The Governance-Experience Gap in Generative AI: A Cross-Level Analysis of Regulatory Frameworks and Interactional Frictions

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Generative artificial intelligence (AI) is rapidly integrating into daily life, while global governance frameworks such as the European Union AI Act, the National Institute of Standards and Technology AI Risk Management Framework, and the Organisation for Economic Co-operation and Development Principles seek to ensure trustworthy AI. This study examines whether these system-centric frameworks align with interactional frictions experienced by end-users. Using a dual-track framework, we derived cross-framework governance dimensions through large language model-assisted semantic harmonization and analyzed mobile application reviews across major generative AI platforms using transformer-based topic modeling. Results indicate a governance–experience gap, with 36.6% of user-reported breakdowns showing direct or partial correspondence with formal governance dimensions. In contrast, 61.0% reflected concerns beyond existing categories, while 2.4% involved tensions around safety enforcement. The findings suggest that AI governance may benefit from incorporating user-centered quality metrics alongside conventional risk-management and adversarial testing approaches.