Table of Contents¹

Note that many of these pages are currently blank. They’ll fill up over time, as I work through my notes and resources. This is a framework that will guide my learning and over time will become a resource for anyone. Right now it’s nothing but a collection of links.

1. Why generative AI matters
   1. Conversational interfaces
   2. Ubiquitous AI
   3. Hype cycle
   4. Expertise
   5. Magic
   6. Personas
2. An introduction to generative AI
   1. Definitions of terms
   2. History of generative AI
   3. Training
   4. Generative AI and artificial intelligence
3. Large language models
   1. Foundation models
   2. Tokens
   3. Chatbots
   4. Prompting (try this one as an example)
   5. Multimodality
4. Ethics
   1. Bias
   2. Access
   3. Transparency
   4. Data
   5. Accountability
   6. Copyright
   7. Sustainability
   8. Automation
   9. Justice
   10. Recruitment
5. Regulation
   1. Copyright
6. Generative AI in the university
   1. AI-first institutions
   2. Institutional policy development
7. Generative AI in the classroom
   1. Teacher roles and identity
   2. Student roles and identity
   3. Classroom policy development
   4. Student collaboration
   5. Assessment
   6. Responsible use of generative AI
8. Generative AI for research
   1. Research assistant
   2. Writing assistant
   3. Reading assistant

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Footnotes

1. This will almost certainly not be the structure for the content. I’m experimenting with different organisation principles for the content and this category-type approach isn’t very satisfying for a learning resource. It’s too rigid and requires me to put everything into a box, which isn’t how learning works. ↩