification. Through inductive thematic analysis, we uncover two unanticipated socio-technical findings. First, we describe a "compression of traditional expertise pathways" where GenAI appears to function as both a mentor-like tutor and a "ladder-shortening" tool. While the tool can support faster task performance in unfamiliar domains, our findings suggest it may also reduce a practitioner's exposure to the foundational, hands-on cycles of building, failing, and debugging that historically served as the training ground for technical expertise. Second, we describe a "performance perception shift," where the speed of AI-assisted work begins to reset organizational and self-expectations for productivity. This shift may create a "two-speed culture" within teams and introduce "productivity guilt," as necessary manual work, even when required for safety or validation, is increasingly perceived as slow or a failure of efficiency. Our results raise broader questions about how GenAI may influence expertise development, how professional value is assessed in high-stakes technical environments, and the role of human judgment in complex technical environments.
