Preface
This set of guidelines is intended for USG instructional faculty and staff when creating new open educational resources (OER) or revising existing OER using generative artificial intelligence (GenAI) tools. These guidelines assume that USG faculty and staff do not have access to the training datasets or validation datasets a generative AI tool uses in order to train its neural networks, and these guidelines are therefore focused on the use of the output of many generative AI tools.
These guidelines are not intended to give advice on how to use GenAI in the classroom. Please consult the following for guides on GenAI and pedagogy:
Dr. Jeanne Beatrix Law and Dr. Tamara Powell of Kennesaw State University, through USG's Coursera instance, have created an open course on Education and AI.
Administrative and pedagogical guides using GenAI are included in Chapter 4 of Generative Artificial Intelligence: Practical Uses in Education by Troy Heaps, CC BY 4.0 International License, Open Ed Manitoba.
Copyright / Trademark Fair Use in Generative AI
Recommended Resources
Search engines and generative AI models often use resources on the web under copyright to build the model, train the model, and validate the model, all of which may be considered a transformative use that fits within Fair Use within court cases. Questions about Fair Use within model building will be resolved within courts, and those findings will not affect the legal status of the prior use of these models. Regardless, when publishing OER which uses GenAI to create any part of the new resources, be sure to check on some general information on Fair Use. ALG recommends the following resources:
Code of Best Practices in Fair Use for Open Educational Resources for the intersection of copyright Fair Use and OER.
INTA Fair Use of Trademarks (Intended for a Non-Legal Audience) fact sheet for how Fair Use works in trademark law.
Note: US Copyright Office and AI-Generated Materials
The United States Copyright Office (USCO) has launched a copyright initiative on AI-generated materials. At the moment, USCO will not register any works under copyright that are entirely generated by machines due to US copyright law's requirement of human authorship (see Copyright Registration Guidance for Works Containing AI-Generated Materials). Works including GenAI-generated images, such as a human-authored book containing GenAI-generated images, can still be registered, but the images are not protected under copyright.
This will likely undergo multiple clarifications, changes, and new legislation in the coming years - ALG will try to keep these guidelines up-to-date with US copyright law surrounding AI-generated content.
Checking Prompts and Outputs
Using generative AI tools will normally fall under a different legal conversation than the aforementioned questions on building models. There are still unanswered questions on how AI tools are distinguishable from other tools which aid in the creation or the copying of content. Regardless of the law, educators have an ethical obligation to use AI tools in good faith within Fair Use, allowing for the transformation, education, and critique without knowingly replicating single copyrighted or trademarked works.
Assess using your due diligence whether or not the output of the GenAI tool explicitly infringes on copyright focusing on your prompts:
- Example: Prompting a GenAI tool to create a “painting of Sonic the Hedgehog” and then using the output as a decorative image without educational value may explicitly infringe on trademark law with the publisher of Sonic the Hedgehog games, Sega.
- Example: Prompting a GenAI tool to create an “abstract book cover featuring a beach and a whale” may or may not result in GenAI output that infringes on an artist's copyright. After doing a due-diligence search for identical works and finding none, this work is likely to be able to be included in an open resource and open-licensed as such.
- Example: Prompting a GenAI tool to write “15 short-answer questions about hydrocarbons” may or may not result in GenAI output that infringes on an author's copyright. After doing a due-diligence search for identical unique phrases and finding none, this work is likely to be able to be included in an open resource and open-licensed as such.
There are still open questions about who should be legally liable for infringing works created using AI tools. Regardless of the legal status of this liability, doing due-diligence searching for infringement and not prompting directly towards infringement are important for OER authors using AI tools. Note that there is still no copyright protection in the United States for styles or ideas; keep a lookout for infringement on individual works primarily for legal reasons.
Due-Diligence Searching for AI Copyright Infringement
To check whether or not an AI output has duplicated a particular work under copyright:
For text, search the web. Ensure that this exact language does not exist within copyrighted web content.
For images, do a reverse image search. Ensure that the image generated by the GenAI tool does not duplicate copyrighted web images. (Tools such as TinEye and Google Images can conduct a reverse image search.)
Mixing OER and AI
Ethical Use of Existing OER within Generative AI Prompts
It is possible to prompt a large language model (LLM) such as GPT to create new works using existing OER. If you are doing so, keeping the open license of the existing OER and attributing the original works would keep the openness of the original work alive and provide a history of where each resource came from. While there are outstanding questions on liability when directly replicating single works using AI tools, these questions shift to ethics when open licenses have already given permission for revising and remixing. It is an ethical practice to acknowledge and properly use the original resource's open license on the output of your GenAI prompt.
Example: “Using https://openstax.org/books/introduction-python-programming/pages/2-2-type-conversion (OpenStax Introduction to Python Programming's section on Type Conversion), create a one-paragraph summary of type conversion.” This open textbook is under a CC BY 4.0 International License, so any use of the output should include an attribution of the original work.
If you are using an OER with an SA (ShareAlike) designation in the license to create a new work using Gen AI, the output should both attribute the original work and be shared under the same open license as the original work.
When using more than one OER to create a new work, use the same guidelines as remixing multiple OER by complying with and applying the most restrictive open license:
Example: “Using https://openstax.org/books/algebra-and-trigonometry-2e/pages/1-4-polynomials (OpenStax Algebra and Trigonometry's section on Polynomials, CC BY) and https://sl2x.aimath.org/book/aafmt/chapter9.html (Elementary Abstract Algebra's chapter on Polynomials, CC BY-NC-SA), create a one-paragraph summary of polynomials.” The resulting derivative work using the output should be shared under a CC BY-NC-SA (Attribution-NonCommercial-ShareAlike) license.
When creating assessments using a SA restriction, it's possible that you would not want to share these assessments with the public. Be sure to credit the original source, assign the same license to the new materials, and provide a way for other faculty to contact you and receive this newly-generated content.
Combining AI-Generated Content and Creative Commons-Licensed Content
When creating OER with Generative AI prompts, use the same labeling that you would if you combined Public Domain materials with your Creative Commons work. Mark them as Public Domain per usual. If you would like to be more transparent in the interest of ethics and comprehensiveness, provide explanatory text in a place like the preface to a textbook or an attributions slide.
For more on remixing CC-licensed works with other materials: CC Certification Course: 4.4 Remixing CC-Licensed Work, CC BY 4.0 International License.
Update History
08/14/2024: Updated based on a webinar on AI in Open Education as hosted by Creative Commons. Special thanks to Meredith Jacob and Will Cross for their authorship and presentation of this webinar.
Except where otherwise noted, content on this site is licensed under a Creative Commons Attribution 4.0 International license.