IntentFlow: Investigating Fluid Dynamics of Intent Communication in Generative AI

TL;DR

This paper explores how users communicate and refine their intentions with generative AI, proposing a comprehensive framework and a prototype to improve understanding, control, and workflow in intent-based interactions.

Contribution

It identifies four key aspects of intent communication and introduces IntentFlow, a prototype that supports these aspects to enhance user experience in AI interactions.

Findings

Support for intent refinement reduces cognitive effort.

Comprehensive intent support improves user control.

Intent communication is a dynamic, iterative process.

Abstract

Generative AI shifts interaction toward intent-based outcome specification, despite user intents being inherently vague, fluid, and evolving. While a growing body of HCI research has proposed diverse interaction techniques to support this process, there is limited understanding of what the key aspects of intent communication are and how they interplay to shape users' workflows. To bridge this gap, we first conduct a systematic literature review of 46 HCI papers and identify four core aspects of intent communication support: intent articulation, exploration, management, and synchronization. To investigate how these aspects interplay in practice, we developed IntentFlow, a research probe that embodies all four aspects for a writing task, and conducted a comparative study (N=12). Our action-level behavioral analysis reveals that comprehensive support enables verification-driven refinement and progressive intent curation, reduces cognitive effort, and improves users' sense of control and understanding of intent-output alignment. We conclude with design implications for building generative AI systems that support intent communication as a dynamic, iterative process.