从语音到价值：利用 AI 增强移动场景下的口述在线评论

Computer Science > Human-Computer Interaction arXiv:2412.05445 (cs) [Submitted on 6 Dec 2024 (v1), last revised 14 Sep 2026 (this version, v3)] Title:From Voice to Value: Leveraging AI to Enhance Spoken Online Reviews on the Go Authors:Kavindu Perera, Dániel Szabó, Niels van Berkel, Aku Visuri, Chi-Lan Yang, Koji Yatani, Simo Hosio View a PDF of the paper titled From Voice to Value: Leveraging AI to Enhance Spoken Online Reviews on the Go, by Kavindu Perera and 6 other authors View PDF HTML (experimental) Abstract:Online reviews help people make better decisions. Review platforms usually depend on typed input, where leaving a good review requires significant effort because users must carefully organize and articulate their thoughts. This may discourage users from leaving comprehensive and high-quality reviews, especially when they are on the go. To address this challenge, we developed Vocalizer, a mobile application that enables users to provide reviews through voice input, with enhancements from a large language model (LLM). In a longitudinal study, we analysed user interactions with the app, focusing on AI-driven features that help refine and improve reviews. Our findings show that users frequently utilized the AI agent to add more detailed information to their reviews. We also show how interactive AI features can improve users self-efficacy and willingness to share reviews online. Finally, we discuss the opportunities and challenges of integrating AI assistance into review-writing systems. Comments: Subjects: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI) Cite as: arXiv:2412.05445 [cs.HC] (or arXiv:2412.05445v3 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2412.05445 Focus to learn more arXiv-issued DOI via DataCite Journal reference: International Conference on Mobile and Ubiquitous Multimedia MUM '24, December 1-4, 2024, Stockholm, Sweden Related DOI: https://doi.org/10.1145/3701571.3701593 Focus to learn more DOI(s) linking to related resources Submission history From: Kavindu Perera [view email] [v1] Fri, 6 Dec 2024 21:59:47 UTC (6,383 KB) [v2] Tue, 10 Dec 2024 19:31:29 UTC (6,383 KB) [v3] Mon, 14 Sep 2026 11:40:36 UTC (2,558 KB) Full-text links: Access Paper: View a PDF of the paper titled From Voice to Value: Leveraging AI to Enhance Spoken Online Reviews on the Go, by Kavindu Perera and 6 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.HC new | recent | 2024-12 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)