Navigate Your AI Journey

Assess your understanding and application of Artificial Intelligence in education. This tool aligns with key competency frameworks to provide insights for NZ educators.

AI Concepts

Understanding AI's nature, function, and impact.

1. Your understanding of technical terms like 'Machine Learning':





2. Your understanding of AI's impact on society:





3. Your ability to explain how AI systems work:





Application & Technical Skills

Applying AI tools effectively and understanding technical aspects.

1. Selecting and using AI tools in your work:





2. Evaluating if/how AI tools fits an educational task:





3. Your understanding of technical AI concepts:





AI Digital Citizenship

Navigating AI's ethical, social, cultural, and safety aspects.

1. How do you approach AI ethics (e.g., bias, fairness) in your practice?





2. How do you identify and manage AI risks (e.g., privacy, security)?





3. How do you promote responsible and inclusive AI use?





AI Educator Competency Framework

Explore capabilities across 3 categories and 4 competency levels: Level 1 (Explorer), Level 2 (Practitioner), Level 3 (Strategist), Level 4 (Innovator).

Category / Level Level 1: Explorer Level 2: Practitioner Level 3: Strategist Level 4: Innovator
AI Concepts
  • Identifies common AI uses.
  • Defines basic terms (e.g., Machine Learning).
  • Gives examples of AI societal impact.
  • Aware of basic user interaction.
  • Explains AI learns from data sources.
  • Applies terms to explain model training steps & algorithms.
  • Discusses human role in development & potential harms.
  • Describes key concepts (neural networks, algorithms).
  • Understands complex task decomposition & large dataset use.
  • Debates AI's future role & interdisciplinary links.
  • Explains advanced models (generative, transfer learning).
  • Understands state-of-the-art, future directions & risks.
  • Independently learns new AI concepts/techniques.
Application & Technical Skills
  • Selects/applies basic tools for specific tasks.
  • Evaluates suitability for simple tasks.
  • Assesses basic implications of use.
  • Focuses on interface.
  • Applies tools across different fields.
  • Uses AI for basic application creation/problem solving.
  • Explains basic ML approaches & human-centered design.
  • Discusses AI transparency/explainability needs.
  • Selects/implements creative AI approaches.
  • Uses appropriate tools (scripts, libraries, visualizations).
  • Demonstrates computational thinking.
  • Understands model building steps (train/test/validate/deploy).
  • Designs/implements AI strategies/apps using advanced techniques.
  • Designs, fine-tunes & troubleshoots complex models.
  • Manages AI projects & collaborates effectively.
  • Plans end-to-end AI processes.
AI Digital Citizenship
  • Identifies basic ethical implications (bias, fairness).
  • Identifies common risks (security, privacy).
  • Demonstrates basic responsible/safe use.
  • Discusses cultural bias/diversity impact.
  • Understands how bias occurs; discusses social impacts.
  • Assesses data use risks (collection, accuracy, security).
  • Implements privacy protection strategies (consent).
  • Advocates for inclusive/accessible design.
  • Evaluates design/implementation ethics (honesty, IP) using principles.
  • Applies strategies for user safety & system reliability.
  • Considers biased data impact; uses AI critically.
  • Recognizes data management & cybersecurity needs.
  • Analyzes future societal implications ethically.
  • Contributes to ethical/safe use policies.
  • Applies transparency/explainability/fairness concepts.
  • Identifies/evaluates complex risks (interaction, IP, deliberate bias).
  • Develops/implements inclusive systems respecting context.