Cognitive Load and Creative Flow in Dynamic Poster Design: A Comparative Study of Manual, AI-Generated, and Human-AI Collaborative Workflows

DOI

10.2991/978-94-6239-737-8_41How to use a DOI?

Keywords

Dynamic poster design; Generative AI; Human-AI collaboration; Cognitive load; Creative flow; Multimedia design

Abstract

Dynamic posters require coordinated visual hierarchy, motion timing, and rapid iteration. Generative AI can support drafts, style references, and motion concepts, but pure AI workflows often shift effort to prompt refinement, selection, and correction. Rather than proposing a new model, this paper presents a reproducible framework for comparing manual, pure-AI, and human-AI workflows in dynamic poster design. The framework standardizes task conditions, AI tools, participant expertise, and a normalized effectiveness index combining cognitive load, creative flow, quality, and completion time. A designed experiment with 60 participants is outlined. Expected results suggest that human-AI collaboration reduces unnecessary load while preserving designer control and creative engagement.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Lin Zhang
AU  - Weiqian Mao
AU  - Yingfei Jia
AU  - Yang Liu
PY  - 2026
DA  - 2026/08/18
TI  - Cognitive Load and Creative Flow in Dynamic Poster Design: A Comparative Study of Manual, AI-Generated, and Human-AI Collaborative Workflows
BT  - Proceedings of the 2026 5th International Conference on Art Design and Digital Technology (ADDT 2026)
PB  - Atlantis Press
SP  - 332
EP  - 338
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-737-8_41
DO  - 10.2991/978-94-6239-737-8_41
ID  - Zhang2026
ER  -