Understanding AI: from concepts to classroom practice - Sessions 1 + 2: 6th October

In partnership with Raspberry Pi Foundation and supported by Google.org we are delighted to offer Experience AI, an educational programme offering cutting-edge resources on artificial intelligence and machine learning for teachers and students.

Learn how to support your students in understanding, questioning, and evaluating AI. This interactive workshop explores real-world applications, ethical considerations and classroom-ready activities that build AI literacy, helping you move beyond using AI tools to teaching about how AI works. All resources are aligned with Australian Curriculum: Digital Technologies and Mathematics, as well as Digital Literacy capability.

Teachers will see examples of content from Year 5 to 10. The content is valuable for all teachers who want to build their own capability and understanding of this important area. You will leave with access to and familiarity with classroom-ready resources.

Content details

Session 1: Mechanics and limitations

Focus: How does AI learn and why is it biased?

Participants will:

• engage in discussions and hands-on practical activities

• build a supervised learning model to classify supermarket items using the Machine Learning for Kids tool

• interact with large-scale datasets like Google’s "Quick, Draw!"

• investigate how data bias and societal bias can lead to inaccurate or unfair outcomes

• explore tools to lead ethical discussions with your students in the classroom.

Session 2: Process and Accountability

Focus: How is AI built and is it safe to release?

We move from basic concepts to the practical application of the AI project lifecycle. This session is designed to equip you with technical knowledge and classroom-ready resources to support your students as they understand, question and evaluate the AI systems surrounding them.

Participants will step into the role of both developer and ethical inspector as we:

• explore the six critical stages of the project lifecycle from defining a problem, cleaning data, to testing and explaining the model

• practise framing real-world problems through user stories

• explore confidence thresholds and model cards

• apply the FATPS principles, including privacy and security implications, to judge if an AI application is safe for public release.

Important notes

• Participants will receive a certificate of completion for 6 hours of professional learning, aligned to the Australian Curriculum and AITSL Standards.