Visual rhetoric and AI-generated visuals in graphic design education: reconstructing meaning, authorship, and critical practice
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Abstract
This study explores the rising importance of AI-generated visuals in graphic design studies and their impact on visual rhetoric, authorship, and conceptual thinking. The rise of accessibility of such technologies in university-level education brings many questions about originality, critical analysis of images, ethical responsibility, and place of the author/designer in the process of creation and meaning-building. In spite of the fact that AI systems create sophisticated images almost automatically, there is a need to ensure active visual thinking of students rather than passive usage of technology.
The research follows a qualitative descriptive-analytical approach with a focus on the theoretical framework of visual rhetoric and further concepts developed by Barthes, Groupe μ and Durand concerning substitution, omission, addition and permutation in images. This study discusses certain educational strategies employed in graphic design programs and investigates the effect of AI-generated visuals on conceptualizing ideas, working with typography, composition and symbolism in studio practice.
As a result, it was found out that such visuals contribute to brainstorming, experimenting, and fast prototyping in case they are employed critically; yet, an excessive use of AI leads to a reduction in originality, a shallow understanding of concepts, and responsibility. Thus, it is concluded that AI should be utilized not as an alternative but as a design assistant.
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References
Acar, O. A. (2023). Generative AI in creative work: Implications for design education and professional practice. Design Studies, 89, 101230.
Barthes, R. (1977). Image, music, text. New York, NY: Hill and Wang.
Blythe, M. (2023). Authorship after automation: Design responsibility in the age of generative systems. Journal of Visual Communication, 22(4), 311–329.
Buchanan, R. (2001). Design research and the new learning. Design Issues, 17(4), 3–23.
Coomer, A. (2022). AI art and visual culture: Algorithmic imagery and the question of authorship. AI & Society, 37(2), 913–927.
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2023). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International. Advance online publication.
Durand, J. (1987). Rhetorical figures in the advertising image. In J. Umiker-Sebeok (Ed.), Marketing and semiotics (pp. 47–52). Berlin, Germany: Mouton de Gruyter.
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Wright, R., … (2023). So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative AI. International Journal of Information Management, 71, 102642.
Frascara, J. (2004). Communication design: Principles, methods, and practice. New York, NY: Allworth Press.
Groupe μ. (1992). Traité du signe visuel. Paris, France: Seuil.
Hertzmann, A. (2018). Can computers create art? Arts, 7(2), 18.
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274.
Kress, G., & van Leeuwen, T. (2021). Reading images: The grammar of visual design (3rd ed.). London, UK: Routledge.
Lupton, E. (2014). Graphic design thinking: Beyond brainstorming. New York, NY: Princeton Architectural Press.
Manovich, L. (2018). AI aesthetics. Moscow, Russia: Strelka Press.
McCosker, A., & Wilken, R. (2024). Generative media and educational ethics: AI image production in university learning environments. Higher Education Research & Development, 43(1), 55–71.
Norman, D. (2013). The design of everyday things (Revised and expanded ed.). New York, NY: Basic Books.
Poynor, R. (2003). No more rules: Graphic design and postmodernism. London, UK: Laurence King Publishing.
Sullivan, G. (2022). Art practice as research: Inquiry in visual arts and design education (2nd ed.). Thousand Oaks, CA: Sage Publications.
Tavin, K. (2010). Visual culture and critical pedagogy in art education. Studies in Art Education, 51(3), 197–213.
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), 15.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(1), 39.
Zhang, J., & Dong, A. (2023). Prompting creativity: The impact of AI image generators on design ideation. Creativity Research Journal, 35(3), 409–425.