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Journals / Görünüm / 2026 / Sayı: 17

Analysis of AI-Supported Artworks Based on Expert Evaluations: A Design-Based Research Study

Yapay Zekâ Destekli Sanat Eserlerinin Uzman Değerlendirmeleri Üzerinden Analizi: Tasarım Tabanlı Bir Araştırma

Gökçe Arifoğlu(RECEP TAYYİP ERDOĞAN ÜNİVERSİTESİ)0000-0002-4695-1421
İpek Sandıkçı(RECEP TAYYİP ERDOĞAN ÜNİVERSİTESİ, EĞİTİM FAKÜLTESİ)0009-0002-1665-6349
Journal
Görünüm
Pages
52–70
Publication Date
2026-06-18
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In conclusion, it has been demonstrated that, while AI-assisted art production is aesthetically acceptable, it is strongly dependent on human guidance in terms of creative expression.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study aims to examine digital artworks created using generative AI tools based on aesthetic value, creativity, technical proficiency and emotional impact, through the lens of expert evaluations.
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Key Findings & Data:Within the scope of the study, 10 digital artworks based on the theme of "loneliness" were produced and evaluated using a measurement tool developed by three subject experts.
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Conclusion & Impact:In conclusion, it has been demonstrated that, while AI-assisted art production is aesthetically acceptable, it is strongly dependent on human guidance in terms of creative expression.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Yapay zekâ destekli sanat · Dijital sanat eğitimi · Tasarım tabanlı araştırma

Abstract

This study aims to examine digital artworks created using generative AI tools based on aesthetic value, creativity, technical proficiency and emotional impact, through the lens of expert evaluations. The study was designed using a design-based research approach and the AI-supported production process was carried out iteratively. Within the scope of the study, 10 digital artworks based on the theme of "loneliness" were produced and evaluated using a measurement tool developed by three subject experts. To assess the reliability of the data collection tool, Cronbach's alpha coefficient was calculated, and the scale was found to have high internal consistency (α = 0.88). The inter-rater reliability was examined using Kendall’s W analysis and a significant level of agreement was found (W = 0.72, p <0.05). The findings show that the works received high scores for aesthetic value and technical proficiency, but were relatively lower in terms of creativity and originality. In conclusion, it has been demonstrated that, while AI-assisted art production is aesthetically acceptable, it is strongly dependent on human guidance in terms of creative expression. This study contributes to the field of art education by providing empirical data on the evaluation of art produced with the support of artificial intelligence.

Özet

Bu araştırmanın amacı, üretken yapay zekâ araçlarıyla oluşturulan dijital sanat eserlerinin estetik değer, yaratıcılık, teknik yeterlik ve duygusal etki gibi ölçütler temelinde uzman değerlendirmeleri aracılığıyla incelenmesidir. Çalışma, tasarım tabanlı araştırma (Design-Based Research) yaklaşımı doğrultusunda yapılandırılmış ve yapay zekâ destekli üretim süreci iteratif bir döngü içinde gerçekleştirilmiştir. Araştırma kapsamında “yalnızlık” teması doğrultusunda 10 dijital sanat eseri üretilmiş ve bu eserler üç alan uzmanı tarafından geliştirilen ölçme aracı ile değerlendirilmiştir. Veri toplama aracının güvenirliği için Cronbach Alpha katsayısı hesaplanmış ve ölçeğin yüksek düzeyde iç tutarlılığa sahip olduğu belirlenmiştir (α = 0.88). Uzmanlar arası uyum Kendall’s W analizi ile incelenmiş ve anlamlı düzeyde uyum olduğu görülmüştür (W = 0,72, p < 0.05). Bulgular, eserlerin estetik değer ve teknik yeterlik açısından yüksek puanlar aldığını, buna karşın yaratıcılık ve özgünlük boyutlarında görece düşük değerlendirildiğini göstermektedir. Sonuç olarak, yapay zekâ destekli sanat üretimlerinin estetik açıdan kabul edilebilir düzeyde olduğu, ancak yaratıcı ifade bakımından insan yönlendirmesine güçlü biçimde bağlı olduğu ortaya konmuştur. Bu çalışma, yapay zekâ ile sanat üretimine ilişkin değerlendirme temelli ampirik veri sunarak sanat eğitimi alanına katkı sağlamaktadır.

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Cite This Article

Arifoğlu, G., Sandıkçı, İ. (2026). Analysis of AI-Supported Artworks Based on Expert Evaluations: A Design-Based Research Study. Görünüm, Sayı: 17, 52–70. https://doi.org/10.5281/zenodo.20744205

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References (33)

  1. Arnheim, R. (1974). Art and visual perception: A psychology of the creative eye (Rev. ed.). University of California Press.
  2. Ballı, Ö. (2020). Yapay Zekâ ve Sanat Uygulamaları Üzerine Güncel Bir Değerlendirme. Sanat ve Tasarım Dergisi, 26, 277-306. https://izlik.org/JA94LE95UE.
  3. Blok, V. (2022). The role of human creativity in human-technology relations. Philosophy & Technology, 35(3), 59. https://doi.org/10.1007/s13347-022-00559-7.
  4. Boden, M. A. (2004). The creative mind: Myths and mechanisms. Routledge.
  5. Brown, A. L. (1992). Design experiments: Theoretical and methodological challenges in creating complex interventions in classroom settings. The Journal of the Learning Sciences, 2(2), 141–178. https://doi.org/10.1207/s15327809jls0202_2.
  6. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A.,. Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877-1901. https://doi.org/10.48550/arXiv.2005.14165.
  7. Crawford, K. (2021). The atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press. https://doi.org/10.2307/j.ctv1ghv45t.
  8. Das, P., & Varshney, L. R. (2022). Explaining artificial intelligence generation and creativity: Human interpretability for novel ideas and artifacts. IEEE Signal Processing Magazine, 39(4), 85-95. https://doi.org/10.1109/MSP.2022.3141365.
  9. Flusser, V. (2011). Into the universe of technical images (N. A. Roth, Trans.). University of Minnesota Press. (Original work published 1985).
  10. Gözet, M., Filiz, U., & Yılmaz, A. E. (2023). Üretken Yapay Zekâ. International Journal of Multidisciplinary Studies and Innovative Technologies, 7(1), 32-40.
  11. Güney, E. & Yavuz, H. (2020). Yapay Zekâ ile Sanatsal Üretim Pratiğinde Sanatçının Rolü ve Değişen Sanat Olgusu. Sanat ve Tasarım Dergisi, 26, 415-439. https://izlik.org/JA39CF64NC.
  12. Latikka, R., Bergdahl, J., Savela, N., & Oksanen, A. (2023). AI as an artist? A two-wave survey study on attitudes toward using artificial intelligence in art. Poetics. https://doi.org/10.1016/j.poetic.2023.101839.
  13. Legendre, P. (2005). Species associations: The Kendall coefficient of concordance revisited. Journal of Agricultural, Biological, and Environmental Statistics, 10(2), 226–245. https://doi.org/10.1198/108571105X46642.
  14. Lupton, E. & Phillips, J. C. (2015). Graphic design: The new basics (revised and expanded). Chronicle Books.
  15. Lyu, Y., Wang, X., Lin, R., & Wu, J. (2022). Communication in human–AI co-creation: Perceptual analysis of paintings generated by text-to-image system. Applied Sciences, 12(22), 1-19.
  16. Manovich, L. (2023). Artificial aesthetics: Generative AI, art and visual media. Strelka Press.
  17. May, R. (2013). Yaratma cesareti (Çev. A. Oysal). Metis Yayınları.
  18. Mazzone, M., & Elgammal, A. (2019). Art, creativity, and the potential of artificial intelligence. Arts, 8(1), 26. https://doi.org/10.3390/arts8010026.
  19. Mutlu, E. (2025). Yaratıcılığın Sınırları: Yapay Zekâ Yaratıcılığına Bir Bakış. Moment Dergi, 11(2), 422-446. https://doi.org/10.17572/mj2024.2.422-446.
  20. Okoli, C. & Pawlowski, S. D. (2004). The Delphi method as a research tool: an example, design considerations and applications. Information & Management, 42, 15–29. https://doi.org/10.1016/j.im.2003.11.002.
  21. Raisch, S. & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. The Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072.
  22. Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M. & Sutskever, I. (2021). Zero-shot text-to-image generation. In M. Meila & T. Zhang (Eds.), ICML (pp. 8821-8831). PMLR. https://proceedings.mlr.press/v139/ramesh21a.html.
  23. Reeves, T. (2006). Design research from a technology perspective. In Educational design research (pp. 64-78). Routledge.
  24. Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10674–10685).
  25. Russell, S. & Norvig, P. (2020). Artificial intelligence: A modern approach (4th ed.). Pearson.
  26. Sawyer, R. K., & Henriksen, D. (2023). Explaining creativity: The science of human innovation (3rd ed.). Oxford University Press. https://doi.org/10.1093/oso/9780197747537.001.0001.
  27. Siegel, S. & Castellan Jr., N. J. (1988). Nonparametric statistics for the behavioral sciences (2nd ed.). McGraw-Hill. Soddu, C. (2018). AI organic complexity in generative art. In C. Soddu & E. Colabella (Eds.), XXI. Generative Art Conference (pp. 68–79). Verona.
  28. Sternberg, R. J. (1999). Successful intelligence: Finding a balance. Trends in Cognitive Sciences, 3(11), 436–442. Taluğ, D. Y., & Eken, B. (2023). Intersection of human creativity and artificial intelligence in visual design. Journal of Art and Iconography, 4(1), 18-29. https://doi.org/10.5152/articon.2023.1256114.
  29. Tigre Moura, F., Castrucci, C., & Hindley, C. (2023). Artificial intelligence creates art? An experimental investigation of value and creativity perceptions. The Journal of Creative Behavior. https://doi.org/10.1002/jocb.600.
  30. Trach, Y. (2021). Artificial intelligence as a tool for creating and analysing works of art. Culture and Arts in the Modern World, 22, 164-173.
  31. Wilson, H. J. & Daugherty, P. R. (2018). Collaborative intelligence: Humans and AI are joining forces. Harvard Business Review, 96, 114-123.
  32. Yakar, G. & Kınık, M. (2020). Yapay Zekâ ile Üretilen Görsel Sanatlar Eserlerinde Fikrî Mülkiyet. Art-E Sanat Dergisi, 13(26), 490-516. https://doi.org/10.21602/sduarte.795610.
  33. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A.,. Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877-1901. https://doi.org/10.48550/arXiv.2005.14165.