IntentLint: Supporting Intent Scaffolding and Prompt-time Linting in Human-AI Collaborative Data Analysis

2026-08-04

AI Summary

This work addresses the challenges in human-AI collaborative data analysis, where rapidly evolving analytical processes often undermine shared understanding, leading to undocumented assumptions, misaligned intentions, and context-poor prompts. To mitigate these issues, the authors propose a rule-based coordination layer that explicitly models user intent as editable, structured rules and validates their consistency with shared intent in real time during prompt formulation. By integrating rule-based reasoning, notebook parsing, and structured intent representation, the approach externalizes user intentions and provides early warnings of potential conflicts. A user study demonstrates that the system significantly enhances analysts’ awareness of their collaborators’ intentions and encourages reflection on analytical strategies, offering a novel design paradigm for human-AI collaborative data analysis.

Abstract

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.

Problem

• human-AI collaboration
• data analysis
• intent alignment
• prompt interpretation
• shared understanding

Innovation

• intent scaffolding
• prompt-time linting
• analytic intent
• human-AI collaboration
• rule-based coordination