Journal 11: Analysis.

For my Analysis project, I want to investigate the rapid domain of AI‑generated art and its implications for authenticity and creativity, positioning it in dialogue with the longer history of traditional generative art and design. This subject is both timely and culturally relevant: algorithmic systems now co‑author some of the images that populate our social feeds, galleries, and advertising campaigns, reshaping the criteria by which visual work is judged. Understanding how these outputs are produced, circulated, and valued has become a new concern, one that sits at the intersection of my research interests in emerging technologies, visual culture, and human creativity.

Over the past five years, diffusion‑based platforms such as Midjourney, DALL‑E, and Stable Diffusion have made it possible for users with little formal artistic training to create high‑resolution images from short textual prompts. Generative art has existed since the 1960’s, where basic code and plotter tools could make drawings that no person could feasible make, and comparing differences between early code-created and generative art with new-era AI images could provide social and cultural insight into how we perceive AI images. While early plotter drawings have been celebrated as collaborations between artist and code, today’s AI‑assisted outputs arrive through pre‑trained neural networks whose inner logic is largely unknown to the public. This has brought up questions about the originality, intentionality, and the “soul” of the artwork.

Keeping this in mind, I have two questions coming into this project I seek to answer:

How do the visual conventions within AI‑generated imagery influence viewers’ perceptions of authenticity and creativity when compared with works produced through traditional generative art practices?

In what ways do AI‑generated artworks challenge or reinforce established artistic criteria, such as intentionality, originality, and expressive agency?

These questions matter because AI generated production questions the assumptions through which artistic value has been negotiated. Viewers' perceptions of images and artwork made from a text prompt may change the collective definition of what counts as authentic artistic practice. Is some AI art more well received than others? What is the deciding factor in this reception?

My motivation for pursuing this is rooted in my dual academic focus on Graphic Design and Human‑Computer Interaction, disciplines that involve both the aesthetics of form and the interaction of user experience. As a designer and researcher, I am interested in how generative AI extends, complicates, or reinforces the designer’s traditional role as intentional author. By comparing historical rule‑based generative systems with contemporary, data‑driven models, I can trace how ideas of authorship evolve when “intuition” and “choice” are partially delegated to machines. This comparative lens also allows reflection on practice: Which design decisions remain human, and which can or should be shared with an algorithmic partner?

Ultimately, the study would aim to contribute to ongoing conversations about the place of artificial intelligence in the arts. By probing how diverse audiences read authenticity into AI‑generated visuals, whether they treat them as legitimate artworks, clever novelties, or something in between, I hope to find insights relevant not only to academic debates but also to broader public discussions about the future of creative labor, intellectual property, and cultural value in a world forever changed by this new technological innovation.