ORIGINAL RESEARCH article
Front. Commun.
Sec. Visual Communication
This article is part of the Research TopicAI and CommunicationView all 9 articles
Visualizing conflict: Tracing Aesthetic Patterns in AI-Generated Images of the 2023 Israel-Hamas War
Provisionally accepted
- Cyprus University of Technology, Limassol, Cyprus
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
While much discussion is focused on the relation between generative artificial intelligence and visual culture, its implications for journalism and public perception require a more in-depth critical examination. This study examines 28 AI-generated images created using DALL-E and DeepAI tools by addressing the ways artificial intelligence constructs visual narratives of the 2023 Israel-Hamas conflict. By employing a visual discourse analysis combined with social actor theory, this essay explores how civilians are represented, categorized, and positioned within digitally rendered conflict imagery. The study highlights how AI-generated imagery reproduces dominant tropes from traditional war photography while simultaneously reshaping them within new technological frameworks. The analysis reveals that the AI-generated visuals consistently foreground the civilian experience, particularly focusing on themes of destruction, resilience, motherhood, and childhood, while omitting direct representations of the primary political actors involved in the conflict. This pattern likely reflects the characteristics of the data used to train contemporary generative AI systems, including biases toward humanitarian imagery and constraints that discourage the depiction of identifiable political actors, thereby privileging emotionally resonant civilian narratives over politically explicit representations.
Keywords: AI Images, Constructing Conflict, DALL-E, DeepAI, Visual discourse analysis
Received: 23 Oct 2025; Accepted: 16 Feb 2026.
Copyright: © 2026 Theodosiou, Papa and Markou. 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: Zenonas Theodosiou
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.