Innovative Methods in Art and Design Education: A Comparative Study of Traditional, AI-Generated, and Human-AI Collaborative Learning Workflows

DOI

10.2991/978-94-6239-737-8_46How to use a DOI?

Keywords

Art and design education; Generative AI; Human-AI collaboration; Cognitive load; Creative self-efficacy; Pedagogical workflow

Abstract

Art and design education faces increasing pressure to integrate generative AI tools while preserving fundamental creative skills, critical thinking, and iterative craftsmanship. However, existing pedagogical approaches either ignore AI or adopt it uncritically, lacking empirical comparison of learning outcomes, cognitive load, and student engagement across different workflows. This paper proposes a reproducible framework for comparing three distinct workflows in a foundational graphic design course: traditional (non-AI), pure-AI (AI as primary executor), and human-AI collaborative (AI as suggester and explorer, student as final decision-maker and executor). The framework further distinguishes collaboration modes by role allocation: AI as suggester, explorer, or critic, and student as final decision-maker and executor. Expected results suggest that human-AI collaboration reduces unnecessary cognitive load, fosters creative confidence, and achieves higher learning gains than either pure-AI or traditional methods, while preserving design reasoning skills. The framework provides a replicable protocol for curriculum integration and assessment of AI in art and design education.

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  - Jiufang Mei
AU  - Ling Jin
AU  - Yingfei Jia
AU  - Weijing Zhong
PY  - 2026
DA  - 2026/08/18
TI  - Innovative Methods in Art and Design Education: A Comparative Study of Traditional, AI-Generated, and Human-AI Collaborative Learning Workflows
BT  - Proceedings of the 2026 5th International Conference on Art Design and Digital Technology (ADDT 2026)
PB  - Atlantis Press
SP  - 370
EP  - 376
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-737-8_46
DO  - 10.2991/978-94-6239-737-8_46
ID  - Mei2026
ER  -