From Voice to Value: Leveraging AI to Enhance Spoken Online Reviews on the Go

Kavindu Ravishan, Dániel Szabó, Niels van Berkel, Aku Visuri, Chi-Lan Yang +2 more
12/6/2024
cs.HCcs.AI

No confident code implementation found for this paper.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

What Users Value and Critique: Large-Scale Analysis of User Feedback on AI-Powered Mobile Apps

85% match2025

Review-LLM: Harnessing Large Language Models for Personalized Review Generation

83% match2024

AI-Driven Review Systems: Evaluating LLMs in Scalable and Bias-Aware Academic Reviews

82% match2024

Self-Improving Customer Review Response Generation Based on LLMs

82% match2024

Can LLM feedback enhance review quality? A randomized study of 20K reviews at ICLR 2025

81% match1 repo362025

Few-Shot and Training-Free Review Generation via Conversational Prompting

80% match2025

Cite this paper

@article{ravishan2024from,
  title  = {From Voice to Value: Leveraging AI to Enhance Spoken Online Reviews on the Go},
  author = {Kavindu Ravishan and Dániel Szabó and Niels van Berkel and Aku Visuri and Chi-Lan Yang and Koji Yatani and Simo Hosio},
  year   = {2024},
  eprint = {2412.05445},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2412.05445v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2412.05445.svg)](https://codeofpaper.com/paper/2412.05445)

Discussion