STYLE TRANSFER IN PRINTING AND PHOTOGRAPHY EDUCATION - Kuppuswamy, Kishore; Arora, Neha; Verma, Subhash Kumar; Kumar, Amit; Bhargava, Anand; Jadhav, Manisha Tushar (ShodhKosh: Journal of Visual and Performing Arts, 2025)
Style transfer has become a strong cross-section of artificial intelligence and visual creativity, allowing to separate and alternative content and artistic style in digital imagery. This aspect is useful in teaching printing and photography in providing pedagogical opportunities in the combination of computational thinking with aesthetic discovery. In this paper, the researcher will explore the application of neural style transfer as a method of boosting creative learning in printing and photography programs. Based on the principles of visual perception and representation, the paper will explore convolutional neural networks as a feature extractor, and both optimization-based and feedforward style transfer methods as well as classical methods and generative adversarial methods. A curriculum integration model is suggested, which entails the incorporation of AI-supported style transfer in the modules of studio practice, image processing and print production. The structure focuses on learning by doing, as the students are able to engage in testing stylistic manipulations without the loss of control over the composition, palette and print limitations. A curated photographic and artistic dataset is created in an experimental methodology with curated data that has been modified to match the educational purpose and then model training and fine-tuning are performed to match the classroom settings. We test usability, learning engagement as well as perceived creative empowerment through user studies with students and educators. Findings reveal that style transfer tools have a great impact on cultivating the student awareness of visual style, increase the speed of experimentation, and foster the critical assessment of aesthetic choices.