研究程序员用智能编程助手时注意力如何下降，提出改进设计方法
"I'm Not Reading All of That": Understanding Software Engineers' Level of Cognitive Engagement with Agentic Coding Assistants
- 通过观察程序员使用智能助手的全过程，发现认知投入随任务推进持续降低
- 现有助手缺乏促进反思、验证和意义建构的支持功能
- 建议引入更丰富的交互方式和强制思考机制，提升人机协作深度
过度依赖AI系统会削弱用户批判性思维，加剧思维惰性，这一风险在无需人工干预的自主型AI系统中尤为突出。在软件工程领域，自主编程助手（ACAs）正快速融入日常开发流程。由于程序员构建的系统部署于多样且高风险的真实场景中，这些助手不应仅作为自动执行任务的工具，而应成为支持人类推理与理解的「思维工具」。我们开展了一项探索性研究，考察程序员在使用ACA时的认知投入与意义建构过程。结果表明，随着任务推进，认知投入持续下降；当前的ACA设计在反思、验证和意义建构方面提供支持有限。基于此，我们识别出具体的设计机会，包括利用更丰富的交互模态和认知强制机制，以维持用户参与度并促进更深层次的思考。
原文摘要 · Abstract (English)
Over-reliance on AI systems can undermine users' critical thinking and promote complacency, a risk intensified by the emergence of agentic AI systems that operate with minimal human involvement. In software engineering, agentic coding assistants (ACAs) are rapidly becoming embedded in everyday development workflows. Since software engineers (SEs) create systems deployed across diverse and high-stakes real-world contexts, these assistants must function not merely as autonomous task performers but as Tools for Thought that actively support human reasoning and sensemaking. We conducted a formative study examining software engineers' cognitive engagement and sensemaking processes when working with an ACA. Our findings reveal that cognitive engagement consistently declines as tasks progress, and that current ACA designs provide limited affordances for reflection, verification, and meaning-making. Based on these findings, we identify concrete design opportunities leveraging richer interaction modalities and cognitive-forcing mechanisms to sustain engagement and promote deeper thinking in AI-assisted programming.
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