ORIGINAL RESEARCH article
Front. Commun.
Sec. Visual Communication
This article is part of the Research TopicMotion, Meaning, and Machine: AI at the Core of Audiovisual ProductionView all articles
Generative AI and the Transformations of Visual Language: An Experimental Study of Audiovisual Production in Kuwait
Provisionally accepted
- 1Faculty of Mass Communication, Radio & TV Department, Yarmouk University-Jordan1, Irbid, Jordan
- 2Mass communications department - College of Arts, Kuwait University, Kuwait, Kuwait
- 3department of medicinal chemistry and pharmacognosy, faculty of pharmacy, yarmouk university, Irbid, Jordan
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Generative AI is transforming audiovisual production, yet empirical research remains limited in Gulf media ecosystems. 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 narrative structures or production workflows, alongside audience negotiations of credibility in relation to 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), capturing 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 findings reveal a sustained increase in AVI that is partially decoupled from pacing (SDI), 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. Keywords: Generative AI, Visual Language, Audiovisual Media, Algorithmic Aesthetics, Digital Storytelling, Cultural Diversity.
Keywords: algorithmic aesthetics, audiovisual media, Cultural Diversity, digital storytelling, Generative AI, visual language
Received: 29 Sep 2025; Accepted: 30 Jan 2026.
Copyright: © 2026 Qudah, A. Murad and Habes. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Mohammad Qudah
Mohammed Habes
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