Metaphors in Voice User Interfaces: A Slippery Fish
Open accessAbstract
We explore a range of different metaphors used for Voice User Interfaces (VUIs) by designers, end-users, manufacturers, and researchers using a novel framework derived from semi-structured interviews and a literature review. We focus less on the well-established idea of metaphors as a way for interface designers to help novice users learn how to interact with novel technology, and more on other ways metaphors can be used. We find that metaphors people use are contextually fluid, can change with the mode of conversation, and can reveal differences in how people perceive VUIs compared to other devices. Not all metaphors are helpful, and some may be offensive. Analyzing this broader class of metaphors can help understand, perhaps even predict problems. Metaphor analysis can be a low-cost tool to inspire design creativity and facilitate complex discussions about sociotechnical issues, enabling us to spot potential opportunities and problems in the situated use of technologies.
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By incorporating different metaphors into AI systems, designers can create shared meanings that help users express nuanced views about, and experiences with, an AI agent.
“By incorporating different metaphors into AI systems, designers can create shared meanings that help users express nuanced views about, and experiences with, an AI agent [16].”
From Where Do I 'Add the Egg'?: Exploring Agency and Ownership in AI Creative Co-Writing Systems (2026) · cites this paper as [16]
The Conclusion (section 8; page unavailable) says metaphors can help people discuss what they want and do not want from future technologies, supporting the claim about expressing views. But section 6.2 notes that users’ metaphors may differ from those provided by designers, and section 2.3.2 warns that explicitly building metaphors into interfaces can be problematic. The paper does not establish that incorporating different metaphors into AI systems creates shared meanings. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe mental models evoked by a metaphorical comparison facilitate users' familiarization with a system by creating a set of expectations around its functionality.
“The mental models evoked by a metaphorical comparison facilitate users' familiarization with a system by creating a set of expectations around its functionality [16].”
From Where Do I 'Add the Egg'?: Exploring Agency and Ownership in AI Creative Co-Writing Systems (2026) · cites this paper as [16]
Users fluctuate between human and non-human metaphors seamlessly without cognitive dissonance.
“Moreover, users fluctuated between human and non-human metaphors seam-lessly without cognitive dissonance.”
From Examining Humanness as a Metaphor to Design Voice User Interfaces (2024) · cites this paper as [24]
Users employ a broad range of human metaphors and also non-human metaphors to describe VUIs.
“They found that users did employ a broad range of human metaphors to describe VUIs, but they also used non-human metaphors (e.g., tower, “Computer” from Star Trek, genie, encyclopedia).”
From Examining Humanness as a Metaphor to Design Voice User Interfaces (2024) · cites this paper as [24]
Desai & Twidale found a great imbalance between deployments of VUIs in human and non-human roles.
“Desai & Twidale [24], in their framework for metaphor contex-tualization, found a great imbalance between deployments of VUIs in human and non-human roles.”
From Examining Humanness as a Metaphor to Design Voice User Interfaces (2024) · cites this paper as [24]
One direct consequence of anthropomorphization is misplaced trust, which in turn can lead to over-reliance on probabilistic automation systems.
“One direct consequence of anthropomorphization is misplaced trust, which in turn can lead to over-reliance on probabilistic automation systems [1, 12, 13, 21, 43].”
From From "AI" to Probabilistic Automation: How Does Anthropomorphization of Technical Systems Descriptions Influence Trust? (2024) · cites this paper as [13]
When probabilistic automation is perceived as having advanced cognitive properties, users may overestimate its capabilities in areas not directly demonstrated.
“When probabilistic automation is perceived as having advanced cognitive properties, users may overestimate its capabilities in areas not directly demonstrated [1, 13].”
From From "AI" to Probabilistic Automation: How Does Anthropomorphization of Technical Systems Descriptions Influence Trust? (2024) · cites this paper as [13]
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- Authors
- Desai, S., & Twidale, M.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- Venue
- ACM Transactions on Computer-Human Interaction
- Published
- 2023-09-25
- Volume
- 30
- Issue
- 6
- Pages
- 1-37
- ISSN
- 1073-0516 (ISSN-L)
- 1557-7325
- OpenAlex
- W4384406040
- Cited by
- 42
- References
- 144
- Citations by year
- 2026: 16 · 2025: 13 · 2024: 13
- FWCI
- 7.26
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Transactions on Computer-Human Interaction (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- voice user interface, metaphor, user perception, end-user perspective, socio-technical challenges, design creativity, conversation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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