AI-Assisted Storyboarding and Previsualization Tools for Animation Education

Abstract

Storyboarding and previsualization (previz) are foundational stages in animation production, shaping narrative structure, shot composition, pacing, and visual continuity. Recent advances in generative artificial intelligence (AI), including diffusion-based image synthesis, multimodal transformers, and real-time rendering engines, have introduced AI-assisted tools capable of accelerating ideation, shot blocking, and animatic development. This study investigates the pedagogical impact of AI-assisted storyboarding and previsualization tools in animation education. Using an explanatory mixed-methods design, Phase I consists of a quasi-experimental comparison between traditional storyboard instruction and AI-augmented workflows (n = 120 students). Phase II includes qualitative interviews with faculty and students, alongside artifact analysis of storyboard outputs and animatics. Results indicate improvements in ideation speed (26%), visual experimentation diversity (31%), and iterative refinement cycles, while raising concerns related to authorship, overreliance, and originality. The study proposes a structured AI-integrated storyboard pedagogy model and an ethical adoption framework suitable for higher education institutions. Findings contribute to research in AI-driven creative education and provide curriculum recommendations for animation and media programs.