SYSTEMATIC REVIEW article
Front. Commun.
Sec. Culture and Communication
Volume 10 - 2025 | doi: 10.3389/fcomm.2025.1645168
Deep Learning in Cultural Imagery Dissemination: A Systematic Scoping Review of AI-Driven Visual Transmission Mechanisms
Provisionally accepted
- 1Tongji University, Shanghai, China
- 2Faculty of Applied Sciences, Macao Polytechnic University, Macao, China
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Background: In an era of rapid media technology and AI advancement, deep learning (DL)-driven visual images (VI) is emerging as a critical mode of cultural transmission (CT). Despite the growing application of DL in the VI domain, there is a lack of a systematic review that comprehensively explores its transmission pathways, mechanisms of influence, and associated challenges. This study aims to systematically explore the pathways and impacts of DL-driven VI in CT and identify key trends and issues in the field through a systematic scoping review of existing literature. Methods: This review analyzes 18 studies published between 2015 and 2024. The literature search was conducted across five databases: WOS, ScienceDirect, Scopus, ACM, and A&HCI. The research was undertaken rigorously following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, ensuring systematic selection, extraction, and analysis of the identified studies. Results: The study analyzed the literature from four aspects: transmission pathways, content, technology, and cultural context, identifying three main research areas: (1) the influence mechanisms of AI and social media on cultural transmission; (2) the role of VI in cross-cultural communication; and (3) the application of AI and digital technology in the conservation of Cultural Ecosystem Services (CES). The study finds that AI-driven visual technologies significantly enhance the breadth and impact of CT, particularly through DL algorithms. However, the field faces critical challenges such as algorithmic bias, cultural homogenization, and the reliability of user-generated content. Conclusion: By systematically synthesizing the existing literature, this study provides a theoretical foundation for future research and points to emerging research directions, such as how to use DL to address ethical challenges in cultural communication and explore the differences in the application of DL and VI in different cultural contexts.
Keywords: deep learning, cultural transmission, visual images, Systematic scoping review, DL
Received: 11 Jun 2025; Accepted: 09 Sep 2025.
Copyright: © 2025 Yang, Liu, Luo and Pang. 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: Patrick Cheong-Iao Pang, Faculty of Applied Sciences, Macao Polytechnic University, Macao, China
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