The Promise and Pitfalls of GenAI-Powered Mobile Apps: Perspectives from User Reviews
TL;DR
This study presents a comprehensive analysis of user reviews for GenAI-powered mobile apps, focusing on identifying both their issues and strengths, and derived 18 key findings that shed light on the rapid growth of GenAI-powered apps across various genres, the common issues leading to user dissatisfaction, and the features contributing to user satisfaction.
Abstract
Generative artificial intelligence (GenAI) is increasingly integrated into mobile apps, creating a rapidly growing category of GenAI-powered apps. Understanding the development trends, challenges, and benefits of these apps is critical for advancing their functionality and improving user experiences. User reviews provide invaluable insights into real-world usage patterns and potential issues. In this study, we present a comprehensive analysis of user reviews for GenAI-powered mobile apps, focusing on identifying both their issues and strengths. Our dataset comprises 13,934 GenAI-powered applications, encompassing a large-scale corpus of over one million GenAI-related user reviews. To extract meaningful insights, we utilized an LLM-augmented analytical framework to develop a comprehensive taxonomy organized across four key dimensions: Technical, User, Market, and GenAI. This taxonomy enabled us to categorize and analyze the reasons behind both positive and negative user feedback, offering a structured understanding of user experiences with GenAI-powered apps. Through the analysis of low-rated and high-rated reviews, we derived 18 key findings that shed light on the rapid growth of GenAI-powered apps across various genres, the common issues leading to user dissatisfaction, and the features contributing to user satisfaction. These findings provide actionable recommendations to optimize GenAI integration, performance, and user satisfaction in mobile development.