Teaching AI Ethics
A free series of resources for educators and students
from leonfurze.com
Thanks for checking out the Teaching AI Ethics resources! Before reading any further, please note that these resources are currently being updated for 2025-2026. Click here to jump straight to the updates, or continue on to read them in publication order.
Cover image: Rens Dimmendaal & Johann Siemens / https://betterimagesofai.org https://creativecommons.org/licenses/by/4.0/
Artificial Intelligence presents many complex ethical concerns which are well worth discussing with our students. The Teaching AI Ethics series started as a single post covering the nine areas I’d identified as particularly important to education, from bias and discrimination to reinforcing societal power structures.
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The original post was so popular that I broke it down into nine further posts. Each post includes a detailed discussion of the ethical concern along with links to other articles and resources. There are case studies, discussion questions for a variety of disciplines, and lesson ideas which can be used across the curriculum.
Though I designed these posts with a K-12 audience in mind, they’ve since been picked up at a tertiary level for discussions with pre-service teachers and other undergraduate students. I’ve created this page as a single reference point for all of the posts.
Here’s a quick list of the posts. Scroll down for more information:
- Bias and discrimination
- Environmental concerns
- Truth and academic integrity
- Copyright
- Privacy
- Datafication
- Emotion recognition
- Human labour
- Power
Original article
This article covers all nine areas and introduces the concept of AI ethics in an education context:
Beginner level
The three articles at this level cover the most commonly discussed ethical concerns with Artificial Intelligence: Bias, environmental concerns, and “truth”. In these articles, you’ll find case studies and discussion points for each area.
Intermediate
At this level, the concepts become more complex and the ethical issues harder to find information on, or more difficult to untangle. This level includes copyright, privacy, and datafication, and offers ways for educators to explore how Artificial Intelligence developers have collected intellectual property and data to train models in ways which don’t always protect users’ rights or privacy.
Advanced
These final three posts deal with the most complex ethical concerns: affect recognition, human labour, and power. These articles explore how AI is already being used to recognise and evaluate human emotions and actions, and how the processes of AI can reinforce societal prejudices and hegemonies. These posts offer discussion points and lesson activities to address these complex issues with students.
2025-2026 Updates
These technologies change rapidly, but the ethical concerns behind them have not gone away. If anything, it’s even more important now to discuss the complexities of GenAI with students of all ages. In 2025, I started updating the entire series. It’s a long and complex project, and one that I’ll continue working on in 2026.
These are the 2025-2026 updates:
Teaching AI Ethics 2025: Introduction
Over the next few months, I’ll be updating my 2023 Teaching AI Ethics collection. In this post, I’ll explain why the updates are necessary and give a recap on the nine original areas from the series. When I wrote the original series in 2023, ChatGPT was only just on people’s radars. I had started my…
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Teaching AI Ethics 2025: Bias
This post initiates a nine-article series revisiting the “Teaching AI Ethics” resources from 2023. It highlights ethical concerns surrounding generative AI, especially bias from data, models, and human input. The series aims to educate on bias awareness and mitigation through practical curriculum examples, reflecting growing familiarity with AI technologies by 2025.
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Teaching AI Ethics 2025: Environment
This article, part of a series updating “Teaching AI Ethics,” explores the environmental impact of artificial intelligence, particularly generative AI. It emphasizes the need for transparency in AI’s energy usage, highlights the resource-intensive nature of training and using AI models, and prompts educational discussions on sustainable technology practices.
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Teaching AI Ethics 2025: Truth
The third post in the Teaching AI Ethics series explores “truth” and GenAI, including the implications for education.
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Teaching AI Ethics: Copyright 2025
This is an updated post in the Teaching AI Ethics series, originally published in 2023. Given the explosive developments in AI and copyright over the past two years – including major court cases, government decisions, and the first billion-dollar settlements – it felt essential to revisit this intermediate-level ethical concern. For the previous updated post…
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Teaching AI Ethics: Privacy 2025
This is an updated post in the series exploring AI ethics, building on the original 2023 discussion of privacy concerns. As generative AI has become embedded in our daily digital lives from chatbots to smart glasses the privacy implications have grown more complex and immediate. This post explores how GenAI has transformed privacy risks and…
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Teaching AI Ethics: Data 2025
This is an updated post in the series exploring AI ethics, building on the original 2023 discussion of data and “datafication”. This post explores why and how GenAI relies on so much data collection, how AI companies are gathering the information, and what it means for education.
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Teaching AI Ethics 2026: Human Labour
This is an updated post in the series exploring AI ethics, building on the original 2023 discussion of human labour. Since 2023, the human cost of AI has become one of the most pressing ethical issues in the industry, with major lawsuits, union formation, and investigative journalism bringing the exploitation of workers into public view.…
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