EdCompass
Resources for educators, students, and organisations building AI-native software engineering capability. Student learning, upskilling, and industry-ready competency.
Go to EdCompassFor Educators
Curriculum resources, assessment frameworks, and classroom materials for teaching AI-native engineering.
Curriculum design guides
Structured guides for building agentic SE courses that develop capability, not just familiarity.
PAAIR assessment rubrics
Plan, Act, Assess, Inspect, Reflect — rubrics that measure process, judgement, and evidence rather than just final code.
Workshop activities
Hands-on activities: agent task design, AI output review, trust calibration, and verification exercises.
Slide decks and lesson plans
Open-access materials covering each principle in the AI-Native Engineer Playbook.
For Students
Guides, portfolio templates, and career resources for developing genuine AI-native capability.
Getting started guide
Your first steps as an AI-native engineer — setting up workflows, understanding accountability, and building good habits.
Portfolio template
Document what you asked the agent to do, what you accepted or rejected, how you verified output, and what you learned.
Assessment preparation
How to evidence process, judgement, and growth in agentic SE assignments and projects.
Career guidance
What employers expect from AI-native graduates — and how to demonstrate genuine capability.
For Organisations
Training blueprints, competency frameworks, and capability measurement tools for engineering teams.
Onboarding blueprint
A structured programme for introducing engineers to agentic workflows safely and effectively.
Competency framework
Defined levels of AI-native engineering capability — from awareness to expert practitioner.
Internal training design
How to build an internal curriculum aligned to your codebase, standards, and risk profile.
Measuring capability
Metrics and evidence approaches for tracking team-level AI-native engineering maturity.
The PAAIR assessment approach
Traditional code-quality rubrics do not capture AI-native engineering capability. PAAIR assesses the full process: what the student planned, how they acted with agents, how they assessed outputs, what they inspected and changed, and what they reflected and learned.