Enhancing Cross-disciplinary Design Competence through an Innovative Visual Identity Model and Structured Teaching

Brand visual identity design is pivotal in shaping corporate image and advancing sustainability; however, traditional methods and artificial intelligence (AI) technologies often fall short in translating core brand values into designs that meet the needs of learners and clients. This study introduces the "Candy Model," an innovative framework that empowers cross-disciplinary learners through a structured pedagogical approach. Built on Quality Function Deployment (QFD) and integrated with the Mandala Chart, the model employs a converge-diverge process to foster collaboration, transforming brand essence into creative and sustainable visual identity designs. Conducted over a 14-week interdisciplinary course at a Taiwanese university, the study involved 52 junior students (divided into 13 groups across fields such as creative design and information engineering) who applied the model to complete design tasks. Experts utilized the Analytic Hierarchy Process (AHP) to establish seven evaluation criteria (e.g., sustainability, originality) and Fuzzy Comprehensive Evaluation (FCE) to assess three sampled outcomes. Results demonstrate that the model significantly enhances learners’ capabilities in brand comprehension and innovative design, offering insights into structured design education that outperform AI-driven approaches in addressing emotional and strategic needs. This human-centric method bridges gaps between education and practice, with potential for broader applications. Future research could expand sample sizes and explore industry integration.

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Publication Details

Published
2025-05-23
DOI
https://doi.org/10.1109/icett66247.2025.11137089
Primary Topic
Engineering Education and Curriculum Development
Type
article
Field-Weighted Citation Impact
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article

Enhancing Cross-disciplinary Design Competence through an Innovative Visual Identity Model and Structured Teaching

Zhijie Jiang, Hsu-Chan Hsiao, Jui-Feng Chang, Yu‐Ting Hsiao et al.
Engineering Education and Curriculum Development
article

Enhancing Cross-disciplinary Design Competence through an Innovative Visual Identity Model and Structured Teaching

Zhijie Jiang, Hsu-Chan Hsiao, Jui-Feng Chang, Yu‐Ting Hsiao, Meng-Dar Shieh
article en

Abstract

Brand visual identity design is pivotal in shaping corporate image and advancing sustainability; however, traditional methods and artificial intelligence (AI) technologies often fall short in translating core brand values into designs that meet the needs of learners and clients. This study introduces the "Candy Model," an innovative framework that empowers cross-disciplinary learners through a structured pedagogical approach. Built on Quality Function Deployment (QFD) and integrated with the Mandala Chart, the model employs a converge-diverge process to foster collaboration, transforming brand essence into creative and sustainable visual identity designs. Conducted over a 14-week interdisciplinary course at a Taiwanese university, the study involved 52 junior students (divided into 13 groups across fields such as creative design and information engineering) who applied the model to complete design tasks. Experts utilized the Analytic Hierarchy Process (AHP) to establish seven evaluation criteria (e.g., sustainability, originality) and Fuzzy Comprehensive Evaluation (FCE) to assess three sampled outcomes. Results demonstrate that the model significantly enhances learners’ capabilities in brand comprehension and innovative design, offering insights into structured design education that outperform AI-driven approaches in addressing emotional and strategic needs. This human-centric method bridges gaps between education and practice, with potential for broader applications. Future research could expand sample sizes and explore industry integration.

Asia University (TW), Dongguan University of Technology (CN), National Cheng Kung University (TW)
Openalex Percentile: Top 66%
Engineering Education and Curriculum Development
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