Total Quality Management with the Integration of Artificial Intelligence in Art and Design Pedagogy: An Innovative Era in Creative Fields

Authors

  • Harini Methuku Scientific College of Design

DOI:

https://doi.org/10.18533/rf0fcm16

Keywords:

Art and Design, Artificial Intelligence, pedagogy, Creativity

Abstract

Total Quality Management (TQM) is an approach that considers customers as the centric point and employee empowerment to emphasize continuous improvement in achieving organizational overall quality/excellence. TQM principles and approaches in higher education are applicable to enhance teaching-learning, research, and other administrative services. In this paper, emphasis is given to the TQM theoretical framework, practices, key principles, and application of other aspects. The paper also explores challenges, benefits, and recommendations for implementation in the higher education sector. In addition, with the fast growth of Artificial Intelligence (AI), the higher education sector's approach to teaching-learning is rapidly changing, including art and design fields. This paper focused on integrating varied AI tools/applications in art and design curricula by indicating their benefits and further stating challenges. The paper researches pedagogical strategies, the use of AI applications, and ethical considerations that guide art and design fields for higher educators in the ethical and efficient use of AI for enhanced teaching-learning experiences. 

References

Adobe. (2023). Adobe Sensei. Retrieved from https://business.adobe.com/products/sensei/adobe-sensei.html

Autodesk. (2023). Autodesk Dreamcatcher. Retrieved

from https://www.research.autodesk.com/projects/project-dreamcatcher/

Billinghurst, M., Kato, H., & Poupyrev, I. (2002). The magic book: A spatially immersive game. Proceedings of the 2002 IEEE International Symposium on Mixed and Augmented Reality, 22–29.

Brown, T. B., Mane, D., Chakraborty, S., Salazar, A., & Karamcheti, V. (2021). "DALL-E 2: Efficient Text-to-Image Diffusion Models," arXiv preprint arXiv:2112.10495

Brusilovsky, P. (2001). Adaptive hypermedia user models: Challenges and perspectives. User Modeling and User-Adapted Interaction, 11(1-2), 87-110.

Cope, B., & Kalantzis, M. (2009). New literacies: Students and the challenges of learning in the digital age. Routledge.

Cuban, L. (2001). Oversold and underused: Computers in the classroom. Harvard University Press.

Dahlgaard-Park, S. M., Park, J. H., & Chen, I. J. (2018). Total quality management and sustainability: A review of the literature. Total Quality Management & Business Excellence, 29(1-2), 3-28.

Elgammal, A., Liu, B., Elhoseiny, M., & Lee, M. (2017). Can machines create art? IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 677–690.

Gunkel, D. J. (2018). The machine question: Critical perspectives on AI, robots, and ethics. MIT Press.

Huang, J., Li, H., & Li, M. (2019). Artificial intelligence in education: A review. IEEE Access, 7, 55423-55436.

Kaname, O. (2003). Handbook for TQM and QCC Vol 1.

Kumar, U., & Shanmuganathan, J. (2019). A structural relationship between TQM practices and organizational performance in selected auto component manufacturing companies. International Journal of Management, 10(5).

Jordan, M. I. (2018). Artificial intelligence—The revolution has not happened yet. Harvard Business Review, 96(10), 56-61.

Siemens, G., & Baker, R. S. J. (2012). Learning analytics and educational measurement. Cambridge University Press.

Warschauer, M. (2003). Technology and social inclusion: Rethinking the digital divide*. MIT Press.

Yang, B., Yang, L., & Zhu, Q. (2019). The impact of total quality management practices on innovation performance: The mediating role of organizational learning. Journal of Business Research, 94, 150-160.

Published

2025-03-26

Similar Articles

11-20 of 354

You may also start an advanced similarity search for this article.