Toward Metaphor-Fluid Conversation Design for Voice User Interfaces

2025-02-17

AI Summary

Contemporary voice user interfaces (VUIs) predominantly employ static, monolithic “assistant persona” metaphors, limiting adaptability to diverse conversational contexts and user needs—resulting in insufficient flexibility and personalization. To address this, we propose the “Metaphor Flow” design paradigm: the first framework enabling dynamic metaphor mapping conditioned on four context types—command execution, information seeking, social interaction, and error recovery. Our approach formalizes metaphors along two orthogonal dimensions—formality and hierarchical authority—and supports real-time, preference-driven adaptation, evaluated via a rigorous A/B testing framework. A controlled study with 91 participants demonstrated statistically significant improvements in user adoption intention, perceived enjoyment, and preference. Furthermore, a preliminary investigation with 130 participants confirmed statistically significant differences in metaphor preferences across the four contextual categories. This work advances VUI design by grounding persona adaptation in empirically validated, context-sensitive, and user-centered principles.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Metaphors play a critical role in shaping user experiences with Voice User Interfaces (VUIs), yet existing designs often rely on static, human-centric metaphors that fail to adapt to diverse contexts and user needs. This paper introduces Metaphor-Fluid Design, a novel approach that dynamically adjusts metaphorical representations based on conversational use-contexts. We compare this approach to a Default VUI, which characterizes the present implementation of commercial VUIs commonly designed around the persona of an assistant, offering a uniform interaction style across contexts. In Study 1 (N=130), metaphors were mapped to four key use-contexts-commands, information seeking, sociality, and error recovery-along the dimensions of formality and hierarchy, revealing distinct preferences for task-specific metaphorical designs. Study 2 (N=91) evaluates a Metaphor-Fluid VUI against a Default VUI, showing that the Metaphor-Fluid VUI enhances perceived intention to adopt, enjoyment, and likability by aligning better with user expectations for different contexts. However, individual differences in metaphor preferences highlight the need for personalization. These findings challenge the one-size-fits-all paradigm of VUI design and demonstrate the potential of Metaphor-Fluid Design to create more adaptive and engaging human-AI interactions.

Problem

• Adapt static metaphors in VUIs
• Enhance user experience with context-aware designs
• Personalize metaphor preferences for diverse users

Innovation

• Dynamic metaphorical representation adjustment
• Context-specific metaphor mapping
• Enhanced user adoption and enjoyment