Generative Artificial Intelligence

Here are some ways that generative AI could be used when creating or adapting OER:

• Create question sets, case studies, and other instructional resources.
• Analyze and create summaries of text.
• Automate the creation of an audio text version, usually for accessibility purposes.
• Translate text to another language.
• Analyze a photo to create alt text for accessibility purposes.
• Create illustrations and photo-realistic images for both decorative and instructional purposes.
• Create instructional videos.
• Generate scripts that can be used for videos and podcasts.
• Check spelling and grammar in your written text.

You can use AI to help generate content for an OER, but it is important to be transparent and provide sourcing where possible. See the recommendations below.

With the proliferation of generative AI tools and the continual integration of these tools into other software packages, you may not know that a tool you are using to create learning materials is using generative AI. Therefore, these guidelines are intended to be interpreted with leniency and flexibility to allow for the possibility that generative AI use may not always be visible or apparent to the person who is using a tool.

While these tools can be of great value, there are numerous ethical concerns and potential risks you should be aware of when you consider using generative AI tools to develop OER.

1. Lack of transparency. The source of the training data and the programming logic used by many generative AI tools is not always made available to the public. Indeed, even the companies that develop the tools may not be able to explain exactly how they work or how they arrived at the outputs they did.
2. Bias. Because there is a lack of transparency in how the tools are constructed and what data is used to train them, this can lead to biases being present in the output.
3. Accuracy. Generative AI systems can sometimes produce inaccurate or made-up answers (also referred to as hallucinations).
4. Intellectual property (IP) and copyright. There are three areas where copyright and IP should be considered: the content that the AI tool has been trained with, the content that the AI tool generates, and using AI to generate summaries of copyrighted content.
   1. Use of content to train AI models. Many generative AI tools have been trained on copyrighted works, often without the permission of copyright holders. While organizations such as Creative Commons argue that this use is considered fair use under current copyright legislation, there are a number of lawsuits where creators are arguing that AI tools are creating unauthorized derivatives. This also means that AI-generated content could be subject to copyright claims.
   2. Applying copyright to generated output. Legal decisions and rulings around copyrighting AI-generated content have been very clear in the United States. AI-generated content is not human-made and, therefore, cannot be protected by copyright. In Canada, there have not been definitive legal rulings around this, although the emerging consensus in Canada is that Canadian copyright law will follow closely with the US when it comes to copyright and AI due to the shared international copyright and trade agreements the two countries have with each other.
   3. Using AI to generate summaries of copyright work. It is unlikely that using generative AI to summarize copyrighted content is a violation of copyright as the AI generated summary is machine and not human-generated.
5. Sustainability. Generative AI uses massive amounts of electricity to operate, which has led to examinations as to how environmentally sustainable generative AI is.