Eliciting and Analysing Users' Envisioned Dialogues with Perfect Voice Assistants
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Abstract (arXiv)
We present a dialogue elicitation study to assess how users envision conversations with a perfect voice assistant (VA). In an online survey, N=205 participants were prompted with everyday scenarios, and wrote the lines of both user and VA in dialogues that they imagined as perfect. We analysed the dialogues with text analytics and qualitative analysis, including number of words and turns, social aspects of conversation, implied VA capabilities, and the influence of user personality. The majority envisioned dialogues with a VA that is interactive and not purely functional; it is smart, proactive, and has knowledge about the user. Attitudes diverged regarding the assistant's role as well as it expressing humour and opinions. An exploratory analysis suggested a relationship with personality for these aspects, but correlations were low overall. We discuss implications for research and design of future VAs, underlining the vision of enabling conversational UIs, rather than single command "Q&As".
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Feb 2021.
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Citing papers per year: 2023: 3; 2024: 3; 2025: 1; 2026: 2.
9 papers.
- 2024CloChat: Understanding How People Customize, Interact, and Experience Personas in Large Language Modelscited by 19
- 2023Transcending the "Male Code": Implicit Masculine Biases in NLP Contextscited by 5
- 2023Rewriting the Script: Adapting Text Instructions for Voice Interactioncited by 5
- 2024Coimagining the Future of Voice Assistants with Cultural Sensitivitycited by 3
- 2026Not My Truce: Personality Differences in AI-Mediated Workplace Negotiationcited by 2
- 2023Digital Modeling for Everyone: Exploring How Novices Approach Voice-Based 3D Modelingcited by 1
- 2024LLM-Mediated Domain-Specific Voice Agents: The Case of TextileBotcited by 1
- 2025StepWrite: Adaptive Planning for Speech-Driven Text Generationcited by 1
- 2026Situated, Dynamic, and Subjective: Envisioning the Design of Theory-of-Mind-Enabled Everyday AI with Industry Practitionerscited by 1
- 2026Not My Truce: Personality Differences in AI-Mediated Workplace Negotiation1 Apr 2026
- 2026Situated, Dynamic, and Subjective: Envisioning the Design of Theory-of-Mind-Enabled Everyday AI with Industry Practitioners11 Feb 2026
- 2025StepWrite: Adaptive Planning for Speech-Driven Text Generation6 Aug 2025
- 2024LLM-Mediated Domain-Specific Voice Agents: The Case of TextileBot15 Jun 2024
- 2024Coimagining the Future of Voice Assistants with Cultural Sensitivity26 Mar 2024
- 2024CloChat: Understanding How People Customize, Interact, and Experience Personas in Large Language Models23 Feb 2024
- 2023Digital Modeling for Everyone: Exploring How Novices Approach Voice-Based 3D Modeling10 Jul 2023
- 2023Rewriting the Script: Adapting Text Instructions for Voice Interaction16 Jun 2023
- 2023Transcending the "Male Code": Implicit Masculine Biases in NLP Contexts22 Apr 2023