Generative AI presents both opportunities and challenges for creating and adapting open educational resources (OER). Gen AI tools can help generate and refine content efficiently, but the outputs require careful review for accuracy, bias, and alignment with educational goals.
For this activity, you will create a single-page section for an open textbook in your field.
- Choose a Generative AI model such as ChatGPT-4, Claude, or LLaMA.
- Select a topic in your area of expertise where you can assess the quality of the content.
- Use a structured prompt to generate a detailed textbook section. Example: “As a psychology professor, write a 1000-word, highly detailed textbook section for first-year college students on the science and theory of happiness. Include learning objectives at the beginning and a glossary of key terms at the end.”
- Review the AI-generated content for accuracy, clarity, bias, and completeness.
Complete this Activity
Reflect on your diagram’s usefulness as an OER and share your insights. What needs to be considered—ethically and practically—when using GenAI to create OER?
Examples
The sample prompt was used to create this textbook section: The Science and Theory of Happiness
Generative AI (GenAI) is rapidly transforming how we conduct research, scholarship, and teaching and learning. Permitted use of GenAI in these areas is constantly shifting and dependent on the organizations or instituions for which you are engaging. Before sharing any outputs of your GenAI use, it is important to consult policies and guidelines on accepted use. To learn more, review the resources on GenerativeAI and Open Scholarship.
Image Credit: 2008-01-26 (Editing a paper) - 31 by Nic McPhee (CC BY SA)
This was my prompt: As a theatre history and performance studies professor, write a 1000-word, highly detailed textbook section for first-year university students on the performance of gender. Include learning objectives at the beginning and a glossary of key terms at the end.
What I got back was a pretty un-nuanced chapter, and I wasn’t really asking for what it gave (which was Performing Gender in Theatre and Performance) – I was keen on non-theatre overview of the topic. I did like the question for consideration – they went beyond the text as provided:
In what ways does theatre reinforce traditional gender roles?
How can cross-casting or drag performance disrupt assumptions about gender identity?
What are the ethical considerations of casting decisions in terms of gender representation?
How does the body function as a site of both compliance and resistance in gender performance?
In terms of bias – it stuck with Western practices by and large (although threw in kabuki) in its review of gender on stage.
It was OK, but I think first years could take on more depth. Would I use it….probably not. I dislike linear summaries and euro-centric theatre history, and I think it should have started with Erving Goffman (which I do in my class discussion about gender performance). Was it awful? no – just not my cup of tea. I’d really need to think about my prompts.
My prompt was: “As a sociology professor, write a 750-word, highly detailed textbook section for first-year college students on intersectionality. Include learning objectives at the beginning and a glossary of key terms at the end.”
ChatGPT’s response was an excellent *outline* of this section, but didn’t offer much depth. Each component of the response was generic and high level although it did accurately identify the historical context and had appropriate examples. The language was too colloquial at times, but that can probably be adjusted through further prompts. And of course, ChatGPT doesn’t seem to be able to count so it gave me nearly 900 words when I asked for 750.