Understanding the Challenges and Opportunities of Generative AI Apps: An Empirical Study

[2506.16453] Understanding the Challenges and Opportunities of Generative AI Apps: An Empirical Study Skip to main content Search Submit Donate Log in Computer Science > Software Engineering arXiv:2506.16453 (cs) [Submitted on 19 Jun 2025 ( v1 ), last revised 27 Aug 2026 (this version, v5)] Title: Understanding the Challenges and Opportunities of Generative AI Apps: An Empirical Study Authors: Buthayna AlMulla , Maram Assi , Safwat Hassan View PDF HTML (experimental) Abstract: Generative AI (Gen-AI) is increasingly integrated into mobile applications (apps), introducing new capabilities while also creating new challenges for users. However, despite their growing adoption, we lack an ecosystem-level understanding of the experiences, opportunities, and challenges users report across Gen-AI mobile apps. We conduct a user-centered analysis of 1,035,342 reviews from 171 Gen-AI apps from the Google Play Store. We propose SARA (Selection, Acquisition, Refinement, and Analysis), a four-phase framework that leverages prompt-based LLMs for large-scale review analysis. We validate the reliability of LLM-based topic extraction and assignment using 4,353 manually evaluated reviews, achieving 91% accuracy with five-shot prompting and filtering of non-informative reviews. We identify the top ten topics (e.g., AI Performance and Emotional Connection) and perform a cross-platform comparison with Apple App Store reviews. Through qualitative analysis of 762 reviews, we uncover three opportunities (AI for Accessibility and Wellbeing, AI as a Collaborative Creative Tool, and AI Versatility) and three challenges (Managing User Expectations and AI Limitations, Balancing Content Moderation and Creative Freedom, and Strategic Integration of Gen-AI Features). Finally, we analyze temporal trends, revealing how the topics discussed and their evaluations change over time, including changing concerns around emotional connection and content moderation. Comments: 47 pages, 13 figures, 13 tables Subjects: Software Engineering (cs.SE) Cite as: arXiv:2506.16453 [cs.SE] (or arXiv:2506.16453v5 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2506.16453 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Buthayna AlMulla [ view email ] [v1] Thu, 19 Jun 2025 16:39:55 UTC (5,066 KB) [v2] Sat, 28 Jun 2025 04:39:35 UTC (5,064 KB) [v3] Fri, 5 Dec 2025 19:40:01 UTC (5,609 KB) [v4] Wed, 29 Apr 2026 16:14:48 UTC (3,164 KB) [v5] Thu, 27 Aug 2026 01:21:12 UTC (3,173 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: cs.SE < prev | next > new | recent | 2025-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation 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? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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