TY  - JOUR
T1  - THE ROLE OF ARTIFICIAL INTELLIGENCE IN DESIGN: A STUDY ON THE REPRODUCTION OF PRESTIGIOUS UNIVERSITY LOGOS
TT  - TASARIMDA YAPAY ZEKANIN ROLÜ: PRESTİJLİ ÜNİVERSİTE LOGOLARININ YENİDEN ÜRETİMİ ÜZERİNE BİR ARAŞTIRMA
AU  - Dursun, Faruk
PY  - 2025
DA  - June
Y2  - 2025
DO  - 10.20488/sanattasarim.1717602
JF  - Sanat ve Tasarım Dergisi
PB  - Anadolu Üniversitesi
WT  - DergiPark
SN  - 2146-9059
SP  - 397
EP  - 424
VL  - 15
IS  - 1
LA  - en
AB  - This study aims to understand the capacity and limitations of AI tools in graphic designprocesses. The research evaluates the potential impact of AI on corporate identity designthrough the reproduction of logos of prestigious universities. The research analyzes in detailthe capacity of AI to interpret visual identities during logo reproduction. The research focuseson examining the current capabilities of AI technology in the context of reproducinguniversity logos. In particular, the study seeks to answer the following questions:- To what extent can AI tools accurately reproduce symbolically and aesthetically complexvisual identities?- How are the visual elements of corporate identities interpreted and redesigned by AI?The methodology of this research consists of three main phases: Data collection, reproductionwith AI tools and evaluation. In the data collection phase, the logos of the top15 universities in The Times Higher Education World University Rankings 2023 list wereanalyzed. However, 5 logos consisting only of typographic elements were excluded from thescope of the study. Therefore, 10 university logos with visual and symbolic diversity (Oxford,Harvard, Cambridge, Stanford, MIT, Princeton, Columbia, Chicago, Pennsylvaniaand Johns Hopkins) were analyzed. The meaning and context of each university logo wascompiled based on the descriptions from their official websites. These descriptions detail thelogos' history, design elements and symbolic meanings. The logos were reproduced using10 different generative AI applications (Artguru, CoPilot, Design.ai, Gemini Advanced,Genraft, Gettin.ai, Leonardo.ai, Chat GPT 4.0, Pixlr and Runwayml). These tools wereselected based on their visual production capabilities and user-friendly interfaces. For eachlogo, descriptions taken from the official website of the relevant university were given as“prompt” (input) to the artificial intelligence. Each logo produced by the AI tools was evaluatedaccording to the following criteria:- Symbolism and Harmony of Meaning: The extent to which the logo generated by the AIreflects the symbolic meaning of the original logo was analyzed.- Design Accuracy: The similarity of the design elements was analyzed by comparing it withthe original logo.- Detail and Composition: The accuracy of visual elements such as color palette, shapes andgeneral arrangement were evaluated.As a result, in the context of detailed and aesthetic design; Leonardo.ai and Artguru offersuccessful designs that reflect aesthetic harmony, richness of detail and academic identityin Cambridge, Columbia and Harvard logos. Leonardo.ai stands out especially in the Columbiaand Harvard logos, while Artguru combines traditional elements with modern inthe Cambridge logo. In terms of preserving historical elements, RunwayML and Artgurusuccessfully modernize historical elements in the Cambridge, Columbia and Chicago logos,reflecting academic identity. RunwayML in particular has managed to create a contemporaryfeel while retaining traditional visual elements. Gencraft, CoPilot, Pixlr and ChatGPT 4.0 produced effective outputs with minimal and modern designs in the Stanford andMIT logos. Especially CoPilot offers a simple but remarkable solution in the MIT logo. ChatGPT 4.0, on the other hand, displays a balanced modernity in the Columbia and Harvardlogos. Design.ai provides strong visual expressions in the Columbia and Harvard logos. TheChicago logo is characterized by minimalism, while the Chicago logo is characterized byartistic solutions, although some of the logos are complex. Gemini Advanced has successfullyapplied modern design techniques to the Cambridge, Columbia and Harvard logos.The Columbia logo stands out for its balanced design, even if it partially ignores traditionalelements. Pixlr and Getting.ai, on the other hand, failed to reflect the corporate identitystrongly enough in some logos (e.g. Pennsylvania and John Hopkins). Getting.ai tended toover-simplify in the Stanford and John Hopkins logos, while Pixlr was weak with a lack ofdetail in the Cambridge and Harvard logos.
KW  - Generative AI
KW  - AI-assisted design
KW  - University logos
KW  - Visual identity reproduction.
N2  - Bu çalışma, yapay zeka araçlarının grafik tasarım süreçlerindeki kapasitesini ve sınırlamalarınıanlamayı amaçlamaktadır. Araştırma, prestijli üniversitelerin logolarının yenidenüretimi üzerinden, yapay zekanın kurumsal kimlik tasarımı üzerindeki potansiyel etkisinideğerlendirmektedir. Araştırma, logoların yeniden oluşturulması sırasında yapay zekanıngörsel kimlikleri yorumlama kapasitesini detaylı bir şekilde analiz etmektedir. Araştırma,üniversite logolarının yeniden üretilmesi bağlamında yapay zeka teknolojisinin mevcutyeteneklerini incelemeye odaklanmıştır. Çalışma, özellikle şu sorulara yanıt aramaktadır:• Yapay zeka araçları, sembolik ve estetik açıdan karmaşık görsel kimlikleri ne ölçüde doğruşekilde yeniden üretebilir?• Kurumsal kimliklerin görsel unsurları, yapay zeka tarafından nasıl yorumlanmakta veyeniden tasarlanmaktadır?Bu araştırmanın metodolojisi üç temel aşamadan oluşmaktadır: Veri toplama, yapay zekaaraçlarıyla yeniden üretim ve değerlendirme. Veri toplama aşamasında, The Times HigherEducation World University Rankings 2023 listesindeki ilk 15 üniversitenin logoları incelenmiştir.Ancak, yalnızca tipografik unsurlardan oluşan 5 logo çalışma kapsamı dışındabırakılmıştır. Bu nedenle, görsel ve sembolik çeşitlilik barındıran 10 üniversite logosu (Oxford,Harvard, Cambridge, Stanford, MIT, Princeton, Columbia, Chicago, Pennsylvania veJohns Hopkins) analiz edilmiştir. Her üniversitenin logosuyla ilgili anlam ve bağlam, resmiweb sitelerinden alınan açıklamalara dayanılarak derlenmiştir. Bu açıklamalar, logolarıntarihi, tasarım unsurları ve sembolik anlamları detaylarını içermektedir. Logolar, 10 farklıüretken yapay zeka uygulaması (Artguru, CoPilot, Design.ai, Gemini Advanced, Genraft,Gettin.ai, Leonardo.ai, Chat GPT 4.0, Pixlr ve Runwayml) kullanılarak yeniden üretilmiş-tir. Bu araçların seçilmesinde, görsel üretim yetenekleri ve kullanıcı dostu arayüzleri gibikriterler dikkate alınmıştır. Her bir logo için, ilgili üniversitenin resmi web sitesinden alınanaçıklamalar, yapay zekaya "prompt" (girdi) olarak verilmiştir. Yapay zeka araçlarının ürettiğiher bir logo, aşağıdaki kriterlere göre değerlendirilmiştir:• Sembolizm ve Anlam Uyumu: Yapay zeka tarafından üretilen logonun, orijinal logonuntaşıdığı sembolik anlamı ne ölçüde yansıttığı analiz edilmiştir.• Tasarım Doğruluğu: Orijinal logo ile karşılaştırılarak, tasarım unsurlarının benzerliğiincelenmiştir.• Detay ve Kompozisyon: Renk paleti, şekiller ve genel düzenleme gibi görsel unsurlarındoğruluğu değerlendirilmiştir.Sonuç olarak detaylı ve estetik tasarım bağlamında; Leonardo.ai ve Artguru, Cambridge,Columbia ve Harvard logolarında estetik uyum, detay zenginliği ve akademik kimliğiyansıtan başarılı tasarımlar sunmaktadır. Leonardo.ai, özellikle Columbia ve Harvard logolarındaöne çıkarken, Artguru ise Cambridge logosunda geleneksel unsurları modernlebirleştirmiştir. Tarihsel unsurları koruma bağlamında RunwayML ve Artguru, Cambridge,Columbia ve Chicago logolarında tarihsel öğeleri başarıyla modernleştirerek akademikkimliği yansıtmaktadır. Özellikle RunwayML, geleneksel görsel unsurları korurkençağdaş bir his yaratmayı başarmıştır. Gencraft, CoPilot, Pixlr ve Chat GPT 4.0, Stanfordve MIT logolarında minimal ve modern tasarımlarla etkili çıktılar üretmiştir. ÖzellikleCoPilot, MIT logosunda sade ama dikkat çekici bir çözüm sunmaktadır. Chat GPT 4.0 iseColumbia ve Harvard logolarında dengeli bir modernlik sergilemektedir. Design.ai, Columbiave Harvard logolarında güçlü görsel anlatımlar sunmaktadır. Chicago logosundaminimalizmle öne çıkarken, bazı logolarda karmaşıklık yaratsa da sanatsal çözümleriyledikkat çekmektedir. Gemini Advanced, modern tasarım tekniklerini Cambridge, Columbiave Harvard logolarında başarılı bir şekilde uygulamıştır. Geleneksel unsurları kısmen gözardı etmiş olsa da Columbia logosundaki dengeli tasarımıyla öne çıkmaktadır. Buna karşınPixlr ve Getting.ai, bazı logolarda (ör. Pennsylvania ve John Hopkins) kurumsal kimliğiyeterince güçlü yansıtamamıştır. Getting.ai, Stanford ve John Hopkins logolarında aşırısadeleşme eğilimi gösterirken Pixlr ise Cambridge ve Harvard logolarında detay eksikliğiylezayıf kalmıştır.
CR  - Alcaide-Marzal, J., Diego-Mas, J. A. & Acosta-Zazueta, G. (2020). A 3D shape generative method for aesthetic product design. Design Studies, 66, 144-176. https://doi.org/10.1016/j.destud.2019.11.003
CR  - Ardhianto, P., Purbo Santosa, Y., & Pusparani, Y. (2023). A generative deep learning for exploring layout variation on visual poster design. International Journal of Visual and Performing Arts, 5(1), 10-17. https://doi.org/10.31763/viperarts.v5i1.920
CR  - Bartlett, K. A. & Camba, J. D. (2024). Generative Artificial Intelligence in Product Design Education: Navigating Concerns of Originality and Ethics. International Journal of Interactive Multimedia and Artificial Intelligence, 8, 5-64. http://dx.doi.org/10.9781/ijimai.2024.02.006
CR  - Barut, S. & Türker, O. (2024). The Meeting of Generative Artificial Intelligence (GAI) and the Large Language Model (LLM): An Application on Book Covers. Research on Humanities and Social Sciences, 14(5), 115-130. https://doi.org/10.7176/RHSS/14-5-10
CR  - Calo,T. & MacLelan, C. (2024). Towards Educator-Driven Tutor Authoring: Generative AI Approaches for Creating Intelligent Tutor Interfaces. In Proceedings of the Eleventh ACM Conference on Learning @ Scale (L@S ‘24). Association for Computing Machinery, New York, NY, USA, 305–309. https://doi.org/10.1145/3657604.3664694
CR  - Chauhan, A. Chauhan, R., Nainwal, A., Arora, A. & Bhatt, C. (2023). Image Multidiffusion Algorithms for AI Generative Art. 6th International Conference on Contemporary Computing and Informatics (IC3I). https://doi.org/10.1109/IC3I59117.2023.10397719
CR  - Dehman, H. (2023). Graphic design, Already Intelligent? Current possibilities of generative AI applications in graphic design. Malmö University, Faculty of Technology and Society
CR  - Epstein, Z., Hertzmann, A., Herman, L., Mahari, R., Frank, M. R., Groh, M., Schroeder, H., Smith, A., Akten, M., Fjeld, J., Farid, H., Leach, N., Pentland, A., S. & Russakovsky, O. (2023). Art and the science of generative AI: A deeper dive. https://doi.org/10.1126/science.adh4451
CR  - Furtado, L. S., Soares, J. B. & Furtado, V. (2024). A task-oriented framework for generative AI in design. Journal of Creativity, 34, 1-9. https://doi.org/10.1016/j.yjoc.2024.100086
CR  - ANADOLU ÜNİVERSİTESİ SANAT & TASARIM DERGİSİ 422
CR  - Sanat&Tasarım Dergisi,15 (1), 2025: 397-424
CR  - Ganović, M., Avdić, A. (2024). Generative AI Tools in Web Design. Paper presented at Sinteza 2024 - International Scientific Conference on Information Technology, Computer Science, and Data Science. https://doi.org/10.15308/Sinteza-2024-392-397
CR  - Gürdal Pamuklu, A. & Bakar Fındıkcı, M. (2023). Grafik tasarımın geleceği: Yapay zekâ ve insan [The future of graphic design: Artificial intelligence and human]. Bilim, Eğitim, Sanat ve Teknoloji Dergisi (BEST Dergi) [Science, Education, Art and Technology Journal (SEAT Journal)], 7(2), 177-191
CR  - Han, A., Zhou, X., Cai, Z., Han, S., Ko, R., Corrigan, S. & Peppler, K. (2024). Teachers, Parents, and Students’ Perspectives on Integrating Generative AI into Elementary Literacy Education. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI ‘24). Association for Computing Machinery, New York, NY, USA, 1–17. https://doi.org/10.1145/3613904.3642438
CR  - Huang, D., Guo, J., Sun, S., Tian, H., Lin, J., Hu, Z., Li, C.Y, Lou, J. G. & Zhang, D. (2023). A Survey for Graphic Design Intelligence. https://doi.org/10.48550/arXiv.2309.01371
CR  - Hutson, J. & Cotroneo, P. (2023). Generative AI tools in art education: Exploring prompt engi-neering and iterative processes for enhanced creativity. Metaverse, 4(1), 1-14. https://doi.org/10.54517/m.v4i1.2164
CR  - Jaruga-Rozdolska, A. (2022). Artificial intelligence as part of future practices in the architect’s work: MidJourney generative tool as part of a process of creating an architectural form. Architectus, 3(71), 95-104. https://doi.org/10.37190/arc220310
CR  - Jin, Y., Yoon, J., Self, J. & Lee, K. (2024). Understanding Fashion Designers’ Behavior Using Generative AI for Early-Stage Concept Ideation and Revision. Archives of Design Research, 37 (3), 25-45. http://dx.doi.org/10.15187/adr.2024.07.37.3.25
CR  - Kulishova, N.& Tsykalo, S. (2024). Decısıon Makıng in Process of Board Games Artwork Design Using Generative Artificial Intelligence Applications. Tehnologìâ ì tehnìka drukarstva, 1(83). https://doi.org/10.20535/2077-7264
CR  - Li, J., Cao, H., Lin, L., Hou, Y., Zhu, R. & El Ali, A. (2024). User Experience Design Professionals’ Perceptions of Generative Artificial Intelligence. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI ‘24). Association for Computing Machinery, New York, NY, USA, 1–18. https://doi.org/10.1145/3613904.3642114
CR  - Ling, L., Chen, X., Wen, R., Jia-Jun Li, T. & Lc,R. (2024). Sketchar: Supporting Character Design and Illustration Prototyping Using Generative AI. Proc. ACM Hum.-Comput. Interact. 8, 1-28. https://doi.org/10.1145/3677102
CR  - ANADOLU ÜNİVERSİTESİ SANAT & TASARIM DERGİSİ 423
CR  - Sanat&Tasarım Dergisi,15 (1), 2025: 397-424
CR  - Liu, Y. & Sra, M. (2024). DanceGen: Supporting Choreography Ideation and Prototyping with Generative AI. In Proceedings of the 2024 ACM Designing Interactive Systems Conference (DIS ‘24). Association for Computing Machinery, New York, NY, USA, 920–938. https://doi.org/10.1145/3643834.3661594
CR  - Lively, J., Hutson, J. & Melck, E. (2023). Integrating AI-Generative Tools in Web Design Education: Enhancing Student Aesthetic and Creative Copy Capabilities Using Image and Text-Based AI Generators. DS Journal of Artificial Intelligence and Robotics 1(1), 23-33.
CR  - Lyu, Y.; Shi, M.; Zhang, Y.; Lin, R. From Image to Imagination: Exploring the Impact of Generative AI on Cultural Translation in Jewelry Design. Sustainability, 16(1), 65-84. https://doi.org/10.3390/su16010065
CR  - Ma, M. & Zhao, W. (2024). Computer-Aided Design & Applications, 21(S25), 60-75. https://doi.org/10.14733/cadaps.2024.S25.60-75
CR  - Mayahi, S. & Vidrih, M. (2022). The Impact of Generative AI on the Future of Visual Content Marketing. Human-Computer Interaction, 1-15. https://doi.org/10.48550/arXiv.2211.12660
CR  - Mikkonen, J.(2023) Advent of GAN: How does a generative AI create a moodboard?, in Holmlid, S., Rodrigues, V., Westin, C., Krogh, P. G., Mäkelä, M., Svanaes, D., Wikberg-Nilsson, Å (eds.), Nordes 2023: This Space Intentionally Left Blank, 12-14 June, Linköping University, Norrköping, Sweden. https://doi.org/10.21606/nordes.2023.114
CR  - Mikroyannidis, A., Sharma, N., Ekuban, A. & Domingue,J. (2024). Using Generative AI and ChatGPT for improving the production of distance learning materials. In: 24th IEEE International Conference on Advanced Learning Technologies (ICALT 2024), 01-04 Jul 2024, Nicosia, Cyprus,
CR  - Ning, J., Gao, Y. & Luo, M. (2024). Application Research of Generative Artificial Intelligence Technology in the Design and Art Course Teaching. International Conference on Informatics Education and Computer Technology Applications. https://doi.org/10.1109/IECA62822.2024.00038
CR  - Putjorn, T. & Putjorn, P. (2023). Augmented Imagination: Exploring Generative AI from the Perspectives of Young Learners. 15th International Conference on Information Technology and Electrical Engineering, https://doi.org/10.1109/ICITEE59582.2023.10317680
CR  - ANADOLU ÜNİVERSİTESİ SANAT & TASARIM DERGİSİ 424
CR  - Sanat&Tasarım Dergisi,15 (1), 2025: 397-424
CR  - Schetinger, V., Di Bartolomeo, S., El-Assady, M., McNutt, A., Miller, M., Passos, J. P. A. & Adams, J. L. (2023). Doom or Deliciousness: Challenges and Opportunities for Visualization in the Age of Generative Models. Eurographics Conference on Visualization, Computer Gaphics Forum, 42(3), 423-435. https://doi.org/10.1111/cgf.14841
CR  - Shin, D., Wang, L. L. & Hsieh, G. (2024). From Paper to Card: Transforming Design Implications with Generative AI. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI ‘24). Association for Computing Machinery, New York, NY, USA, 1–15. https://doi.org/10.1145/3613904.3642266
CR  - Sinngh, V. & Gu, N. (2012). Towards an integrated generative design framework. Design Studies, 33(2), 185-207. https://doi:10.1016/j.destud.2011.06.001
CR  - Smolinski, P. R., Januszewicz, J., & Winiarski, J. (2023). Towards Completely Automated Advertisement Personalization: An Integration of Generative AI and Information Systems. In A. R. da Silva, M. M. da Silva, J. Estima, C. Barry, M. Lang, H. Linger, & C. Schneider (Eds.), Information Systems Development, Organizational Aspects and Societal Trends (ISD2023 Proceedings). Lisbon, Protugal: Instituto Superior Técnico. ISBN: 978-989-33-5509-1. https://doi.org/10.62036/ISD.2023.60
CR  - Tomić, I., Jurič, I., Dedijer, S. & Adamović, S. (2023). Artificial Intelligence in Graphic Design. 54th IC Annual Conference Proceedings, 18-20 September, 85-94
Tran, L. D., Tung, N., Macalinga, E. T., Tang, A., Woo, B. & Tam, W. (2024). Visual narratives in nursing education: A generative artificial intelligence approach. Nurse Education in Practice, 79, 1-9. https://doi.org/10.1016/j.nepr.2024.104079
CR  - Xu, W., Li, M. & Yang, X. (2024). Can Generative Ai Models Count?. Computer-Aided Architectural Design Research in Asia, 1, 89-98.
CR  - Ye, Y., Hao, J., Hou, Y., Wang, Z., Xiao, S., Luo, Y. & Zeng, W. (2024). Generative AI for visualization: State of the art and future directions. Visual Informatics 8, 43–66. https://doi.org/10.1016/j.visinf.2024.04.003
UR  - https://doi.org/10.20488/sanattasarim.1717602
L1  - https://dergipark.org.tr/tr/download/article-file/4950111
ER  -