Generative Algorithms for Art and Architecture: A Collaborative Teaching Approach

Authors

Sam Keene, The Cooper UnionFollow
Benjamin Aranda, The Cooper UnionFollow

Keywords

generative algorithms, collaborative teaching

Abstract

We will present a course that we have been offering for the past few years that engages art, architecture and engineering students and challenges them to collaborate using generative methods to produce creative work. Our work contributes to the long-term understanding of AI in the arts and design in higher education because we have developed a successful course model focused on collaboration between creatives and technologists that can be replicated at other institutions. Feedback between creatives and technologists has been fundamental to opening new frontiers, giving students the tools to collaborate successfully is tremendously important. We will share example of in-class exercises, assignment prompts and examples of work. The course culminates in a final exhibition, open to the public. Some key themes emerge from the final works. First, generative algorithms are opening new avenues for creative[BA1] work. Second, creative applications of machine learning often reveal the flaws and bias present in these methods.

[BA1]multi-disciplinary creative

Recommended Citation

Keene, Sam and Aranda, Benjamin (2023) "Generative Algorithms for Art and Architecture: A Collaborative Teaching Approach," Tradition Innovations in Arts, Design, and Media Higher Education: Vol. 1: Iss. 1, Article 7.
DOI: https://doi.org/10.9741/2996-4873.1007
Available at: https://digitalscholarship.unlv.edu/tradition_innovations/vol1/iss1/7