Interface Framework for Human-AI Collaboration within Intelligent User Interface Ecosystems

TL;DR

This paper introduces a dimensional framework for designing human-AI interfaces that adapt to user needs and task complexity, aiming to improve scalability and user control in AI-powered systems.

Contribution

It proposes a novel framework based on workflow complexity, AI autonomy, and reasoning, developed through co-design and qualitative research, to guide scalable AI interface design.

Findings

Identified task-to-interface relationships.

Highlighted importance of business impact and security risk.

Validated framework through user research.

Abstract

As interfaces evolve from static user pathways to dynamic human-AI collaboration, no standard methods exist for selecting appropriate interface patterns based on user needs and task complexity. Existing frameworks only provide guiding principles for designing AI agent capabilities. We propose a dimensional framework based on workflow complexity, AI autonomy, and AI reasoning to guide the design of context-aware, scalable AI interfaces aka modalities (e.g., prompt bars, split screens, full screens, etc.). The framework was developed through co-design workshops with designers of marketing products and refined through qualitative research with eight long-term AI users. The study evaluated the three dimensions, identified task-to-interface relationships, and surfaced the importance of both business impact and security risk across all high-autonomy scenarios. This framework provides product teams with a shared language to develop scalable AI interfaces, emphasizing fluidity between interfaces and progressive user control to balance AI autonomy with human oversight.