Generative AI and the transformations of visual language: an experimental study of audiovisual production in Kuwait

Generative artificial intelligence (AI) is transforming audiovisual production, yet empirical research on Gulf media ecosystems is limited. This study examines how generative AI reconfigures visual language in the Kuwaiti context, conceptualized as shifts in aesthetic conventions, stylistic patterning, and symbolic repertoires of audiovisual materials, rather than in narrative structures or production workflows, alongside audience negotiations of credibility regarding synthetic media. We constructed a multi-layered corpus of publicly accessible videos, audience comments, and metadata drawn from Kuwaiti audiovisual platforms and applied a Python-based computational research design. Visual change was operationalized using the Shot Dynamics Index (SDI), which captures pacing and editing rhythms, and the AI-Visual Index (AVI), measuring the prevalence of AI-associated visual cues. These visual measures were integrated with audience discourse analysis, including a Credibility Lexicon Score (CLS), topic modeling, sentiment analysis, and network-based diffusion, community, and centrality metrics. Descriptive fixed-effects models link these analytical layers without making causal claims and are supported by extensive robustness checks. The results showed that a sustained increase in AVI, partially decoupled from pacing (SDI), was accompanied by intensified verification discourse (higher CLS) and clustering around AI-tagged content within central network hubs and cross-platform bridging nodes. The study contributes cross-cultural evidence on algorithmic aesthetics and advances transparent, transferable measurement frameworks, highlighting provenance labeling and dialect-aware NLP as viable mechanisms for supporting credibility in AI-mediated audiovisual environments.