🤖 AI Summary
This study addresses the absence of a systematic approach for selecting appropriate human-AI interaction interfaces based on user needs and task complexity. It proposes a three-dimensional classification framework grounded in workflow complexity, AI autonomy, and AI reasoning capability, developed and validated through co-design workshops and longitudinal qualitative user studies. The work establishes, for the first time, a mapping between task complexity and interface modalities, offering actionable guidance for designing context-aware, scalable AI interfaces that support seamless transitions between modalities and enable users to exercise progressive control. The framework also balances operational impact and safety risks in high-autonomy scenarios. By providing product teams with a shared conceptual language, this research enhances the ability to achieve an effective balance between human oversight and AI autonomy in collaborative settings.
📝 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.