AI 中文总结
研究针对人机协同数据分析中意图对齐差等问题，提出含意图支架和提示时 linting 的 IntentLint 系统，经16名分析师验证可提升意图感知与策略反思，为相关设计提供启示。
AI 中文摘要
在人机协同数据分析中，随着分析工作快速推进，用于捕捉共同理解的制品往往变得不完整或难以解读，进而导致未被记录的假设、跨用户对齐不佳的意图、缺乏上下文的提示以及智能体的非预期行为。为应对这些挑战，我们提出了一种基于规则的协调层，包含意图支架（intent scaffolding）和提示时 linting 两种交互机制，可在人机协同数据分析过程中使分析意图明确且可操作。我们在 IntentLint 中实现了上述机制，该系统作为概念验证，可从共享笔记本中推断分析意图，将其表示为结构化、可编辑的规则，并对照共享规则检查用户的提示。IntentLint 帮助分析师外化并完善自身意图，主动检查提示是否存在潜在冲突。对16名数据分析师开展的研究显示，IntentLint 提升了对合作者意图的感知，促使对分析策略进行反思，为支持更对齐、更透明的人机协同数据分析提供了设计启示。
英文摘要
In human-AI collaborative data analysis, as analyses rapidly evolve, the artifacts meant to capture shared understanding often become incomplete or difficult to interpret, leading to undocumented assumptions, cross-user misaligned intent, context-poor prompts, and unwanted agent behaviors. To address these challenges, we introduce a rule-based coordination layer with two interaction mechanisms, intent scaffolding and prompt-time linting, that make analytic intent explicit and actionable during human-AI collaborative data analysis. We implement them in IntentLint, a proof-of-concept system that infers analytic intent from shared notebooks, represents it as structured, editable rules, and checks users' prompts against shared rules. IntentLint helps analysts externalize and refine their intent and proactively checks prompts for potential conflicts. A study with 16 data analysts shows that IntentLint improves awareness of collaborators' intent and encourages reflection on analytic strategies, and provides design implications for supporting more aligned and transparent human-AI collaborative data analysis.
