Yapay Zekâ (Prompt Sanatı): Yeni Bir Tasarım Dili Olarak Metin-Görsel Dönüşümü

Yıl 2025, Cilt: 8 Sayı: 2, 33 - 45, 26.12.2025

Abdulkadir Özdemir , Nuri Sezer

https://doi.org/10.53804/izlek.1840253

Öz

Dijital tasarım ekosistemi, üretken yapay zekânın getirdiği ontolojik bir dönüşüm yaşıyor. Görsel üretim, el becerisi yerine dilsel yetkinliğin öne çıktığı “Prompt Mühendisliği”ne evriliyor. Bu araştırma, metin-görsel sistemlerin görsel iletişimi yeniden yapılandırmasını, tasarımcıyı “zanaatkâr”dan “küratör” konumuna taşımasını ve yaratıcı endüstrilerdeki etkilerini inceliyor. İstemlerin (prompt) teknik komut değil, kültürel ve estetik kodları harekete geçiren dilsel araçlar olduğu saptanıyor. Yapay zekânın yaratıcılığı demokratikleştirdiği iddiası ile mesleki dönüşüm endişeleri eleştirel bir dengede tartışılıyor. Sonuç olarak, görsel okuryazarlık algoritmik düşünmeyle bütünleşiyor ve geleceğin tasarımcısı, imgeyi çizen değil, kelimelerle kodlayan bir “anlam mühendisi” olacak.

Anahtar Kelimeler

Görsel İletişim , Üretken Yapay Zekâ , Metin-Görsel Dönüşümü , Tasarım Eğitimi , Prompt

Artificial Intelligence (Prompt Art): New Design Language for Text-Visual Transformation

Öz

The digital design ecosystem is undergoing an ontological transformation driven by generative AI. Visual production is evolving from manual skill towards "Prompt Engineering," where linguistic proficiency takes precedence. This research examines how text-to-image systems are restructuring visual communication, shifting the designer's role from "craftsman" to "curator," and the implications for creative industries. It finds that prompts are not merely technical commands but linguistic tools that activate cultural and aesthetic codes. The claim that AI democratizes creativity is critically balanced against concerns of professional disruption. Consequently, visual literacy is integrating with algorithmic thinking, and the future designer will be a "meaning engineer" who codes images with words, rather than drawing them.

Anahtar Kelimeler

Visual Communication , Generative AI , Text-to-Image Transformation , Design Education , Prompt

Kaynakça

• AMABILE, T. M. (1996). Creativity in context: Update to the social psychology of creativity. Westview Press.
• BARTHES, R. (1977). Image, Music, Text. (S. Heath, Çev.). Hill and Wang.
• BENJAMIN, W. (1936). The Work of Art in the Age of Mechanical Reproduction.
• BERGER, J. (1972). Ways of Seeing. Penguin Books.
• BETKER, M., et al. (2023). DALL-E 3: Integrating DALL-E with a large language model. OpenAI Research Report.
• BODEN, M. A. (1998). Creativity and artificial intelligence. Artificial Intelligence, 103(1-2), 347-356.
• BODEN, M. A. (2004). The Creative Mind: Myths and Mechanisms. Routledge.
• BONFANTI, A. (2023). Generative AI and the future of stock imagery. Visual Media Review, 11(2).
• BROOKS, T., et al. (2024). Video generation models. OpenAI Research Report.
• BROWN, T., MANN, B., et al. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems (NeurIPS) 33.
• CAGAN, J., & Vogel, C. (2002). Creating Breakthrough Products: Innovation from Product Planning to Program Approval. FT Press.
• CRAWFORD, K. (2021). Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press.
• CROSS, N. (2006). Designerly Ways of Knowing. Springer.
• CSIKSZENTMIHALYI, M. (1996). Creativity: Flow and the Psychology of Discovery and Invention. Harper Perennial.
• Design Council (2023). Future of Design Report: AI and the Creative Economy.
• DIAKOPOULOS, N. (2020). Automating the News: How Algorithms are Rewriting the Media. Harvard University Press.
• EPSTEIN, E., et al. (2023). The impact of generative AI on creative agencies' workflow. Journal of Advertising Research, 63(3).
• EUBANKS, V. (2018). Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin's Press.
• EYAL, G., et al. (2021). The language of visual: Prompting as a new semiotics. AI and Society, 36(4).
• FRAILICH, R. R., KLINGER, T., et al. (2024). AI in the design studio: Affecting student creativity and learning. International Journal of Art & Design Education, 43(1).
• GOODFELLOW, I., POUGET-ABADIE, J., et al. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems (NeurIPS) 27.
• HO, J., JAIN, A., & ABBEEL, P. (2020). Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems (NeurIPS) 33.
• HU, E. J., SHEN, Y., et al. (2021). LoRA: Low-Rank Adaptation of Large Language Models. Proceedings of ICLR.
• JIANG, F., et al. (2023). Ethical implications of AI style mimicry in generative art. ACM Transactions on Computer-Human Interaction, 30(4).
• KOJIMA, T., et al. (2022). Large language models are zero-shot reasoners. Advances in Neural Information Processing Systems (NeurIPS) 35.
• KRESS, G., & van LEEUWEN, T. (2006). Reading Images: The Grammar of Visual Design. Routledge.
• LEACH, N. (2022). Architecture in the Age of Artificial Intelligence: An Introduction. Springer.
• LICKLIDER, J. C. R. (1960). Man-computer symbiosis. IRE Transactions on Human Factors in Electronics, HFE-1(1), 4-11.
• LIU, J., & CHILTON, L. B. (2022). Prompt engineering: A new frontier for human-computer interaction. Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems.
• MANOVCH, L. (2001). The Language of New Media. MIT Press.
• MANOVICH, L. (2023). AI and the Future of Art. (Online Publication).
• MARCUS, G., & DAVIS, E. (2019). Rebooting AI: Building Artificial Intelligence We Can Trust. Pantheon.
• MAZZONE, G., & ELGAMMAL, A. (2019). Art, creativity, and the potential of artificial intelligence. Arts, 8(1).
• MERON, I. (2022). Algorithm-Driven Design: The shift from craftsman to strategist. Harvard Business Review.
• NICOLETTI, M., & BASS, S. (2023). Algorithmic bias in generative image models: A study of cultural and gender stereotypes. Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT).
• NORMAN, D. A. (2023). Design for a Better World: Meaning, Emotion, and Ethics. MIT Press.
• OH, S. J., et al. (2023). Generative AI in automotive design: From concept to visualization. SAE International Journal of Vehicle Dynamics, 16(2).
• OPPENLAENDER, J. (2022). Prompt engineering for creative coding. Proceedings of the 2022 ACM on Creativity and Cognition.
• PAVLIK, J. V. (2023). The Future of Journalism in the Age of AI. Columbia University Press.
• POOLE, B., et al. (2022). DreamFusion: Text-to-3D using 2D diffusion. arXiv preprint arXiv:2209.14988.
• RADFORD, A., KIM, J. W., et al. (2021). Learning transferable visual models from natural language supervision. Proceedings of the 38th International Conference on Machine Learning (ICML).
• RAMESH, A., PAVLOV, M., et al. (2021). Zero-shot text-to-image generation. International Conference on Machine Learning (ICML).
• REYNOLDS, A., & MCDONELL, K. (2021). Prompt programming for large language models. Journal of Artificial Intelligence Research, 71.
• ROMBACH, P., BLATTMANN, A., et al. (2022). High-resolution image synthesis with latent diffusion models. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
• SAHARIA, A., CHAN, W., et al. (2022). Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arXiv:2205.11487.
• SAUSSURE, F. de (1916). Course in General Linguistics. McGraw-Hill.
• SCHÖN, D. A. (1983). The Reflective Practitioner: How Professionals Think In Action. Basic Books.
• SMITH, G., WHITEHEAD, J., et al. (2011). Procedural content generation in games: A textbook. MIT Press.
• VASWANI, A., SHAZEER, N., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems (NeurIPS) 30.
• ZHANG, L., AGRAWALA, M., et al. (2023). Adding conditional control to text-to-image diffusion models. arXiv preprint arXiv:2302.05543.
• ZHOU, Y., et al. (2022). Learning to prompt for continual learning. Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI).