AI-Enhanced Generative Motion Design for Interactive Digital Storytelling - Nihayah, Azed Yayah Durrotun; Marchel, Joseflim; Henry, Lawrence (International Journal of Graphic Design, 2026)
The increasing demand for dynamic digital content has positioned motion graphics as a key medium in contemporary visual communication. However, conventional motion design workflows remain largely static and production-oriented, limiting their capacity to support adaptive and interactive storytelling. This study introduces an AI-enhanced generative motion design framework that integrates generative visual formation, temporal animation logic, and user-driven interaction within a unified system. The framework embeds generative AI directly into the motion design process, enabling visual elements to evolve continuously in response to contextual input and user interaction. A three-layer architecture, comprising generative, motion, and interaction components, is implemented in a functional prototype to support non-linear and responsive narrative structures. The system is evaluated through a combination of structured observation and user-oriented assessment, involving 8 participants with backgrounds in digital media and design. The results indicate that the proposed approach produces visually coherent yet evolving motion graphics while supporting real-time responsiveness to user input. Compared with conventional workflows, the framework demonstrates greater adaptability and variability without compromising narrative consistency. These findings highlight the potential of integrating generative processes with motion and interaction to support adaptive visual storytelling.