One of the biggest barriers to creating and customizing OER is time. With generative AI tools becoming more accessible, it may be worthwhile to consider which possible uses might be appropriate for your project (and which might not!).

What is AI and how does it relate to OER work?

According to the VCU AI Guidebook, “Artificial Intelligence (AI) is a broad field of computer science aimed at creating machines capable of intelligent behavior.” AI is already working behind the scenes in many tools, services and platforms we use daily.

Within this broad field lies generative AI, or GenAI, which is “a subset of AI that focuses on creating novel content, like text, images, code, music, video, etc. It uses both Machine Learning and Deep Learning to understand what it needs to do and then it generates a new, complex output.” GenAI tools respond to user requests, or “prompts” (VCU AI Guidebook). Because these tools can generate large amounts of content very quickly, they have the potential to change OER workflows significantly. Some potential applications include:

• Using GenAI tools to help you to plan your project by generating an outline or first draft
• Using an existing OER as a prompt to generate companion ancillaries such as quiz questions, slide decks or podcasts
• Using AI tools to create alt text for images
• Using tools like Grammarly or ChatGPT can provide basic proofreading, which can be very helpful to OER authors who don’t have the budget for a professional copyeditor
• Openly licensing and sharing the specific prompts they used to generate OER content and/or ancillaries, which is an emerging area of content sharing

However, GenAI tools make mistakes, and the content it returns needs to be reviewed and refined. We’ll get more into the challenges of using AI below.

Prompt Engineering

So, how do you actually go about using these GenAI tools? “Prompt engineering refers to the process of designing, crafting, and refining contextually appropriate inputs or questions in order to elicit specific types of responses or behaviors from an AI language model The goal of prompt engineering is to optimize how the model responds based on the structure, content, and tone of the question, thereby facilitating more accurate, useful, or engaging interactions. In the context of AI language models, prompt engineering involves formulating effective prompts that yield desired responses, ensuring clarity, relevance, and specificity.” (Adapted from page ii, Generative AI and Prompt Engineering: The Art of Whispering to Let the Genie Out of the Algorithmic World by Aras Bozkurt and Ramesh C Sharma, licensed under CC BY.)

Effective prompts are specific and clear and may involve including specific parameters for the response, proposing a scenario to the GenAI tool, or asking the tool to imagine itself in a certain role to elicit the desired response (ex. “You are an undergraduate psychology professor working on an OER…”). Prompt engineering can be an iterative process, whereby users tailor increasingly specific prompts to achieve the desired response.

What are some AI tools that might be useful in OER creation?

Chatgpt4.0

Perhaps the most well-known GenAI tool, ChatGPT was created by OpenAI. ChatGPT is a powerful chatbot, known for high-quality text outputs. ChatGPT can answer questions, generate creative content, translate languages, and with additional plugins, can utilize external services, and perform computations.

Grammarly

This tool can be found in the extension section of Google Chrome or Google Docs! Grammarly is a helpful AI assistant, particularly known for correcting grammatical errors, sense of tone, and phrasing in your writing. Grammarly can help you sound more professional when emailing co-workers and the tool can even help you write creatively as well.

NotebookLM

NotebookLM is an AI powered research and analyzing tool designed to help users organize their notes efficiently. NotebookLM makes team projects less time consuming by giving summaries of complex documents and adding an analytical perspective to your notes.

Google Gemini

Google Gemini is a Google-User exclusive AI tool that allows users to find quick and accessible answers to your questions with linked sources and data. This tool can also help you with brainstorming ideas for assignments, learning material in your courses and writing as well.

What are some challenges associated with using AI in OER Creation?

Bias

AI systems are trained to collect datasets in which these data sets may contain inherent biases. This biased information could potentially lead to misrepresentation of information or certain groups, amplify harmful narratives, and introduce bias to its users.

Hallucinations

When speaking of “hallucinations” created by AI, we mean fabricated information provided to you by AI which is often made up of incorrect facts, misleading information, or duplicated images or text. This could be a big risk when using AI for educational purposes so it is highly advised to double check before using or publishing anything assisted by AI.

Privacy

The challenge of privacy, or lack thereof, primarily comes from the way that AI systems work. AI systems often require large amounts of data for training which could potentially include sensitive personal information. This raises concerns about the possibility of data collection, usage, or leakage.

Copyright issues

The most common issue with copyrighting while using an AI tool is that any content that is solely created by AI is not copyrightable because only the human created components are copyrightable. Along with this rule, all applicants have a duty to disclose the inclusion of AI-generated content in a work submitted for registration and to provide a brief explanation of the human author’s contributions to the work. This is a requirement to provide an honest and fair playing field for all applicants and contributors.

In order to ensure that you’re not inadvertently using copyrighted material generated by an AI tool, authors should search the web for any language they plan to use verbatim to ensure that this exact language does not exist within copyrighted web content.

Attribution and Disclosure

Although AI-generated content is not copyrightable, you should still attribute any content used that came from a generative AI tool. BCcampus suggests including these components for disclosing genAI usage in OER creation:

• what content was generated
• what tools were used to generate the content, including links to the tool,
• how you used that tool (ie what prompts was the tool given that generated the content)
• the date the content was generated
• what steps were taken to review the content to ensure it was valid and correct.

Further Reading on AI and OER Creation

VCU-Specific Resources

• VCU AI Guidebook
  • This source is a guide to the uses of AI and the learning of AI at VCU. It features inside perspectives from both the students and staff of VCU.
• Generative AI Guidelines
  • This resource is a guide to using Generative AI in OER including things to look out for, reminders of policies and guideline rules, and what to avoid.
• CTLE AI Tool
  • This resource provides information about CTLE (Center for Teaching and Learning Excellence) and what they do to encourage teaching of equity and inclusive practices. There is a section here that talks about Teaching and Learning in the Era of Artificial Intelligence.
• VCU Libraries Guide to Gen AI Tools
  • This resource provides information on Generative Artificial Intelligence in education institutions like VCU. It expands on the tools of AI, the terms, and definitions used to understand AI as well.

Examples of Specific Use Cases in OER Projects

• Updating an OER Textbook via AI and ChatGPT – This article documents the process of updating an OER textbook with the assistance of an AI platform called ChatGPT. Adam Croom explains the advantages and disadvantages of using this platform to update an existing textbook in his Introduction to Advertising class.

• Exploring Artificial Intelligence in Open Education Contexts (not so scary after all?) – The archived webinar from Open Oregon focused on topics related to AI and OER, including an overview of a project by OpenRN which used AI to create nursing instructional materials. (Section begins at 2:47).

Other Resources

• How Generative AI Affects Open Educational Resources
  • In this resource, David Wiley explains how Generative AI Affects Open Educational Resources by going into depth about what Traditional OER is, how to author it, revise and remix it, what Generative OER is and the open prompts that inherently coexist with it.
• Understanding CC Licenses and Generative AI
  • From Creative Commons, this resource provides information about the way that CC Licensing works when it comes to Generative AI. The article exemplifies commonly asked questions about the topic and answers them thoroughly.
• Guidelines for Using Generative AI Tools in Open Educational Resources
  • This resource from Affordable Learning Georgia is a guide for Using Generative AI Tools in Open Educational Resources, explaining in depth how Copyright/Trademark fair use works and mixing OER with AI.

Sources

BCcampus OER Production Team. (2021). Getting started: OER publishing at BCcampus. BCcampus. https://opentextbc.ca/gettingstarted

Bozkurt, A., & Sharma, R. C. (2023). Generative AI and Prompt Engineering: The Art of Whispering to Let the Genie Out of the Algorithmic World. Asian Journal of Distance Education, 18(2), i–vii. https://doi.org/10.5281/zenodo.8174941 licensed under CC BY.

Virginia Commonwealth University Academic Affairs. (2024). A Guide to AI at VCU. https://aiguidebook.vcu.edu/