What are some accessible, free and good quality courses educators can take to learn more about AI?

Foundations & AI Literacy

Useful starter courses for educators to learn about and with AI. 

AI Pedagogy Project. MetaLAB at HGSE. https://aipedagogy.org/guide/tutorial/ 

• Explore LLM’s from an education perspective. 

Elements of AI. University of Helsinki  https://www.elementsofai.com/

• Non-technical demystification of AI logic. Includes Introductions to AI and Building AI.

AI 101 for Teachers Code.org / Khan Academy 

• Practical classroom concepts & responsible use.

AI for Educators. Microsoft Learn. https://learn.microsoft.com/en-us/training/paths/ai-education/

• ISTE and UNESCO-connected course for educators. Basic overviews and skills. 

Generative AI for Educators with Gemini. Grow with Google. https://grow.google/ai-for-educators/ 

• Learn how to use generative AI tools to help you save time on everyday tasks, personalize instruction, enhance lessons and activities in creative ways, and more.

AI Basics for K-12 Teachers. Common Sense Media. https://www.commonsense.org/education/training/ai-basics-for-k-12-teachers 

• Understand the basics of generative AI and its impact on education. 

ChatGPT Foundations for K–12 Educators. Common Sense Media. https://www.commonsense.org/education/training/chatgpt-k12-foundations 

• Practical strategies for using their popular AI tool ChatGPT in schools. 

Advanced ChatGPT for K-12. Common Sense Media. https://www.commonsense.org/education/training/advanced-chatgpt-for-k-12 

• Builds upon the ChatGPT Foundations course with additional focus on developing the insights, mindsets, and practices that enable effective AI use in education.

Machine Learning & How AI Works

Courses that teach you how AI works. Many can be used with students. 

Hugging Face Learn Hub. HuggingFace. https://huggingface.co/learn 

• Includes courses on LLM, Robotics, Agents and more. 

Machine Learning Crash Course. Google Developers. https://developers.google.com/machine-learning/crash-course 

• Fast-paced, practical introduction to machine learning, featuring a series of animated videos, interactive visualizations, and hands-on practice exercises.

CS50’s Intro to AI with Python. Harvard. https://cs50.harvard.edu/ai/ 

• Implementing algorithms, search, and ML.

AI with MIT (suitable for students). MIT. https://appinventor.mit.edu/explore/ai-with-mit-app-inventor 

• Use MIT App inventor on a range of AI projects. 

Machine Learning with Amazon. AWS. https://aws.amazon.com/ai/learn/

• Practical modules to learn about how AI works.

Teachable Machine. Google. https://teachablemachine.withgoogle.com/

• Train a computer to recognize your own images, sounds, & poses.

AI Playground. Nvidia. https://www.nvidia.com/en-us/research/ai-playground/ 

• Interesting projects at the intersection of Art, AI and Science.

Ethical Perspectives on AI. 

Navigating the moral and environmental landscape of AI.

Ethics of AI. University of Helsinki.  https://ethics-of-ai.mooc.fi/ 

• Structured reasoning on fairness and human rights.

Critical AI Perspectives. University of British Columbia.. https://opl.educ.ubc.ca/ai-mooc/ 

• Evaluating equity, power, and systemic implications.

Ethics & Global Catastrophic Risks. Lingnan University. https://www.ln.edu.hk/philoso/hkcrc/risk 

• Includes modules on AI and environmental risks.

For deeper reading on AI using MIT’s Open Press texts, see this page.