Understanding the Challenges and Opportunities of Generative AI Apps: An Empirical Study
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arXiv
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| Format: | Preprint |
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2025
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| author | AlMulla, Buthayna Assi, Maram Hassan, Safwat |
| author_facet | AlMulla, Buthayna Assi, Maram Hassan, Safwat |
| contents | The release of ChatGPT in 2022 triggered a rapid surge in generative artificial intelligence mobile apps (Gen-AI apps). Despite widespread adoption, little is known about how end users perceive and evaluate these Gen-AI functionalities. 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 user concerns shift as users mature. Our findings enable researchers and developers to better leverage the capabilities of Gen-AI apps and address potential challenges. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_16453 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Understanding the Challenges and Opportunities of Generative AI Apps: An Empirical Study AlMulla, Buthayna Assi, Maram Hassan, Safwat Software Engineering The release of ChatGPT in 2022 triggered a rapid surge in generative artificial intelligence mobile apps (Gen-AI apps). Despite widespread adoption, little is known about how end users perceive and evaluate these Gen-AI functionalities. 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 user concerns shift as users mature. Our findings enable researchers and developers to better leverage the capabilities of Gen-AI apps and address potential challenges. |
| title | Understanding the Challenges and Opportunities of Generative AI Apps: An Empirical Study |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2506.16453 |