I’ve been thinking about how I could possibly adapt the excellent work done by the Penn State Commonwealth Campus Teaching Support team on Assignment Level Guidance on Generative AI Use. I wanted to explore something analogous for course design. Instead of guidance for students and faculty, I am interested in guidance for faculty authors and instructional designers.
If AI is to be used at any stage, to any degree, all content must be reviewed by the appropriate subject matter experts. In this case, that might be the faculty authors, faculty reviewers, multimedia specialists, or instructional designers.
There are some scale questions that might start a conversation:
- How much are we going to allow Gen AI to be used?
- Are there certain tasks that it might be appropriate for compared to others?
- How would we properly cite or document the work?
A framework to map how to apply Gen AI tools in ways that are acceptable, ethical, and meaningful would be useful. These would be captured for each stage of a course development or revision project. For example:
- Scope definition
- Creating learning outcomes
- Developing a detailed course outline
- Development and alignment of assignments
- Researching and selecting third party materials and creating new content
- Content and Quality reviews
- Surveying, analyzing, and resolving course feedback
Along the way, there should be resources available to authors and designers to assist with agreeing upon a scale and framework for using Gen AI in course development projects.
- Example AI-guided workflows for the overall and specific stages of course development
- Prompt templates to rapidly and effectively apply AI
- FAQs on common faculty questions related to bias, reliability, training data, etc.
- Course templates with example activities
- Course review rubrics, like Clear QM, to evaluate content whether it was generated by AI or not
OpenAI. (2025). ChatGPT 4o. https://chat.openai.com/chat