The ARTificial revolution. Introducing generative artificial intelligence tools into artistic education

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Palabras clave:

artificial intelligence, content generator, creativity, art education, critical thinking, technology

Resumen

This article presents an experience of integrating generative artificial intelligence (AI) technologies in artistic education in the Primary School Teaching degree course. The aim of combining AI algorithms with traditional image editing techniques is not only to teach instrumental skills but also to foster a critical understanding of the limitations and risks associated with these technologies, promoting responsible and ethical use. The ability of AI to rapidly generate original content from text, images, video, and code, presents a complex landscape of opportunities and challenges. AI is redefining human cognitive processes and creativity, making it crucial to emphasise the importance of maintaining human intelligence as an irreplaceable complement to technology. The implementation of generative AI in art education not only enriches visual and aesthetic learning but also prepares students to contribute critically and creatively to the intersection of art and technology, equipping them with essential skills for innovation in their artistic practices.

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Citas

Benjamin, W. (2018) La obra de arte en la era de su reproductibilidad técnica. Taurus.

Bhandari, B., Park, G., & Shafiabady, N. (2023). Implementation of transformer-based deep learning architecture for the development of surface roughness classifier using sound and cutting force signals. Neural Computing and Applications, 1-18.

Buschow, C., & Suhr, M. (2022). Change management and new organizational forms of content creation. In Media and Change Management: Creating a Path for New Content Formats, Business Models, Consumer Roles, and Business Responsibility (pp. 381-397). Springer International Publishing.

Campbell, C., Plangger, K., Sands, S., & Kietzmann, J. (2022). Preparing for an era of deepfakes and AI-generated ads: A framework for understanding responses to manipulated advertising. Journal of Advertising, 51(1), 22-38.

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 conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642.

Gangadharbatla, H. (2022). The role of AI attribution knowledge in the evaluation of artwork. Empirical Studies of the Arts, 40(2), 125-142.

Habbal, A., Ali, M. K., & Abuzaraida, M. A. (2024). Artificial Intelligence Trust, risk and security management (AI trism): Frameworks, applications, challenges and future research directions. Expert Systems with Applications, 240, 122442.

Köbis, N. C., Doležalová, B., y Soraperra, I. (2021). Fooled twice: People cannot detect deepfakes but think they can. Iscience, 24(11).

Montasari, R. (2024). The Dual Role of Artificial Intelligence in Online Disinformation: A Critical Analysis. In Cyberspace, Cyberterrorism and the International Security in the Fourth Industrial Revolution: Threats, Assessment and Responses (pp. 229-240). Springer International Publishing.

Mulé, S., Lawrance, L., Belkouchi, Y., Vilgrain, V., Lewin, M., Trillaud, H., ... & Lassau, N. (2023). Generative adversarial networks (GAN)-based data augmentation of rare liver cancers: The SFR 2021 Artificial Intelligence Data Challenge. Diagnostic and Interventional Imaging, 104(1), 43-48.

Navarro, E. & Torres, A. (2023). Jo també hi era | Fake Mundet. In Bosch E. (ed.) No és un llibre de receptres (pp. 105-119). Rosa Sensat.

Nguyen, T. T., Nguyen, Q. V. H., Nguyen, D. T., Nguyen, D. T., Huynh-The, T., Nahavandi, S. & Nguyen, C. M. (2022). Deep learning for deepfakes creation and detection: A survey. Computer Vision and Image Understanding, 223, 103525.

Nguyen, T.H. (2021). 5 impactful technologies from the Gartner emerging technologies and trends impact radar for 2022. Gartner. https://www.gartner.com/en/articles/5-impactful-technologies-from-the-gartner-emerging-technologies-and-trends-impact-radar-for-2022

Torres-Carceller, A. (2022). Mentiras reveladoras: el fake como práctica artística contra la defactualización. VISUAL REVIEW. International Visual Culture Review/Revista Internacional de Cultura Visual, 9(Monográfico), 1-13. https://doi.org/10.37467/revvisual.v9.3560

Vartiainen, H., & Tedre, M. (2024). How Text-to-Image Generative AI is Transforming Mediated Action. IEEE Computer Graphics and Applications.

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Publicado

2024-11-01

Cómo citar

Torres-Carceller, A. (2024). The ARTificial revolution. Introducing generative artificial intelligence tools into artistic education. Revista Catalana De Pedagogia, 26, 64–81. Recuperado a partir de https://revistes.iec.cat/index.php/RCP/article/view/153243