2026/08/05 by Felicia Li Feng, Jian Zhao, Anamaria Crisan
Computer Science · #cs.HC
arxiv created 2026/08/05 · arxiv updated 2026/08/06
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.