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Artificial ıntelligence and ınnovation in graphic design: Pushing The Boundaries

Yıl 2025, Cilt: 28 Sayı: 54, 681 - 698, 16.12.2025
https://doi.org/10.31795/baunsobed.1752840

Öz

This study aims to examine the transformative impact of artificial intelligence on graphic design practice and thinking. The primary objective of the research is to reveal how AI transcends being merely a tool to become an active component of the creative process and to demonstrate the symbiotic relationship this establishes with human creativity. The study was conducted by adopting a qualitative research method. The findings show that AI-supported design tools bring speed, diversity, and a new dimension of originality to the process. It was determined that boundaries are expanding, particularly in fields such as image processing, style transfer, and automated content generation. The contribution of the research to the field is that it demonstrates how this technological transformation not only accelerates production processes but also reshapes the aesthetic, conceptual, philosophical, and pedagogical dimensions of design. In this respect, the study positions human-machine collaboration as one of the fundamental dynamics of contemporary creativity. The importance of the study lies in its highlighting of a paradigm shift by critically evaluating the critical issues brought about by the use of AI, such as originality, copyright, ethical boundaries, and cultural representation. However, the limitations of the study are the limited number of cases examined and the potential for the findings to change over time due to the constantly evolving nature of the technology.

Kaynakça

  • Adams, R. (2021). Ethical implications of AI-generated visual content. Journal of Digital Ethics, 6(2), 45–60.
  • Adams, R. (2022). The economics of AI-generated art. Journal of Art Market Studies, 5(2), 45–62.
  • Anderson, L., ve Park, S. (2023). Ethical implications of training data in creative AI. AI Ethics Review, 8(1), 112–128.
  • Anderson, R., ve Park, S. (2023). Ethical implications of AI in creative education. Journal of Design Ethics, 12(3), 45–60.
  • Baker, T. (2021). Interdisciplinary design education: Challenges and opportunities. Design Studies, 34(2), 78–94.
  • Baker, T. (2021). The Belamy case: AI art in the auction house. Art Law Journal, 12(3), 78–94.
  • Brown, L. (2021). Hybrid learning in design education: Integrating AI and traditional methods. International Journal of Art and Design, 15(1), 112–128.
  • Brown, L. (2022). Copyright challenges in AI-assisted design. Intellectual Property Quarterly, 35(1), 112–128.
  • Chen, H., ve Wong, K. (2023). Cross-disciplinary innovation in AI-driven design. Tech and Creativity, 8(4), 201–218.
  • Clark, B. (2022). Transparency in AI-assisted design. Design Ethics Quarterly, 15(4), 201–218.
  • Davis, M. (2021). Copyright law in the age of artificial creativity. Intellectual Property Review, 34(2), 155–170.
  • Davis, M. (2021). The risks of over-reliance on AI in creative processes. AI ve Society, 36(2), 155–170.
  • Garcia, E. (2023). Manipulation risks in AI-powered advertising. Marketing Ethics Review, 8(3), 78–94.
  • Garcia, E., ve Martinez, P. (2022). Attribution standards for AI-generated designs. Journal of Digital Ethics, 7(1), 33–50.
  • Garcia, E., ve Martinez, P. (2022). Bias in AI-generated designs: A critical analysis. Journal of Digital Ethics, 5(1), 33–50.
  • Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., ve Bengio, Y. (2014). Generative adversarial nets. In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, ve K. Q. Weinberger (Eds.), Advances in neural information processing systems (Vol. 27, pp. 2672–2680). Curran Associates, Inc. https://doi.org/10.5555/2969033.2969125
  • Gombrich, E. H. (2000). The story of art (16th ed.). Phaidon Press.
  • Harris, D. (2021). Best practices for documenting AI contributions in design. Creative Technology Reports, 14(3), 89–104.
  • Harris, D. (2021). Responsible AI practices in design education. Ethics in Technology, 14(3), 89–104.
  • Hertzmann, A. (2020). Visual indeterminacy in GAN art. Leonardo, 53(4), 373–378. https://doi.org/10.1162/leon_a_01912
  • Johnson, K., ve Smith, R. (2023). Legal status of AI-generated artworks. Harvard Technology Law Review, 46(1), 1–28.
  • Kim, S. (2023). Professional standards for ethical AI use in design. Design Education Journal, 16(4), 201–218.
  • Lee, J. (2021). Data privacy in creative AI applications. Technology and Society, 14(2), 155–170.
  • Lee, J., ve Kim, S. (2023). Project-based learning in AI-enhanced design education. Educational Technology Research, 41(2), 67–82.
  • Lee, J., ve Kim, S. (2023). Reproduction challenges in AI art. Digital Media Studies, 21(2), 67–82. Manovich, L. (2018). AI aesthetics. Strelka Press.
  • Miller, A. (2019). The artist in the machine: The world of AI-powered creativity. MIT Press.
  • Nguyen, T. (2023). Defining creativity in human-AI collaboration. AI ve Society, 38(1), 55–70.
  • Nguyen, T., ve Tran, V. (2023). Balancing manual and digital skills in design education. Design Pedagogy, 19(1), 55–70.
  • Roberts, A. (2022). Data provenance in creative AI systems. Data Science Journal, 19(4), 210–225.
  • Roberts, A. (2022). Developing industry standards for ethical design. Professional Communication Quarterly, 25(1), 33–50.
  • Robinson, A. (2023). Market dynamics of AI-generated designs. Art Market Research, 10(2), 134–150.
  • Robinson, A., ve Clark, B. (2022). Data visualization and aesthetic design: A multidisciplinary approach. Visual Communication, 21(4), 210–225.
  • Russell, S. J., ve Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
  • Smith, R., ve Johnson, L. (2022). Practical applications of AI in branding and UI design. Journal of Creative Technology, 10(2), 134–150.
  • Taylor, R., Wilson, E., ve Brown, L. (2022). Ownership models for AI creations. Journal of Intellectual Property Law, 29(1), 22–38.
  • Thompson, K. (2023). Ethical sourcing of training data for creative AI. AI Policy Review, 9(1), 77–92.
  • Thompson, K. (2023). Global standards for ethical AI in design. AI Policy Review, 7(1), 22–38.
  • Thompson, K., ve Davis, M. (2023). Ownership models for AI-generated artworks. Art Law Review, 38(1), 22–38.
  • Wilson, E. (2022). The decline of manual skills in the age of AI. Art Education Today, 18(3), 77–92.
  • Wilson, E. (2023). Commercial considerations for AI-assisted designs. Design Business Quarterly, 18(3), 45–60.
  • Wilson, E., Zhang, H., ve Park, S. (2022). Bias detection in AI design tools. AI Ethics Journal, 7(1), 77–92.
  • Zhang, Y., ve Park, J. (2023). Responsible AI practices in graphic design. Digital Creativity, 12(3), 89–104.

Yapay zeka ve grafik tasarımda inovasyon: Sınırları Zorlamak

Yıl 2025, Cilt: 28 Sayı: 54, 681 - 698, 16.12.2025
https://doi.org/10.31795/baunsobed.1752840

Öz

Bu çalışma, yapay zekânın grafik tasarım pratiği ve düşüncesi üzerindeki dönüştürücü etkisini incelemeyi amaçlamaktadır. Araştırmanın temel hedefi, yapay zekânın sadece bir araç olmanın ötesine geçerek yaratıcı sürecin etkin bir bileşeni haline gelişini ve bu durumun insan yaratıcılığı ile kurduğu simbiyotik ilişkiyi ortaya koymaktır. Çalışma, nitel araştırma yöntemi benimsenerek yürütülmüştür. Elde edilen bulgular, yapay zekâ destekli tasarım araçlarının sürece hız, çeşitlilik ve yeni bir özgünlük boyutu kattığını göstermektedir. Özellikle görüntü işleme, stil transferi ve otomatik içerik üretimi gibi alanlarda sınırların genişlediği tespit edilmiştir. Araştırmanın alana katkısı, bu teknolojik dönüşümün yalnızca üretim aşamalarını hızlandırmakla kalmayıp, tasarımın estetik, kavramsal, felsefi ve pedagojik boyutlarını da yeniden şekillendirdiğini göstermesidir. Bu yönüyle çalışma, insan-makine işbirliğini çağdaş yaratıcılığın temel dinamiklerinden biri olarak konumlandırmaktadır. Çalışmanın önemi, otonom sistem kullanımının beraberinde getirdiği özgünlük, telif hakkı, etik sınırlar ve kültürel temsil gibi kritik sorunları eleştirel bir bakış açısıyla değerlendirerek paradigmal bir kaymanın altını çizmesidir. Ancak, çalışmanın sınırlılıkları incelenen örneklerin sayıca sınırlı oluşu ve teknolojinin sürekli gelişen doğası nedeniyle bulguların zamanla değişebilme potansiyelidir.

Kaynakça

  • Adams, R. (2021). Ethical implications of AI-generated visual content. Journal of Digital Ethics, 6(2), 45–60.
  • Adams, R. (2022). The economics of AI-generated art. Journal of Art Market Studies, 5(2), 45–62.
  • Anderson, L., ve Park, S. (2023). Ethical implications of training data in creative AI. AI Ethics Review, 8(1), 112–128.
  • Anderson, R., ve Park, S. (2023). Ethical implications of AI in creative education. Journal of Design Ethics, 12(3), 45–60.
  • Baker, T. (2021). Interdisciplinary design education: Challenges and opportunities. Design Studies, 34(2), 78–94.
  • Baker, T. (2021). The Belamy case: AI art in the auction house. Art Law Journal, 12(3), 78–94.
  • Brown, L. (2021). Hybrid learning in design education: Integrating AI and traditional methods. International Journal of Art and Design, 15(1), 112–128.
  • Brown, L. (2022). Copyright challenges in AI-assisted design. Intellectual Property Quarterly, 35(1), 112–128.
  • Chen, H., ve Wong, K. (2023). Cross-disciplinary innovation in AI-driven design. Tech and Creativity, 8(4), 201–218.
  • Clark, B. (2022). Transparency in AI-assisted design. Design Ethics Quarterly, 15(4), 201–218.
  • Davis, M. (2021). Copyright law in the age of artificial creativity. Intellectual Property Review, 34(2), 155–170.
  • Davis, M. (2021). The risks of over-reliance on AI in creative processes. AI ve Society, 36(2), 155–170.
  • Garcia, E. (2023). Manipulation risks in AI-powered advertising. Marketing Ethics Review, 8(3), 78–94.
  • Garcia, E., ve Martinez, P. (2022). Attribution standards for AI-generated designs. Journal of Digital Ethics, 7(1), 33–50.
  • Garcia, E., ve Martinez, P. (2022). Bias in AI-generated designs: A critical analysis. Journal of Digital Ethics, 5(1), 33–50.
  • Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., ve Bengio, Y. (2014). Generative adversarial nets. In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, ve K. Q. Weinberger (Eds.), Advances in neural information processing systems (Vol. 27, pp. 2672–2680). Curran Associates, Inc. https://doi.org/10.5555/2969033.2969125
  • Gombrich, E. H. (2000). The story of art (16th ed.). Phaidon Press.
  • Harris, D. (2021). Best practices for documenting AI contributions in design. Creative Technology Reports, 14(3), 89–104.
  • Harris, D. (2021). Responsible AI practices in design education. Ethics in Technology, 14(3), 89–104.
  • Hertzmann, A. (2020). Visual indeterminacy in GAN art. Leonardo, 53(4), 373–378. https://doi.org/10.1162/leon_a_01912
  • Johnson, K., ve Smith, R. (2023). Legal status of AI-generated artworks. Harvard Technology Law Review, 46(1), 1–28.
  • Kim, S. (2023). Professional standards for ethical AI use in design. Design Education Journal, 16(4), 201–218.
  • Lee, J. (2021). Data privacy in creative AI applications. Technology and Society, 14(2), 155–170.
  • Lee, J., ve Kim, S. (2023). Project-based learning in AI-enhanced design education. Educational Technology Research, 41(2), 67–82.
  • Lee, J., ve Kim, S. (2023). Reproduction challenges in AI art. Digital Media Studies, 21(2), 67–82. Manovich, L. (2018). AI aesthetics. Strelka Press.
  • Miller, A. (2019). The artist in the machine: The world of AI-powered creativity. MIT Press.
  • Nguyen, T. (2023). Defining creativity in human-AI collaboration. AI ve Society, 38(1), 55–70.
  • Nguyen, T., ve Tran, V. (2023). Balancing manual and digital skills in design education. Design Pedagogy, 19(1), 55–70.
  • Roberts, A. (2022). Data provenance in creative AI systems. Data Science Journal, 19(4), 210–225.
  • Roberts, A. (2022). Developing industry standards for ethical design. Professional Communication Quarterly, 25(1), 33–50.
  • Robinson, A. (2023). Market dynamics of AI-generated designs. Art Market Research, 10(2), 134–150.
  • Robinson, A., ve Clark, B. (2022). Data visualization and aesthetic design: A multidisciplinary approach. Visual Communication, 21(4), 210–225.
  • Russell, S. J., ve Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
  • Smith, R., ve Johnson, L. (2022). Practical applications of AI in branding and UI design. Journal of Creative Technology, 10(2), 134–150.
  • Taylor, R., Wilson, E., ve Brown, L. (2022). Ownership models for AI creations. Journal of Intellectual Property Law, 29(1), 22–38.
  • Thompson, K. (2023). Ethical sourcing of training data for creative AI. AI Policy Review, 9(1), 77–92.
  • Thompson, K. (2023). Global standards for ethical AI in design. AI Policy Review, 7(1), 22–38.
  • Thompson, K., ve Davis, M. (2023). Ownership models for AI-generated artworks. Art Law Review, 38(1), 22–38.
  • Wilson, E. (2022). The decline of manual skills in the age of AI. Art Education Today, 18(3), 77–92.
  • Wilson, E. (2023). Commercial considerations for AI-assisted designs. Design Business Quarterly, 18(3), 45–60.
  • Wilson, E., Zhang, H., ve Park, S. (2022). Bias detection in AI design tools. AI Ethics Journal, 7(1), 77–92.
  • Zhang, Y., ve Park, J. (2023). Responsible AI practices in graphic design. Digital Creativity, 12(3), 89–104.
Toplam 42 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Güzel Sanatlar, Görsel Sanatlar (Diğer)
Bölüm Derleme
Yazarlar

Abdulkadir Özdemir 0000-0002-3337-4274

Gönderilme Tarihi 28 Temmuz 2025
Kabul Tarihi 7 Kasım 2025
Yayımlanma Tarihi 16 Aralık 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 28 Sayı: 54

Kaynak Göster

APA Özdemir, A. (2025). Yapay zeka ve grafik tasarımda inovasyon: Sınırları Zorlamak. Balıkesir Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 28(54), 681-698. https://doi.org/10.31795/baunsobed.1752840

BAUNSOBED