The study, published in Frontiers in Communication by Areej Albawardi of Imam Abdulrahman Bin Faisal University and Alyaa Olwi of King Abdulaziz University, is the first to trace the full remediation chain — original advertisement to AI-generated text to AI-generated image — through the lens of multimodal discourse analysis. The researchers selected six advertisements from Almarai, one of the largest dairy companies in Saudi Arabia and among the most valuable companies in the Middle East, recently posted on the company’s official Instagram page with more than ten thousand views each. The ads were chosen precisely because they carry a rich variety of Saudi cultural and social markers: traditional dress, family configurations, gender and age representation, and distinctly Saudi settings.

The experimental design was deceptively simple but technically revealing. Seven AI models — ChatGPT, Gemini, Meta AI, Grok, Qwen, Leonardo AI and Recraft V3 — were each given the same instruction, “turn it into a text prompt,” producing forty-two textual descriptions. Then, using fresh accounts so the models would not rely on stored memory or context, the researchers fed those generated prompts back with the instruction “create an image.” Because Google and Leonardo AI each operate two image-generation submodels — Google Imagen 4 and Gemini 2.5 Flash (Nano Banana), and Leonardo’s Phoenix 1.0 and Lucid Origin — the image stage involved nine generators, yielding fifty-four images. Data collection took place between October 1 and November 1, 2025, using models ranked on Artificial Analysis’ Text-to-Image Leaderboard for accessibility and performance.

To ensure the qualitative judgments had a reliable empirical basis, the two authors independently coded each output against a shared framework, recording whether each model, advertisement and semiotic feature fulfilled a criterion. The coding produced 906 paired decisions — 420 for the image-to-text stage and 486 for the text-to-image stage. Observed agreement reached 91.94 percent, and Cohen’s kappa, which corrects for chance agreement, was 0.8319 — within the “almost perfect” band of the Landis and Koch categories. Alternative coefficients, including Krippendorff’s alpha, Scott’s pi and Gwet’s AC1, all exceeded 0.82, confirming that the coding framework was applied consistently in both directions of transformation.

The analytical framework drew on social semiotic technology, an extension of multimodal discourse analysis rooted in Halliday’s socio-semiotic approach and Kress and Van Leeuwen’s grammar of visual design. Rather than treating AI as a neutral tool, the researchers viewed the models as semiotic technologies that actively shape meaning — echoing Lucy Suchman’s observation that AI works as a “disclosing agent for assumptions about the human.” The analysis identified recurring semiotic resources across the data: color, setting and location, people, action, cultural artifacts, language, intertextuality, logos, and modality, the degree of realism an image communicates through lighting, saturation and filters.

The results split cleanly along a technical fault line. Features that are visually salient and semantically explicit — color, people, action, setting — were transferred with high fidelity. Eight of the nine image generators produced correct colors, and most models accurately rendered the number, gender and activities of the people described in the prompts. Action verbs such as “placing,” “smiling” and “handing,” enriched with adverbs like “gently,” translated reliably into the final images. But features that are implicit, layered or linguistically encoded collapsed in transit. Camera angle and gaze direction were rarely described in the prompts, so generated images showed people looking the wrong way. Screen layering — the advertisement’s graphic plane superimposed on the photographic scene — was misread by several models, which rendered the Almarai sign as if it were physically hanging inside a supermarket rather than added as a logo.

Cultural representation proved the most consequential failure. The researchers found that AI models operate on concentric geographic circles — Muslim, Arab, Middle Eastern, Gulf, Saudi — and dress is represented or misrepresented according to the word chosen. Traditional Saudi clothing in the original advertisements was frequently replaced with Gulf, Palestinian or Pakistani garments. One model described “checkered keffiyehs” as “traditional Saudi Arabian attire,” although that specific garment belongs to Palestinian culture — a contradiction the authors link to what Zoe Hurley has called “image hallucination,” a distortion of reality in which cultural norms are misrepresented. In one generated image, a woman wears a red-and-white shemagh, a headpiece normally worn by men. Race was inferred from dress alone, so when the prompts lacked explicit racial description, models substituted Asian features for Saudi ones.

Language was an equally stark marker of the technology’s limits. Arabic script — the brand name in the logo and the Arabic slogan — was the lowest-scoring feature across all models. Most generators either removed the Arabic text entirely or filled the linguistic void with meaningless gibberish. Only ChatGPT correctly included Arabic textual elements in nearly all of its generated images, and only Qwen3-Max provided an accurate translation of the Arabic text while identifying the language as Arabic. Logos fared little better: none of the nine image generators reproduced the Almarai logo correctly, and several omitted it even when the prompt described it. Notably, Qwen3-Max produced the best textual prompts — averaging 159 to 303 words, rich in cultural and intertextual detail — yet failed to generate accurate images, while ChatGPT, whose prompts lacked detail, produced the best images, possibly because the model stores the input image in its database or draws on additional internet input.

The study also introduces a new conceptual tool: “text hallucination,” derived from Hurley’s taxonomy of image hallucination. Where image hallucination distorts visual reality, text hallucination describes inaccurate textual descriptions of objects or actions in uploaded images — as when one model described a man “holding a hard yellow hat” that does not exist in the original advertisement, which then materialized in the generated image. The authors identify three types: textual, cultural-perspective, and spatial, the last involving misplaced objects and anatomically distorted faces. Hallucinations appeared in the majority of models even when the textual prompts contained no misinformation, suggesting that the image generators themselves add, alter or misrepresent visual content. Only ChatGPT and Google Imagen produced images without any hallucinations.

The implications reach beyond the laboratory. Generative AI is projected to become a 1.3 trillion dollar market within a decade, and advertising agencies are already deploying AI-generated imagery in campaigns for brands like Casablanca, Valentino and Moncler. Advertisements do not merely communicate marketing messages; they create cultural experiences that carry social and ideological perspectives. In a market like Saudi Arabia, where companies deliberately ensure that advertising adheres to local traditions and represents culture accurately, the technology’s inability to render Arabic script, correct logos and authentic cultural dress is not a cosmetic flaw but a functional barrier. The authors argue that their findings carry weight for theories of multimodality and culture, and practical weight for AI developers, who must pay closer attention to scripts, languages and cultural specificity. As the models continue to evolve, the study offers a benchmark: fluency in color and composition is no longer enough. The next frontier for generative AI is cultural precision — the ability to see, and to show, the difference between an Arab advertisement and a Saudi one.

Subject of Research: AI and multimodal communication

Article Title: Cultural and multimodal realization in various AI models: text to image, image to text

Article References: Albawardi, A., & Olwi, A. (2026). Cultural and multimodal realization in various AI models: text to image, image to text. Frontiers in Communication, 11, Article 1959385. https://doi.org/10.3389/fcomm.2026.1959385

Image Credits: AI Generated

DOI: 10.3389/fcomm.2026.1959385

Keywords: generative AI, multimodality, advertising, cultural representation, text-to-image, image-to-text, hallucination, semiotics, Cultural, multimodal, realization, various