Diving into Data and Privacy confronts one of the most important but least understood dimensions of AI in education: what happens to the data. Every interaction with an AI tool generates data, and educators need to understand where it goes, who owns it, and what risks it creates.

This course walks participants through the fundamentals of data privacy in the context of AI, including how large language models are trained, what data schools share when they adopt AI platforms, and how privacy legislation (including the Australian Privacy Principles) applies to educational settings.

Participants learn to read and evaluate privacy policies, ask the right questions of vendors, and understand concepts like data minimisation, consent, de-identification, and third-party data sharing. The course also explores the tension between personalisation and surveillance, helping educators think critically about the trade-offs involved in AI-powered learning tools.

By the end of the course, educators are equipped to contribute meaningfully to school-level decisions about AI procurement, data governance, and student privacy protection.