Digital Fashion Photography and Generative AI: Understanding Student Perceptions, Learning Experiences, and Creative Development at NIFT Bengaluru
Authors
Dr. B. Raja
Assistant Professor. Department of Fashion Communication, National Institute of Fashion Technology (NIFT), Bengaluru, Karnataka, 560102 (IN)
Article Information
DOI: 10.51583/IJLTEMAS.2026.150500258
Subject Category: Generative AI,
Volume/Issue: 15/5 | Page No: 3149-3160
Publication Timeline
Submitted: 2026-06-23
Published: 2026-06-23
Abstract
This qualitative research study investigates how students at the National Institute of Fashion Technology (NIFT), Bengaluru engage with Generative Artificial Intelligence (AI) in the context of digital fashion photography education. As AI tools such as Midjourney, DALL-E 3, Adobe Firefly, and Stable Diffusion are increasingly integrated into fashion media learning environments, critical questions arise around how students perceive these technologies, how they influence creative development, and what pedagogical and ethical tensions emerge in the classroom.
The study employed a qualitative research design, drawing on in-depth semi-structured interviews and open-ended questionnaires administered to a purposively selected sample of 42 students enrolled in undergraduate and postgraduate fashion communication and photography programmes at NIFT Bengaluru. Data were collected over a ten-week period (February–April 2026) and analysed using thematic analysis guided by Braun and Clarke’s (2006) six-phase framework.
Four principal themes emerged from the data: (i) AI as a creative scaffold—students described generative AI primarily as a tool for concept visualisation and ideation rather than final output generation; (ii) tension between technological enthusiasm and creative authenticity—many participants expressed ambivalence about AI’s impact on their identity as photographers; (iii) pedagogical gaps in AI literacy—students reported a lack of structured institutional guidance on the ethical and technical dimensions of AI use; and (iv) cultural and representational concerns—participants frequently highlighted the inadequacy of AI-generated imagery in reflecting South Indian and broader Indian aesthetic identities.
The findings underscore the need for AI-integrated, critically reflective fashion photography curricula at NIFT Bengaluru, and contribute original qualitative evidence to the growing scholarly discourse on AI-mediated creativity in fashion education.
Keywords
Generative AI, Digital Fashion Photography, Fashion Education, ,Student Perceptions, Creative Development, AI Literacy, Fashion Communication, Visual Aesthetics
This article is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
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References
1. Boden, M. A. (2004). The creative mind: Myths and mechanisms (2nd ed.). Routledge. [Google Scholar] [Crossref]
2. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. [Google Scholar] [Crossref]
3. Csikszentmihalyi, M. (1996). Creativity: Flow and the psychology of discovery and invention. HarperCollins. [Google Scholar] [Crossref]
4. Eckert, C., & Stacey, M. (2000). Sources of inspiration: A language of design. Design Studies, 21(5), 523–538. [Google Scholar] [Crossref]
5. Elgammal, A., Liu, B., Elhoseiny, M., & Mazzone, M. (2017). CAN: Creative adversarial networks generating ‘art’ by learning about styles and deviating from style norms. Proceedings of the 8th International Conference on Computational Creativity, 96–103. [Google Scholar] [Crossref]
6. Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., … & Vayena, E. (2020). An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707. [Google Scholar] [Crossref]
7. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., … & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 27. [Google Scholar] [Crossref]
8. Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33, 6840–6851. [Google Scholar] [Crossref]
9. Holland, D. (1997). Identity and agency in cultural worlds. Harvard University Press. [Google Scholar] [Crossref]
10. Kim, J., & Cho, S. (2023). Can consumers distinguish AI-generated from photographer-captured fashion imagery? A perceptual differentiation study. Journal of Fashion Marketing and Management, 27(4), 612–629. [Google Scholar] [Crossref]
11. McCormack, J., Gifford, T., & Hutchings, P. (2019). Autonomy, authenticity, authorship and intention in computer generated art. Proceedings of the 10th International Conference on Computational Creativity, 35–42. [Google Scholar] [Crossref]
12. Pasquale, F. (2020). New laws of robotics: Defending human expertise in the age of AI. Harvard University Press. [Google Scholar] [Crossref]
13. Pham, L., Nguyen, T., & Tran, H. (2023). Design students and AI tools: Navigating enthusiasm, anxiety, and institutional uncertainty. International Journal of Design Education, 17(2), 88–107. [Google Scholar] [Crossref]
14. Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 10684–10695. [Google Scholar] [Crossref]
15. Sharma, P., & Venkataraman, R. (2024). Artificial intelligence and Bollywood-adjacent fashion styling: A qualitative exploration of AI tool adoption and cultural representation concerns. Indian Journal of Fashion Technology, 12(1), 44–61. [Google Scholar] [Crossref]
16. Yilmaz, E., & Kiliç, B. (2024). Generative AI adoption in European fashion brand visual production: Patterns, motivations, and practitioner perspectives. Fashion Theory: The Journal of Dress, Body & Culture, 28(2), 201–228. [Google Scholar] [Crossref]
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