AI is fundamentally transforming audiovisual content production and radically altering how visual content is conceived, produced, and disseminated. Recent research shows that AI technologies can now automate key production tasks such as editing, scene detection, and motion graphics. AI-powered design tools can also generate entire layouts and original artwork, boosting productivity and opening new avenues for innovation (Anantrasirichai and Bull, 2022).

Building on these automation capabilities, advanced models like latent diffusion models, which blend diffusion processes with generative adversarial networks (GANs), are now ushering in a new era of creativity. Generative tools such as Runway, powered by latent diffusion, exemplify how machine learning not only accelerates production but also expands what is possible in audiovisual content creation. Thanks to these technologies, artists and content creators can dedicate more attention to storytelling and creative decisions, relying on AI to manage a growing share of both technical and generative tasks.

AI’s influence goes beyond both automation and generative content creation. Modern systems are now capable of producing entirely original designs, tailor content to user preferences, and enable real-time personalization (Lee & Kim, 2024). As Chen (2025) notes, these advances are reshaping audience engagement and challenging traditional notions of creative authorship.

Despite extensive research into AI-generated media, critical questions remain about how these technologies shape our understanding of visual information. Beyond their automation and generative capabilities, diffusion models and similar AI systems fundamentally alter how we perceive and interpret visual material. When statistical algorithms produce images and videos, they influence what we consider visually significant and how we make sense of what we see and hear. This, in turn, affects what is represented, whose perspective is captured, and how audiovisual content conveys information and knowledge. Research by Farooq and Vreese (2025) demonstrates this in practice: they found that “the level of aesthetic realism in AI generated disinformation images was a key indicator of how likely participants were to judge them as being authentic,” with such surface cues also affecting viewers' confidence in their authenticity judgments.

The growing influence of AI-generated visuals reshapes both audience interpretation and media production practices, particularly as these artificially created visuals increasingly serve as supporting documentation or narrative devices. Understanding how artificially generated images contribute to knowledge construction is therefore essential in visual communication research, as it addresses both empirical questions about audience responses and theoretical concerns about epistemic credibility.

The broader adoption of generative AI in media production also raises pressing cultural and economic concerns. While generative AI offers substantial creative advantages, it introduces notable challenges for the audiovisual industry as a whole. Widespread adoption has the potential to displace creative workers and could reduce revenue in the audiovisual industry by more than 20% by 2028 (CISAC, November 2024).

This Research Topic aims to critically examine how AI is transforming the audiovisual production process from conception to execution. While AI content generation models are being rapidly integrated into motion graphics, video content creation, editing, and post-production workflows, there is still limited understanding of their impact on authorship, creativity, and meaning-making. The industry’s swift embrace of these technologies contrasts with a relative lack of theoretical and empirical analysis regarding their cultural, ethical, and aesthetic implications.

Our goal is to explore how AI not only streamlines production, but also fundamentally changes the visual language and narrative structures of media content. This includes considering the risks of homogenization, assessing the importance of human agency in collaborative creation, and investigating new creative paradigms enabled by computational logic. By bridging theory and practice, we hope to advance a critical understanding of AI’s evolving role in audiovisual communication and to propose frameworks that guide its responsible and creative integration.

This Research Topic invites interdisciplinary submissions that investigate how AI is reshaping creative processes, visual languages, authorship, and cultural production in areas such as film, animation, motion graphics, XR environments, and digital storytelling.

We welcome contributions addressing, but not limited to, the following themes:

• generative AI in motion graphics, media creation, and post-production
• algorithmic aesthetics and visual innovation
• human–machine co-creation in audiovisual workflows
• AI-mediated authorship and creative labor
• ethical and cultural implications of automated content
• case studies of creative projects involving AI
• AI-driven tools and workflows in animation and editing
• algorithmic authorship and creative labor
• visual aesthetics and narrative innovation through AI
• socio-cultural implications of AI-generated media
• epistemic implications of AI-generated imagery and its impact on audiovisual knowledge production.

We welcome a range of article types, including Original Research, Perspective, Conceptual Analysis, Methods, Policy and Practice Review, Case Report, and Brief Research Report. Contributions that integrate theoretical reflection with practice-based or applied research are particularly encouraged.

This article collection aims to foster an inclusive, critical dialogue among scholars, artists, designers, technologists, and media practitioners.

Are you interested in contributing a manuscript to this Research Topic?

Motion, Meaning, and Machine: AI at the Core of Audiovisual Production