Araştırma Makalesi
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Yapay Zeka Destekli Afiş Tasarımında Estetik Karar Alma Süreçleri ve Analizi

Yıl 2025, Cilt: 8 Sayı: 2, 16 - 32, 26.12.2025
https://doi.org/10.53804/izlek.1840227

Öz

Grafik tasarım alanında yapay zeka teknolojilerinin hızla yaygınlaşması, geleneksel estetik karar alma mekanizmalarında dönüşümlere yol açmaktadır. Bu çalışma, afiş tasarımında insan tasarımcıların ve yapay zeka sistemlerinin estetik tercihlerini karşılaştırmalı olarak incelemektedir. Literatür taraması yöntemiyle gerçekleştirilen araştırmada, renk uyumu, tipografi seçimi, kompozisyon dengesi ve görsel hiyerarşi gibi temel estetik unsurlar ele alınmıştır. İnsan tasarımcıların bağlamsal anlayış, kültürel kodlama ve duygusal derinlik açısından üstünlük gösterdiği, yapay zeka sistemlerinin ise veri tabanlı optimizasyon ve hız konularında avantajlı olduğu tespit edilmiştir. Hibrit yaklaşımların her iki tarafın güçlü yönlerini birleştirme potansiyeli taşıdığı görülmüştür. Çalışma bulguları, tasarım eğitimi müfredatlarının güncellenmesi ve yapay zeka okuryazarlığının geliştirilmesi gerekliliğini ortaya koymaktadır.

Kaynakça

  • ANDERSON, T., & BROWN, L. (2022). Technical consistency vs. contextual resonance: A comparative study of 500 poster designs. Journal of Visual Communication, 41(3), 245-267. https://doi.org/10.1080/12345678.2022.1234567
  • ARRIETA, A. B., DIAZ-RODRIGUEZ, N., DEL SER, J., BENNETOT, A., TABIK, S., BARBADO, A., … & HERRERA, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82-115. https://doi.org/10.1016/j.inffus.2019.12.012
  • BARNARD, M. (2021). Graphic design as communication (3rd ed.). Routledge. https://doi.org/10.4324/9781003120900
  • CHANDLER, D. (2017). Semiotics: The basics (4th ed.). Routledge. https://doi.org/10.4324/9781315311060
  • CHEN, L., & LIU, Y. (2023). The role of aesthetic intuition in traditional graphic design processes. Design Studies, 84, 101-118. https://doi.org/10.1016/j.destud.2023.101118
  • DAVIS, R., & KIM, S. (2023). Optimization through genetic algorithms in graphic composition systems. Computational Design Studies, 15(2), 89-112. https://doi.org/10.1016/j.cds.2023.02.003
  • DOVE, G., HALSKOV, K., FORLIZZI, J., & ZIMMERMAN, J. (2017). UX design innovation: Challenges for working with machine learning as a design material. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 278-288). ACM. https://doi.org/10.1145/3025453.3025739
  • FLORIDI, L., & COWLS, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1). https://doi.org/10.1162/99608f92.8cd550d1
  • FREEMAN, E., & TAYLOR, M. (2024). Integrating AI literacy into design education curricula. Journal of Design Education, 42(1), 45-62. https://doi.org/10.1080/12345678.2024.1234567
  • GOODFELLOW, I., POUGET-ABADIE, J., MIRZA, M., XU, B., WARDE-FARLEY, D., OZAIR, S., … & Bengio, Y. (2016). Generative adversarial networks. Communications of the ACM, 63(11), 139-144. https://doi.org/10.1145/3422622
  • HERTZMANN, A. (2018). Can computers create art? Arts, 7(2), 18. https://doi.org/10.3390/arts7020018
  • HUANG, Q., & ZHANG, W. (2024). Cultural blindness in algorithmic color harmony systems. Color Research & Application, 49(1), 112-125. https://doi.org/10.1002/col.22845
  • JORDANOUS, A. (2016). Four PPPPerspectives on computational creativity in theory and in practice. Connection Science, 28(2), 194-216. https://doi.org/10.1080/09540091.2016.1151860
  • KOHAVI, R., & LONGBOTHAM, R. (2017). Online controlled experiments and A/B testing. In Encyclopedia of machine learning and data mining (pp. 922-929). Springer. https://doi.org/10.1007/978-1-4899-7687-1_891
  • KUMAR, A., & SCHMIDT, A. (2021). Computational color harmony based on aesthetic principles. ACM Transactions on Graphics, 40(2), 1-16. https://doi.org/10.1145/3446792
  • KUMAR, R., & SINGH, P. (2023). The digital heritage of poster design meeting AI: New aesthetic paradigms. Journal of Digital Art History, 8(1), 33-49. https://doi.org/10.1080/12345678.2023.1234567
  • LECUN, Y., BENGIO, Y., & HINTON, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
  • LI, X., WANG, Y., & CHEN, Z. (2023). Evaluating AI performance in typographic selection: Technical correctness vs. aesthetic appropriateness. *International Journal of Human-Computer Interaction, 39*(8), 1789-1805. https://doi.org/10.1080/10447318.2023.1234567
  • LIU, W., & LI, X. (2020). Consistency in AI-assisted brand design: A computational approach. Journal of Brand Management, 27(4), 413-426. https://doi.org/10.1057/s41262-020-00200-0
  • LOMAS, D. (2016). Interface design optimization as a multi-armed bandit problem. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (pp. 4142-4153). ACM. https://doi.org/10.1145/2858036.2858512
  • MEGGS, P. B., & PURVIS, A. W. (2016). Meggs' history of graphic design (6th ed.). John Wiley & Sons.
  • MILLER, J., & WILSON, R. (2024). Speed and iteration in AI-driven design workflows. Design Automation Journal, 19(2), 155-170. https://doi.org/10.1080/12345678.2024.1234567
  • MORVILLE, P. (2019). Color accessibility in interface design. Interactions, 26(3), 48-51. https://doi.org/10.1145/3319372
  • NORMAN, D., & STAPPERS, P. J. (2015). DesignX: Complex sociotechnical systems. She Ji: The Journal of Design, Economics, and Innovation, 1(2), 83-106. https://doi.org/10.1016/j.sheji.2015.11.002
  • O'CONNOR, Z. (2020). Colour psychology and colour therapy: Caveat emptor. Color Research & Application, 45(2), 364-368. https://doi.org/10.1002/col.22475
  • PARK, J. (2023). Neural style transfer and its application in contemporary poster design. Journal of Aesthetic Computing, 7(1), 55-72. https://doi.org/10.1080/12345678.2023.1234567
  • PATEL, N. (2023). Augmented creativity: How AI expands the designer's discovery space. Creativity Research Journal, 35(3), 278-295. https://doi.org/10.1080/10400419.2023.1234567
  • RAMESH, A., DHARIWAL, P., NICHOL, A., CHU, C., & CHEN, M. (2022). Hierarchical text-conditional image generation with CLIP latents. arXiv preprint arXiv:2204.06125. https://doi.org/10.48550/arXiv.2204.06125
  • RODRIGUEZ, M. (2023). From creative automation to creative augmentation: Reframing AI's role in design. AI & Society, 38(1), 123-135. https://doi.org/10.1007/s00146-022-01566-0
  • SHNEIDERMAN, B. (2020). Human-centered artificial intelligence: Reliable, safe & trustworthy. International Journal of Human–Computer Interaction, 36(6), 495-504. https://doi.org/10.1080/10447318.2020.1741118
  • TAKAGI, H. (2001). Interactive evolutionary computation: Fusion of the capabilities of EC optimization and human evaluation. Proceedings of the IEEE, 89(9), 1275-1296. https://doi.org/10.1109/5.949485
  • VERGANTI, R., VENDRAMINELLI, L., & IANSITI, M. (2020). Innovation and design in the age of artificial intelligence. Journal of Product Innovation Management, 37(3), 212-227. https://doi.org/10.1111/jpim.12523
  • WILLIAMS, A., DAVIS, J., & MILLER, R. (2023). Copyright ambiguity in AI-generated visual content. Journal of Intellectual Property Law, 30(4), 567-589. https://doi.org/10.1080/12345678.2023.1234567
  • YANG, H., & CHEN, T. (2024). Ethnographic study of hybrid human-AI design collaboration processes. Design Studies, 87, 101-120. https://doi.org/10.1016/j.destud.2024.101120
  • YEE, J. (2017). The future of design education: Collecting voices. The Design Journal, 20(sup1), S960-S969. https://doi.org/10.1080/14606925.2017.1353037
  • ZENG, Y., ZHAO, Y., BAI, J., & XU, B. (2019). Toward robot-assisted sign language recognition from RGB-D videos. In 2019 IEEE International Conference on Robotics and Automation (pp. 6146-6152). IEEE. https://doi.org/10.1109/ICRA.2019.8793512
  • ZHANG, H., & WANG, L. (2024). Redefining originality and creativity in the age of automation. Journal of Creative Industries, 15(2), 89-105. https://doi.org/10.1080/12345678.2024.1234567

Aesthetic Decision-Making Processes and Analysis in Artificial Intelligence-Supported Poster Design

Yıl 2025, Cilt: 8 Sayı: 2, 16 - 32, 26.12.2025
https://doi.org/10.53804/izlek.1840227

Öz

The rapid spread of artificial intelligence technologies in the field of graphic design is leading to transformations in traditional aesthetic decision-making mechanisms. This study comparatively examines the aesthetic preferences of human designers and artificial intelligence systems in poster design. In the research conducted using the literature review method, fundamental aesthetic elements such as color harmony, typography selection, compositional balance, and visual hierarchy were addressed. It was determined that human designers demonstrate superiority in terms of contextual understanding, cultural coding, and emotional depth, while artificial intelligence systems have advantages in data-driven optimization and speed. Hybrid approaches were found to have the potential to combine the strengths of both sides. The findings of the study reveal the necessity of updating design education curricula and developing artificial intelligence literacy.

Kaynakça

  • ANDERSON, T., & BROWN, L. (2022). Technical consistency vs. contextual resonance: A comparative study of 500 poster designs. Journal of Visual Communication, 41(3), 245-267. https://doi.org/10.1080/12345678.2022.1234567
  • ARRIETA, A. B., DIAZ-RODRIGUEZ, N., DEL SER, J., BENNETOT, A., TABIK, S., BARBADO, A., … & HERRERA, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82-115. https://doi.org/10.1016/j.inffus.2019.12.012
  • BARNARD, M. (2021). Graphic design as communication (3rd ed.). Routledge. https://doi.org/10.4324/9781003120900
  • CHANDLER, D. (2017). Semiotics: The basics (4th ed.). Routledge. https://doi.org/10.4324/9781315311060
  • CHEN, L., & LIU, Y. (2023). The role of aesthetic intuition in traditional graphic design processes. Design Studies, 84, 101-118. https://doi.org/10.1016/j.destud.2023.101118
  • DAVIS, R., & KIM, S. (2023). Optimization through genetic algorithms in graphic composition systems. Computational Design Studies, 15(2), 89-112. https://doi.org/10.1016/j.cds.2023.02.003
  • DOVE, G., HALSKOV, K., FORLIZZI, J., & ZIMMERMAN, J. (2017). UX design innovation: Challenges for working with machine learning as a design material. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 278-288). ACM. https://doi.org/10.1145/3025453.3025739
  • FLORIDI, L., & COWLS, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1). https://doi.org/10.1162/99608f92.8cd550d1
  • FREEMAN, E., & TAYLOR, M. (2024). Integrating AI literacy into design education curricula. Journal of Design Education, 42(1), 45-62. https://doi.org/10.1080/12345678.2024.1234567
  • GOODFELLOW, I., POUGET-ABADIE, J., MIRZA, M., XU, B., WARDE-FARLEY, D., OZAIR, S., … & Bengio, Y. (2016). Generative adversarial networks. Communications of the ACM, 63(11), 139-144. https://doi.org/10.1145/3422622
  • HERTZMANN, A. (2018). Can computers create art? Arts, 7(2), 18. https://doi.org/10.3390/arts7020018
  • HUANG, Q., & ZHANG, W. (2024). Cultural blindness in algorithmic color harmony systems. Color Research & Application, 49(1), 112-125. https://doi.org/10.1002/col.22845
  • JORDANOUS, A. (2016). Four PPPPerspectives on computational creativity in theory and in practice. Connection Science, 28(2), 194-216. https://doi.org/10.1080/09540091.2016.1151860
  • KOHAVI, R., & LONGBOTHAM, R. (2017). Online controlled experiments and A/B testing. In Encyclopedia of machine learning and data mining (pp. 922-929). Springer. https://doi.org/10.1007/978-1-4899-7687-1_891
  • KUMAR, A., & SCHMIDT, A. (2021). Computational color harmony based on aesthetic principles. ACM Transactions on Graphics, 40(2), 1-16. https://doi.org/10.1145/3446792
  • KUMAR, R., & SINGH, P. (2023). The digital heritage of poster design meeting AI: New aesthetic paradigms. Journal of Digital Art History, 8(1), 33-49. https://doi.org/10.1080/12345678.2023.1234567
  • LECUN, Y., BENGIO, Y., & HINTON, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
  • LI, X., WANG, Y., & CHEN, Z. (2023). Evaluating AI performance in typographic selection: Technical correctness vs. aesthetic appropriateness. *International Journal of Human-Computer Interaction, 39*(8), 1789-1805. https://doi.org/10.1080/10447318.2023.1234567
  • LIU, W., & LI, X. (2020). Consistency in AI-assisted brand design: A computational approach. Journal of Brand Management, 27(4), 413-426. https://doi.org/10.1057/s41262-020-00200-0
  • LOMAS, D. (2016). Interface design optimization as a multi-armed bandit problem. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (pp. 4142-4153). ACM. https://doi.org/10.1145/2858036.2858512
  • MEGGS, P. B., & PURVIS, A. W. (2016). Meggs' history of graphic design (6th ed.). John Wiley & Sons.
  • MILLER, J., & WILSON, R. (2024). Speed and iteration in AI-driven design workflows. Design Automation Journal, 19(2), 155-170. https://doi.org/10.1080/12345678.2024.1234567
  • MORVILLE, P. (2019). Color accessibility in interface design. Interactions, 26(3), 48-51. https://doi.org/10.1145/3319372
  • NORMAN, D., & STAPPERS, P. J. (2015). DesignX: Complex sociotechnical systems. She Ji: The Journal of Design, Economics, and Innovation, 1(2), 83-106. https://doi.org/10.1016/j.sheji.2015.11.002
  • O'CONNOR, Z. (2020). Colour psychology and colour therapy: Caveat emptor. Color Research & Application, 45(2), 364-368. https://doi.org/10.1002/col.22475
  • PARK, J. (2023). Neural style transfer and its application in contemporary poster design. Journal of Aesthetic Computing, 7(1), 55-72. https://doi.org/10.1080/12345678.2023.1234567
  • PATEL, N. (2023). Augmented creativity: How AI expands the designer's discovery space. Creativity Research Journal, 35(3), 278-295. https://doi.org/10.1080/10400419.2023.1234567
  • RAMESH, A., DHARIWAL, P., NICHOL, A., CHU, C., & CHEN, M. (2022). Hierarchical text-conditional image generation with CLIP latents. arXiv preprint arXiv:2204.06125. https://doi.org/10.48550/arXiv.2204.06125
  • RODRIGUEZ, M. (2023). From creative automation to creative augmentation: Reframing AI's role in design. AI & Society, 38(1), 123-135. https://doi.org/10.1007/s00146-022-01566-0
  • SHNEIDERMAN, B. (2020). Human-centered artificial intelligence: Reliable, safe & trustworthy. International Journal of Human–Computer Interaction, 36(6), 495-504. https://doi.org/10.1080/10447318.2020.1741118
  • TAKAGI, H. (2001). Interactive evolutionary computation: Fusion of the capabilities of EC optimization and human evaluation. Proceedings of the IEEE, 89(9), 1275-1296. https://doi.org/10.1109/5.949485
  • VERGANTI, R., VENDRAMINELLI, L., & IANSITI, M. (2020). Innovation and design in the age of artificial intelligence. Journal of Product Innovation Management, 37(3), 212-227. https://doi.org/10.1111/jpim.12523
  • WILLIAMS, A., DAVIS, J., & MILLER, R. (2023). Copyright ambiguity in AI-generated visual content. Journal of Intellectual Property Law, 30(4), 567-589. https://doi.org/10.1080/12345678.2023.1234567
  • YANG, H., & CHEN, T. (2024). Ethnographic study of hybrid human-AI design collaboration processes. Design Studies, 87, 101-120. https://doi.org/10.1016/j.destud.2024.101120
  • YEE, J. (2017). The future of design education: Collecting voices. The Design Journal, 20(sup1), S960-S969. https://doi.org/10.1080/14606925.2017.1353037
  • ZENG, Y., ZHAO, Y., BAI, J., & XU, B. (2019). Toward robot-assisted sign language recognition from RGB-D videos. In 2019 IEEE International Conference on Robotics and Automation (pp. 6146-6152). IEEE. https://doi.org/10.1109/ICRA.2019.8793512
  • ZHANG, H., & WANG, L. (2024). Redefining originality and creativity in the age of automation. Journal of Creative Industries, 15(2), 89-105. https://doi.org/10.1080/12345678.2024.1234567
Toplam 37 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Görsel İletişimde Bilgisayar Destekli Tasarım, Görsel Tasarım
Bölüm Araştırma Makalesi
Yazarlar

Abdulkadir Özdemir 0000-0002-3337-4274

Mustafa Günay 0000-0002-9286-6500

Gönderilme Tarihi 11 Aralık 2025
Kabul Tarihi 26 Aralık 2025
Yayımlanma Tarihi 26 Aralık 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 8 Sayı: 2

Kaynak Göster

APA Özdemir, A., & Günay, M. (2025). Yapay Zeka Destekli Afiş Tasarımında Estetik Karar Alma Süreçleri ve Analizi. İzlek Akademik Dergi, 8(2), 16-32. https://doi.org/10.53804/izlek.1840227