The AI Syllabus

Critical AI Literacies: 

• Examine how generative AI tools are training on data that is copyrighted and/or obtained without the original creator's consent.
• Analyze the purpose of appropriately citing and/or attributing AI usage in your own creative works. 

On this page you will find multimedia sources that explore the breadth of how generative AI is impacting attribution and citation practices including AI and copyright, AI and "cheating", and AI and ownership of creative works. This page also includes key concepts, activities, and assignments to build understanding of and critically engage with AI and citation and attribution. 

Citation, Attribution, and Copyright

Sources: 

 

Analla, T. (2023, March 6). Zarya of the dawn: How AI is changing the landscape of copyright protection. JOLT (Harvard Journal of Law and Technology).

• This article explores how the US Copyright Office is ruling on creative works made with generative AI including the graphic novel, Zarya of the Dawn, in which the author used MidJourney to create the images in the novel. The author was granted partial copyright on the text and design but not the images and as such, this AI output is in the public domain. This case is instructive for how copyrightable generative AI output is/will be. #Practical #Philosophical

 

Engelbrecht, K. (2025, March 4). The great AI art heist. Chicago magazine. 

• This long form article profiles the creator of Glaze, a program that artists can use to their work that applies pixel-level changes making it harder for AI to train on the images. The computer science professor who invented the program sees it as a tool to prevent the exploitation of artists whose publicly available work is scraped and used as training data without their consent. #Practical #Philosophical

 

Furze, L. (2023, September 20). Generative AI, plagiarism, and “cheating”. Leon Furze. 

• This post is intended for educators and provides an explanation of if and how generative AI output serves as plagiarism and provides suggestions on how to approach Gen AI use in the classroom including rethinking design and evaluation of learning assessments. #Philosophical #Practical

 

Klein, E (Host). (2024, April 5). Will AI break the internet? Or save it? [Audio podcast episode]. In The Ezra Klein Show. 

• In this 90 minute discussion with Nilay Patel, co-founder and editor-in-chief of tech publication, The Verge, they discuss the influence of generative AI on how people will engage with the open web, social media, and acts of creation. Beginning at 35:36, they discuss copyright as it relates to AI including the idea that “copyright law is a coin flip” and there’s no real way to predict how copyright will impact AI use and regulation. #Practical #Philosophical

 

Marcus, G. and Southen, R. (2024, Jan. 6). Generative AI has a visual plagiarism problem. IEEE Spectrum. 

• In this investigative article, the authors conclude that AI image generators, Midjourney and DALL-E, were likely trained on copyrighted data given how closely their outputs resemble existing images in film and television. They also note that users of the AI tools are likely not made aware that they may be in violation of copyright if they do anything with the generated images. This article explores the issue of Fair Use as well as ethical considerations of how open access material is used as training data. #Practical #Philosophical

 

McAdoo, T. (2024, February 23). How to cite ChatGPT. APA Style. 

• This blog post from the APA website provides several examples of the various ways people might use ChatGPT in their research and writing and how to appropriately the AI tool in their work. #Practical

 

Moreno, J.E. (2023, Dec. 30). Boom in AI prompts a test of copyright law. The New York Times.

• This news article discusses the lawsuit that the New York Times has brought against OpenAI (the company behind ChatGPT) under the basis that because GPT-3 has trained on, among other news sources, New York Times content, ChatGPT can “produce content nearly identical to [NYT].” More broadly, considers the ongoing question of whether AI generated content is protected under fair use or serves as copyright violation. #Practical

 

Shively, L. and Moss, E. (2024, April 15). Who owns this word salad? AI inputs, outputs, and the evolving attribution landscape [Professional development workshop]. Diablo Valley College. 

• In this presentation, DVC librarians explore key concepts related to copyright and AI as well as how this discussion is instructive to educators thinking about AI and academic integrity. #Practical #Philosophical

 

The Wall Street Journal. (2024, April 15). Copyright lawyer explains Drake AI song and more [Video]. Youtube. 

• In this 8 minute video, an intellectual property lawyer provides a clear and accessible breakdown of how copyright is impacting AI using three notable cases related to AI output as copyrightable, AI training data as violating copyright, and AI output as copyright infringement while noting that these are fluid issues determined on a case-by-case basis. #Practical

 

KNOWING: 

Generative AI tools are trained on vast datasets, often including copyrighted material or content used without creator consent. This raises ethical and legal questions about attribution, ownership, and the responsibility of citing AI-generated contributions in creative and academic work. While Fair Use provides some flexibility in repurposing content, the role of AI in transforming original works remains legally and ethically complex.

To critically engage with AI in attribution and citation, it’s important to: 

• Identify how generative AI uses copyrighted and uncredited data raising ethical concerns about consent and ownership. 
• Understand the purpose of citing AI-generated content and recognize its role in maintaining academic and creative integrity. 
• Analyze how AI challenges traditional notions of authorship and attribution in navigating the often unpredictable nature of copyright law. 

DOING:

• Follow the copyright. Research and make a list of recent AI copyright issues that impact film and television. Document the case on a chart, the findings if available, and any information that details the case (for example, in the news). In research groups, reflect on impact, what the legal issue tells us about copyright, and whether or not you think it is a copyright violation. Then, use an AI to compare outcomes and findings. Try switching up how the AI responds (for example, as a librarian, as a teacher, as a radical lawyer, as an ACLU Lawyers, as a digital activist, etc.)
• Find the hallucination. Provide an AI model (ChatGPT; Gemini; CoPilot; Perplexity, etc.) with prompts to generate citations about specific academic topics. Investigate patterns—when does it generate realistic but fake article titles, authors, or journal names? Does it? Similarly, how do the AI cited journal articles compare to what you would find or cite from your own research? 
• Create a classwide practice of citing AI use in research and/or content creation (writing, art, music, etc.)
• Research AI and copyright complexity. What would a 1 page guide look like to AI and copyright knowledge? Review case studies, research, news/press, and AI to synthesize the information. Use graphic tools to provide clarity and legibility to the 1 page guide.
• “How will AI and human collaborations, specifically around creative works,  impact our understanding of “authorship”? Adapted from USC’s “AI, Copyright, and the Law”. Who is saying what? What do we need to know?