Artificial intelligence is reshaping engineering education, yet it's often misunderstood or oversimplified. In this module, educators will build a clear, practical understanding of AI fundamentals and learn how to address them confidently in the classroom. You’ll explore core terminology such as machine learning, foundation models, neural networks, and generative AI, while examining common misconceptions, limitations, and ethical considerations.

Beyond definitions, this module focuses on teaching strategy. Learn how to explain complex ideas without hype, protect student privacy when using AI tools, design assignments that promote original thinking, and establish clear guidelines for academic integrity. The goal is not just technical literacy, but instructional clarity and confidence.

It's expected that educators will have a general understanding of engineering concepts and experience teaching or supporting higher education coursework.

In this module, educators will learn how to:

Explain key AI concepts in accessible, classroom-ready language.
Distinguish between narrow AI, foundation models, and theoretical concepts such as AGI and ASI.
Evaluate data quality and address privacy considerations when using AI tools with students.
Design assignments that incorporate AI while maintaining academic integrity.
Discuss ethical considerations, bias, transparency, and responsible use in both education and industry.

By the end of this module, you’ll be equipped to demystify AI for your students and frame it as a powerful, evolving tool rather than a threat to learning.

Data: The fuel of AI