AI-DRIVEN PHOTOGRAPHY CURRICULUM FOR ART SCHOOLS
DOI:
https://doi.org/10.29121/shodhkosh.v6.i2s.2025.6714Keywords:
Artificial Intelligence, Education of Photography, Computer Vision, Generative Adversarial Networks, AI Art, Curriculum Development, Image Processing, Machine Learning, Creative Pedagogy, Ethical AIAbstract [English]
The adoption of the Artificial Intelligence (AI) in art education has provided a paradigm shift in the teaching, learning, and practice of photography. The proposed AI-based Photography Curriculum at the Art Schools is expected to balance the principles of traditional photography and the latest AI technologies so that the students are able to combine the capability to creatively explore the new technologies and the ability to master the camera. The modules are dedicated to automated image curation, style transfer, facial recognition ethics, and intelligent editing tools, which will introduce students to the nature of AI-based artistic processes in their entirety. The curriculum is also pedagogically based on a hybrid approach that integrates theory, real life learning, and project based experimentation. Algorithms efficiency is measured using quantitative performance, such as the accuracy of object recognition and the accuracy of image classification, whereas creativity, originality, and conceptual knowledge are evaluated with the help of qualitative measurement. The incorporation of mathematical underpinnings enhances insight into the process in which the neural networks acquire visual information. As well, AI-generated art, authorship rights, and data bias have ethical aspects ingrained into them to promote responsible artists. The future of the paradigm of education of photography as an art is the product of this AI-driven curriculum that is destined to help future artists find their way in the unstable overlap of technology and imagination.
References
Bautista, P., and Inventado, P. S. (2021). Protecting Student Privacy with Synthetic Data From Generative Adversarial Networks. In I. Roll, D. McNamara, S. Sosnovsky, R. Luckin, and V. Dimitrova (Eds.), Artificial Intelligence in Education, 66–70. Springer. https://doi.org/10.1007/978-3-030-78292-4_12 DOI: https://doi.org/10.1007/978-3-030-78270-2_11
Bethencourt-Aguilar, A. (2023). The Use of Gans in Educational Contexts: from Data Augmentation to Generative Learning. Computers in Human Behavior Reports, 9, 100278. https://doi.org/10.1016/j.chbr.2023.100278 DOI: https://doi.org/10.1016/j.chbr.2023.100278
Bethencourt-Aguilar, A., Area-Moreira, M., Sosa-Alonso, J. J., and Castellano-Nieves, D. (2021). The Digital Transformation of Postgraduate Degrees: A Study on Academic Analytics at the University of La Laguna. In Proceedings of the XI International Conference on Virtual Campus (JICV)). IEEE, 1-4. https://doi.org/10.1109/JICV53208.2021.9600402 DOI: https://doi.org/10.1109/JICV53222.2021.9600311
Bian, C., Wang, X., and Lee, D. (2025). Effects of AI-Generated Images in Visual Art Education: A Controlled Classroom Study. Education and Information Technologies, 30(1), 289–310. https://doi.org/10.1007/s10639-024-12491-7
Condorelli, F., and Berti, F. (2025). Creativity and Awareness in Co-Creation of Art Using Artificial Intelligence-Based Systems in Heritage Education. Heritage, 8(5), 157. https://doi.org/10.3390/heritage8050157 DOI: https://doi.org/10.3390/heritage8050157
He, Y., Sun, J., and Luo, W. (2025). Enhancing Art Creation Through AI-Based Generative Systems. Journal of Visual Computing and Art Education, 17(2), 155–170.
Heaton, R., Low, J., and Chen, V. (2024). AI Art Education—Artificial or Intelligent? Transformative Pedagogic Reflections from Three Art Educators in Singapore. Pedagogies: An International Journal, 19(1), 1–13. https://doi.org/10.1080/1554480X.2024.2395260 DOI: https://doi.org/10.1080/1554480X.2024.2395260
Le Monde Culture. (2025). L’art et l’intelligence Artificielle: Nouvelles Frontières Pédagogiques. Le Monde.
Sáez-Velasco, S., Alaguero-Rodríguez, M., Delgado-Benito, V., and Rodríguez-Cano, S. (2024). Analysing the Impact of Generative AI in Arts Education: A Cross-Disciplinary Perspective of Educators and Students In Higher Education. Informatics, 11(2), 37. https://doi.org/10.3390/informatics11020037 DOI: https://doi.org/10.3390/informatics11020037
Wadibhasme, R. N., Chaudhari, A. U., Khobragade, P., Mehta, H. D., Agrawal, R., and Dhule, C. (2024). Detection and Prevention of Malicious Activities in Vulnerable Network Security Using Deep Learning. In Proceedings of the 2024 International Conference on Innovations and Challenges in Emerging Technologies (ICICET). IEEE, 1-6. https://doi.org/10.1109/ICICET59348.2024.10616289 DOI: https://doi.org/10.1109/ICICET59348.2024.10616289
Yan, X., Shao, F., Chen, H., and Jiang, Q. (2024). Hybrid CNN-Transformer Based Meta-Learning Approach for Personalized Image Aesthetics Assessment. Journal of Visual Communication and Image Representation, 98, 104044. https://doi.org/10.1016/j.jvcir.2024.104044 DOI: https://doi.org/10.1016/j.jvcir.2023.104044
Yi, M. (2025). Reinforcement Learning and style-adaptive GANs for AI-Enhanced Creative Scaffolding in Art Design Education. In Proceedings of the 3rd International Conference on Educational Knowledge and Informatization (EKI ’25) (pp. 167–171). Association for Computing Machinery. DOI: https://doi.org/10.1145/3765325.3765355
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Copyright (c) 2025 Dr. Ritesh Kumar Singh, Abhinav Rathour, Dr. Shakti Prakash Jena, Sonia Pandey, Abhinav Mishra, Kalpana K
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