探索主动式AI编程助手的利弊与设计权衡
Assistance or Disruption? Exploring and Evaluating the Design and Trade-offs of Proactive AI Programming Support
- 设计可主动介入的AI助手Codellaborator，基于编辑行为触发支持
- 主动助手提升效率但干扰流程，视觉提示和上下文信息减轻干扰
- 适合关注AI辅助编程体验与人机协作的研究者与开发者
AI编程工具具备强大的代码生成能力，近期原型尝试通过主动式AI代理减少用户负担，但其对编程流程的影响尚未明确。本文提出并评估了Codellaborator——一种基于编辑活动与任务上下文主动发起帮助的设计探针式LLM代理。通过三种界面变体（仅提示、主动代理、带存在感与上下文的主动代理）评估支持强度的权衡。在18名参与者的自身对照研究中发现，主动代理相比仅提示模式提升效率，但也带来工作流中断。然而，存在性提示与交互上下文支持缓解了中断，并增强了用户对AI过程的感知。研究揭示了Codellaborator在用户控制、代码归属感与理解力方面的权衡，强调需根据编程过程适配主动性。本研究为主动式AI系统的设计探索与评估提供支持，提出集成AI的编程工作流设计启示。
原文摘要 · Abstract (English)
AI programming tools enable powerful code generation, and recent prototypes attempt to reduce user effort with proactive AI agents, but their impact on programming workflows remains unexplored. We introduce and evaluate Codellaborator, a design probe LLM agent that initiates programming assistance based on editor activities and task context. We explored three interface variants to assess trade-offs between increasingly salient AI support: prompt-only, proactive agent, and proactive agent with presence and context (Codellaborator). In a within-subject study (N=18), we find that proactive agents increase efficiency compared to prompt-only paradigm, but also incur workflow disruptions. However, presence indicators and interaction context support alleviated disruptions and improved users' awareness of AI processes. We underscore trade-offs of Codellaborator on user control, ownership, and code understanding, emphasizing the need to adapt proactivity to programming processes. Our research contributes to the design exploration and evaluation of proactive AI systems, presenting design implications on AI-integrated programming workflow.
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