Teaching and Learning with Generative AI
Introduction
This resource provides an overview of key considerations when exploring the impact of Generative AI on Teaching and Learning. It is built around 7 components of AI Literacy: Knowledge, Skill, Ethics, Values, Affect, Pedagogy and Interconnectedness. You can dive into the sections that align with your specific questions and interests in Generative AI.
Throughout, there will be opportunities to reflect and to engage in different activities designed to allow you to explore the components of AI Literacy. At the end of each section, you can reflect on your own AI Readiness for each component of AI Literacy.
These will be indicated as follows:
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Activity |
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Making Connections |
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Readiness Reflection |
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Stop and Reflect |
This resource was last updated on [DATE].
AI Literacy in Education
AI Literacy is the ability to understand, use, and critically reflect on Generative AI technologies.
Common across many definitions of AI Literacy include the ability to:
- Understand what AI technologies are and how they work
- Use AI technologies effectively to achieve your goals
- Critically evaluate Generative AI outputs
- Develop practices that acknowledge broader ethical issues of AI
(Ng et. al., 2021; Miao and Cukurova, 2024, Becker et. al., 2024)
Other important components of AI literacy for education include:
- Developing a practice that aligns with our individual and collective values
- Recognizing and managing our emotional response to AI-technologies
- Understanding how AI technologies and AI practices are interconnected with other factors within larger educational and social structures
The AI Literacy Framework used in this resource acknowledges all of these domains as important, but also not mutually exclusive.

Knowledge: What do educators need to know about Generative AI?
Ethics: What ethical considerations do educators need to be aware of when choosing to use/not use Generative AI?
Affect: How can educators navigate their emotional response to Generative AI technologies?
Skill: What do educators need to be able to do with Generative AI?
Pedagogy: (How) can Generative AI support teaching and learning?
Interconnectedness: How are Generative AI technologies and practices impacted by larger institutional, social, and political factors?
To get started, click on any of the sections below:
Table of Contents
Teaching and Learning with Generative AI
Part 1: Foundations of Generative AI (Knowledge)
Part 2: Ethical Considerations of Generative AI (Ethics)
- 2.1 Overview and Outcomes
- 2.2 Privacy, Intellectual Property & Copyright
- 2.3 Access & Accessbility
- 2.4: Environmental Impact of AI
- 2.5: Bias & Misinformation
- 2.6: Summary
Part 3: Emotional Considerations of Generative AI (Affect)
Part 4: How to use Generative AI (Skill)
- 4.1 Overview and Outcomes
- 4.2 Prompt Engineering
- 4.3 Critically Appraising AI Outputs
- 4.4 Summary & Reflection
Part 5: A Values-Based Approach to Generative AI
- 5.1: Overview and Outcomes
- 5.2: Individual Values
- 5.3: Fundamental Values of Academic Integrity
- 5.4: Crafting the Syllabus Statement
- 5.5: Summary & Reflection
A subset of Deep Learning that can use learned rules or patterns to generate new content.