Abstract:

Here at Ole Miss we pride ourselves on how our university serves our students and community at large. This paper exists to open a dialogue between students and administration at the University of Mississippi regarding the use of AI generated content in promotional works done by Olemiss. 

The University of Mississippi has many talented artists studying and working on staff here. It is our belief that commissioning work from students is a mutually beneficial endeavor, building up both the student and the university at large. This paper also seeks to deconstruct dangerous narratives supporting AI imaging.

What is AI:

AI stands for Artificial intelligence, which is a term that can often be misleading. As of 2025 the “intelligence” as we understand it, consists of processes and algorithms. The AI language models and image generators do not understand the data that they’re processing, are unaware of themselves as an AI, and do not make decisions. Intelligence is a human attribute. Though impressive, algorithmic programming and data “training” are not signs of intelligence. A more straightforward term would be machine learning.

How AI Works:

Large Language Models (LLM) and Image Generators require data sets in order to function, and cannot originate content. Companies call this “training”. If AI cannot create spontaneously, it must receive its data set from human creations. Books, media, the internet. In the case of image generators, the entire internet is “scraped” so that it is using as many images as possible in its dataset. Without human data, none of these tools would exist.

Student Engagement:

Using student-created artwork in university promotional materials—rather than relying on machine learning models to generate visuals—is not only an ethical choice, but also a strategic and community-oriented one. Student artwork adds authenticity, fosters institutional identity, and strengthens the university’s core mission of empowering human creativity.

First, student artwork represents the lived experiences, perspectives, and identities of the campus community. Unlike machine-generated art—which often lacks context, intentionality, or emotional depth—student art carries meaning rooted in personal, cultural, and academic exploration. The National Survey of Student Engagement (NSSE) has found that students who actively participate in campus activities are significantly more likely to persist and succeed academically. Research often shows that engaged students have retention rates that are approximately 12–18% higher than those who are less involved. Incorporating this work into promotional materials signals that the university values the creative voices of its students, not just as learners but as active contributors to campus culture.

Second, using student art reinforces a learning-centered model. Universities are not corporations—they are educational institutions committed to nurturing talent. Showcasing student work in public-facing materials provides students with real-world exposure, builds professional portfolios, and validates their creative labor. It’s a powerful form of experiential learning that affirms the university’s investment in student growth beyond the classroom.

Third, prioritizing student art over machine-generated alternatives is a statement of ethical and cultural leadership. AI-generated images are often trained on artists’ work without consent, raising concerns about appropriation, copyright, and the devaluation of human creativity. By elevating student creators, universities resist the trend of replacing artistic labor with algorithmic shortcuts, and instead stand for fair recognition, originality, and human-centered design.

Finally, student artwork helps the university stand out. While AI tools produce polished but generic content, student-created pieces are unique, site-specific, and rich in personality. They reflect the diversity, energy, and spirit of the actual campus—qualities that prospective students and stakeholders are far more likely to connect with. According to the Journal of Consumer Research, research found that authenticity leads to higher engagement and trust. 

In short, using student artwork is not merely an aesthetic choice—it’s a practice that aligns with educational values, promotes ethical creativity, and builds stronger connections between students and the institution they represent.

Democratizing Art:

One step in understanding AI art from an objective perspective is by deconstructing the language presented by AI companies. The Laion 5B dataset that provides the data for the image generator Stable Diffusion is put together by a group funded by VC capital. Their website says

 “We believe that machine learning research and its applications have the potential to have huge positive impacts on our world and therefore should be democratized.“ 

The call to democratize machine learning research rests on the belief that access alone equates to empowerment and innovation. A similar logic is often applied to art: that if the tools are available, the creative process becomes inherently accessible and egalitarian. However, this assumption demands critical interrogation—particularly when we consider the questions: Are artists gatekeeping the skills to art? Is art truly accessible to anyone? And what are the implications of democratized art?

First, while artists may not always explicitly gatekeep in the traditional sense, there are systemic barriers—such as access to education, materials, mentorship, and time—that limit who can meaningfully engage in art-making. The same holds true for machine learning: while open-source platforms and online courses have expanded technical access, meaningful participation remains shaped by deeper inequities in education, infrastructure, and social capital. Just as mastering artistic technique takes time, guidance, and critical discourse, so does responsible and effective ML research.

Second, art may appear more accessible in the digital age, but access alone does not guarantee equity or voice. Cultural institutions, economic hierarchies, and digital algorithms all influence which art is seen, valued, and legitimized. In the context of machine learning, democratization often empowers those who already hold technological fluency and institutional support, while marginal voices remain excluded or even exploited—as in the case of datasets trained on artists’ work without consent.

Finally, the implications of democratized art—and by extension, democratized machine learning—are deeply complex. On one hand, lowering barriers to participation can foster innovation, diversity, and collective expression. On the other, it can also lead to appropriation, the erosion of craft, and the commodification of creativity. In the ML space, this can translate into rushed, unregulated applications that prioritize speed and novelty over ethics, rigor, or accountability.

Therefore, the question is not whether democratization is inherently good or bad, but rather: Democratized for whom, and under what conditions? Just as responsible art practice involves dialogue, context, and reflection, so too must machine learning be situated within frameworks of ethics, equity, and critical engagement. Without these, the rhetoric of democratization risks masking the very forms of exclusion and harm it claims to resist.

Conclusion:

In conclusion, this analysis underscores that the integration of student-created artwork in university promotional materials is not merely a stylistic preference but a strategic commitment to authenticity, ethical practice, and community empowerment. By showcasing the genuine expressions of students, the university not only reinforces its educational mission and fosters a unique institutional identity but also challenges the reductionist narratives of democratized art that often overlook the inherent barriers and ethical dilemmas posed by AI-generated content. Ultimately, true democratization—whether in art or machine learning—must be critically examined and implemented within frameworks that prioritize equity, cultural sensitivity, and accountability over mere accessibility.

Readings:

Michael B. Beverland, Francis J. Farrelly (February 2010) The Quest for Authenticity in Consumption: Consumers’ Purposive Choice of Authentic Cues to Shape Experienced Outcomes, Journal of Consumer Research, Volume 36, Issue 5, Pages 838–856, https://doi.org/10.1086/615047

Kuh, G. D. (2008). High-Impact Educational Practices: What They Are, Who Has Access to Them, and Why They Matter. Report from the Association of American Colleges and Universities.

Tinto, V. (1993). Leaving College: Rethinking the Causes and Cures of Student Attrition (2nd ed.). Chicago, IL: University of Chicago Press. 
https://doi.org/10.7208/chicago/9780226922461.001.0001