Wave 1 - 2026

The Differentiation Engine

The Differentiation Engine shows educators how to use AI to personalise learning without lowering standards. Discover practical ways to adjust content, scaffolding, feedback, and extension so every student is appropriately challenged.
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Host

Matthew Esterman

Who is this for

  • Classroom teachers
  • Casual teachers
  • Secondary
  • Primary 
  • Early childhood

Standards

APST 1.5
APST 3.3
APST 5.1

Included

  • Templates and resources
  • Applied implementation tasks
  • Community access
  • Certificate of completion

Course Description

The Differentiation Engine explores one of AI’s most powerful applications in education: responsive learning design. Rather than using AI to complete work for students, this course focuses on using AI to adjust learning conditions.

Participants learn how to generate multiple entry points into complex concepts, adapt reading levels, create scaffolded explanations, develop tiered questioning, and design extension tasks for high-performing students. The emphasis is on maintaining rigour while increasing accessibility.

The course also addresses the cognitive science behind differentiation. When does scaffolding support growth, and when does it create dependency? How do we avoid cognitive atrophy while still providing targeted support?

Educators practise crafting prompts that preserve thinking demand and encourage productive struggle. AI becomes a flexible co-designer rather than a shortcut generator.

The Differentiation Engine helps schools move toward equity through precision. It enables teachers to respond to learner variability with efficiency and intention, strengthening both engagement and achievement.

What you will learn

  • How to use AI to create multiple entry points into complex concepts without lowering standards
  • How to generate tiered tasks, scaffolded supports and extension pathways in minutes
  • How to personalise reading levels, explanations and questioning while maintaining cognitive rigour
  • How to design interest-based and choice-driven learning pathways using AI
  • How to build repeatable AI-assisted workflows that make differentiation sustainable, not exhausting

Designed for Real Classrooms

Every strategy in this course has been tested in schools navigating real constraints: time, governance, workload and accountability. This is not theory. It is applied practice.

From Curiosity to Capability

AI experimentation is easy. Sustained capability is not. Educator Intelligence helps schools move from isolated exploration to shared professional judgement.
Your host

Matthew Esterman

Matthew Esterman is a nationally recognised educator, school leader and AI consultant working at the forefront of AI in education. He has supported independent, Catholic and government schools across Australia and internationally to integrate AI responsibly, strengthen governance, and build professional capability in teaching and leadership.
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