CONFERENCE DAYThursday, 15 October 2026

• Why many current conversations about AI in education are missing the realities of classroom practice, workload and assessment
• Emerging challenges for schools, including academic integrity, equity, feedback, student agency and teacher confidence
• Practical examples of how teachers and school leaders are beginning to integrate Generative AI meaningfully and responsibly
• What education systems and leaders should prioritise now to support teachers through rapid technological change

Educators have always pursued the promise of personalised learning, yet were constrained by time, energy, and the limitations of available technology.

This presentation traces the evolution of personalisation and differentiation from basic levelled content to today's breakthrough moment, where truly authentic, individualised learning is not just possible, but scalable for every student.

• Contemporary AI systems can be seen as an emerging “species” that could compete with humans
• Successful co-existence with AI will involve nurturing uniquely human cognitive abilities such as innovation/creativity and critical thinking
• The teaching of critical thinking and creativity is currently given a low priority in tertiary education programs, despite claims that these are 'graduate skills'
• However, a growing body of research highlights methods and programs that can be used to successfully boost these abilities. Expanding these programs and implementing them in tertiary curricula is essential for human-AI co-existence

• AI as prediction shaped by human data and behaviour
• Students as active contributors to AI systems
• Recommender systems as a gateway to AI literacy
• Understanding digital influence, agency and choice
• Teaching through data driven recommender system experiences

• Operationalising National Frameworks: Practical steps for ACT school leaders and policymakers to translate the Australian Framework for Generative AI in Schools into clear, enforceable local governance.
• Building Capability, Not Dependency: Shifting away from heavy reliance on external consultants toward cultivating organic AI literacy across leadership and administrative teams.
• Human-Centred Accountability: Ensuring human oversight, data privacy, and ethical guardrails remain central as AI-assisted workflows expand across ACT schools.

• The gap isn’t always knowledge. It’s practice
• AI simulations move professional learning from passive to participatory
• New frontiers – Safety, repetition, personalisation and new evidences of learning
• Early evidence from the St. Clare’s pilot
• School well-being and career readiness as the initial frontiers

• Governance and guardrails: Development of policies, safety boundaries, privacy and academic integrity expectations, and practical procedures for staff and students
• Implementation and capability building: Staff training, fit-for-purpose use cases, and the creation and deployment of AI agents within an explicit teaching and learning context
• Community partnership: Parent/carer communication strategies, consent and transparency, responding to concerns, and building a shared language around safe and ethical AI use
• Student learning and outcomes: Explicit teaching of safe, ethical, and appropriate AI use, examples of classroom applications, and early indicators of impact (engagement, efficiency, quality, and consistency)

• Understanding privacy regulations and student data protection in AI implementation.
• Addressing bias and fairness in AI tools within educational settings.
• Developing frameworks for ethical AI integration in schools.
• Practical strategies for maintaining academic integrity with AI tools.

• How the college has developed ‘AI literacy outcomes’ to explicitly teach AI to align with The Framework for Generative AI in schools and the general capacities of the Australian curriculum.
• Identifying connections between our AI literacy outcomes and content descriptors of the Australian Curriculum to meaningfully integrate the teaching of AI in all subjects and within assessment tasks.
• Focusing on AI feedback literacy, developing AI robust assessment and guiding students to use AI transparently, responsibly and ethically