Research on the Teaching System of Generative Artificial Intelligence Prompts Empowered by Hierarchical Gestalt Theory from the Perspective of Higher Design Education: Taking Cultural and Creative Design and Cultural Narrative Illustration as Empirical Ca

Hangfan Zhou

doi:10.63313/IJED.9052

Authors

Hangfan Zhou Faculty of Innovation and Design, City University of Macau, Macau, 999078, China Author

DOI:

https://doi.org/10.63313/IJED.9052

Keywords:

Hierarchical Gestalt Theory, Generative Artificial Intelligence, Prompt Optimization, Higher Design Education, Cultural Narrative Illustration

Abstract

Generative Artificial Intelligence (GenAI) is widely integrated into higher design education as a core creative tool for cultural and creative design, yet critical teaching gaps remain. Existing prompt frameworks are technology-focused rather than teaching-oriented, and students’ prompt writing relies heavily on intuition, leading to frequent mismatches between GenAI outputs and design expectations, as well as superficial expression of cultural connotations. This study aimed to construct a teachable GenAI prompt optimization framework for higher design education based on Hierarchical Gestalt Theory, using cultural narrative illustration as the empirical scenario. Through theoretical deduction, framework construction, and case verification, this study clarified the mapping between the theory’s three-level logic (component-context-gestalt) and prompt structure. Results showed the framework effectively improved the consistency between GenAI outputs and cultural narrative connotation, solving core pain points of traditional prompt writing. This study fills the gap of systematic prompt teaching frameworks in design education and provides an operable teaching tool for the field.

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