Ohio University faculty members are examining the rise of “AI slop,” a term commonly used to describe low-quality, low-effort digital content produced at high volume with generative artificial intelligence. Examples can include misleading images, disposable videos, poorly written books, fake news, and other material created primarily to attract attention rather than provide meaningful information.
The term became increasingly prominent as generative AI tools made it easier for people to produce text, images and videos quickly. Merriam-Webster selected “slop” as its 2025 Word of the Year, defining it as low-quality digital content typically produced in large quantities through AI.
Ohio University experts Paul Shovlin, Chad Mourning and Jennifer Garrette Lisy said the defining characteristics of AI slop include minimal effort, rapid production, limited substance and an emphasis on volume over quality. The content is also frequently unwanted, filling social media feeds, search results and other digital platforms with material that provides little value.
Shovlin is an Assistant Professor of AI and Digital Rhetoric and Co-Director of Ohio University’s Human-First AI Initiative. He views AI as a tool whose effects depend heavily on the intentions, judgment and practices of the people using it.
He said stronger AI literacy can help people move beyond the idea that they must either fully embrace or completely reject the technology. Understanding AI’s capabilities, limitations, environmental effects and ethical implications can support more thoughtful decisions about when and how it should be used.
Mourning, an Assistant Professor of Computer Science, identifies ease of production as one of the most important characteristics of slop. He noted that low-effort content existed before generative AI, but current tools have dramatically reduced the time and expertise required to create and distribute it.
Lisy, an Assistant Professor of Elementary Education at Ohio University Zanesville and an AI Faculty Fellow, similarly defines AI slop as content that adds little to a broader discussion. She compared it with earlier forms of entertainment and media that were criticized for prioritizing attention and consumption over substance.
Generative AI and social platforms have also lowered traditional barriers to publishing. Content that once required the support of a production company, broadcaster or publishing house can now be created and distributed by an individual within minutes, contributing to a large supply of inexpensive and disposable media.
The faculty members distinguish AI slop from responsible use of generative AI by examining the creator’s objective and level of involvement. Responsible use is generally purposeful, task-oriented and centered on producing a specific outcome rather than generating material merely because the technology makes it possible.
Human oversight is another critical difference. Users should review AI-generated content for accuracy, reliability, relevance and suitability rather than publishing it without examination.
This process can include checking factual claims, revising language, verifying sources and considering whether the final product serves its intended audience.
Shovlin noted that experimentation can be an important part of learning how to use generative AI. People may initially produce trivial or low-value content while exploring a new system, but greater familiarity can help them identify more productive applications and understand the technology’s limitations.
Lisy said AI slop is often designed to generate clicks and circulate rapidly rather than educate audiences. By contrast, ethical use requires people to remain actively involved in creating, examining and validating the resulting material.
Mourning believes the strongest AI-enabled work will combine the technology with human creativity and expertise. In this model, AI functions more like a professional tool that supports a creator rather than replacing human judgment or independently determining the final output.
He also uses generative AI in scientific research involving atmospheric visibility prediction. When certain environmental conditions are underrepresented in a dataset, AI-generated samples can help researchers supplement the available information and improve model development.
Mourning said these less-visible uses may deliver significantly more value than the AI-generated novelty content that most people encounter online. He also reported becoming substantially more productive through generative AI programming tools.
Ohio University supports the principled use of generative AI through its Center for Teaching, Learning and Assessment. The center encourages applications that advance learning outcomes, improve faculty effectiveness, strengthen human connection and provide personalized assistance to students.
The university’s AI Faculty Fellows represent fields including science, education, English and the arts. They develop professional learning resources, course materials, AI-supported tools and guidance to help faculty and students use the technology appropriately.
Ohio University does not treat one AI policy as suitable for every academic discipline. A use considered productive in one course could qualify as plagiarism in another, making transparent expectations and field-specific guidance especially important.
The School of Electrical Engineering and Computer Science has also proposed an introductory AI literacy and ethics course that could be available to students across different majors. The initiative reflects the university’s position that students and educators must adapt as AI becomes more common in classrooms and workplaces.
The faculty members ultimately believe that the benefits of thoughtful use of generative AI can outweigh the problems posed by AI slop. Achieving those benefits, however, depends on literacy, clear policies, human oversight and a willingness to use the technology for defined and meaningful purposes.
