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Generative AI as an External Cognitive Tool for Developing Creative Intelligence in Visual Design: A Mixed-Methods

Ziyang Huang¹, Jiajia Zhao¹, Xuan Fu¹

¹Department of Communication Design, College of Design, Graduate School, Hanyang University, Seoul 04763, Republic of Korea.

Journal of Intelligence

|April 27, 2026

Abstract:

Generative artificial intelligence (GenAI) is rapidly transforming design education by enabling new forms of human-AI collaborative learning. However, how GenAI relates to cognitive and motivational processes in design learning contexts remains insufficiently understood. This study examines whether integrating GenAI into visual design instruction is associated with improvements in domain-specific creative performance and explores the relationships among cognitive load, learning motivation, and learning outcomes. A six-week randomized instructional experiment was conducted with 120 undergraduate students majoring in visual communication design. Creative performance was evaluated through blind expert ratings, and the relationships among key variables were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that GenAI-integrated instruction is associated with higher levels of learning motivation, engagement, and expert-rated creative performance compared with traditional instruction, whereas cognitive-load indicators show comparatively limited predictive strength within the overall model. In addition, Integrated Teaching Alignment (ITA) significantly moderates the relationship between perceived relevance and learning satisfaction. These findings suggest that GenAI may function as an external cognitive support tool, with learning outcomes appearing to be associated with motivational and instructional factors, while cognitive-load indicators show comparatively limited associations within this instructional context.

Keywords:

PLS-SEM cognitive load creative intelligence domain-specific intelligence expert-rated performance generative AI human–AI co-creation learning motivation psychometric modeling

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