📚 Unit Plans → 🌍 Unit 7: Digital Technologies & AI Ethics → 📖 Unit 7 Lesson 1: AI Through Te Ao Māori Lens
Years 11–13Hangarau / Technology

Lesson 1: AI Through a Te Ao Māori Lens

Frame a senior inquiry by testing AI claims against named sources, mapping authority and affected groups, and establishing evidence and governance requirements for a design project.

Unit 7: Digital Technologies & AI Ethics

Inquiry, Audit & Culturally Responsive Design

1 AI & Te Ao Māori
2 AI Ethics & Bias
3 Ethics in Practice
4 Cultural Protocols
5 Tech for Good
🪶 Authority boundary. This lesson teaches AI literacy and source checking; it does not authorise an AI system or a classroom to define or validate mātauranga Māori. Do not treat the values or English glosses on this page as one universal or iwi-neutral framework. Meanings, permissions, and applications can vary between iwi and hapū. Use named, public sources from the relevant Māori authority, seek mana whenua or iwi/hapū guidance when localising, and do not enter knowledge into an AI system unless the people who hold it have approved that use. Māori and Indigenous ākonga are learners, not default cultural authorities.

Lesson 1: AI Through a Te Ao Māori Lens

Inquiry & Design Studio · Lesson 1

Studio checkpoint: frame the inquiry

Teams select one consequential AI system used in everyday or community life. They begin an evidence log that records its purpose, data inputs, affected people, uncertainties, named source base, and the authorities whose permission or governance would be required.

  • Scaffold: Teacher-modelled inquiry question, then a team evidence log
  • Studio output: One-page AI suitability and stakeholder map
Earlier Years 9–10 pathway: Critical AI Ethics Seminar →

🌅 Opening & Source Protocol

Source protocol: evidence before interpretation.

Opening Protocol (5 minutes)

  1. Name the source: Identify who produced it and the audience or project it represents.
  2. Keep the boundary: Separate what the source states from what an AI system or learner infers.
  3. Reserve authority: Mark local, restricted, or cultural decisions for the people entitled to make them.

🎯 Learning Objectives & Success Criteria

By the end of this lesson, ākonga will be able to:

  • Define: Explain artificial intelligence in accessible, non-technical language
  • Distinguish: Separate what an AI output claims from what a named source and the relevant cultural authority can establish
  • Analyse: Examine AI using named ethical and Māori data-sovereignty sources
  • Evaluate: Assess an AI claim using a named source, its stated scope, and the relevant authority boundary

Success Criteria - Ākonga will demonstrate:

  • ✓ Clear understanding of what AI is and isn't
  • ✓ Recognition of both opportunities and threats AI presents
  • ✓ Accurate use of a named source without extending it beyond its scope
  • ✓ Clear separation of evidence, inference, uncertainty, and decisions held by others

Phase 1: AI Demystification - What Actually IS Artificial Intelligence? (20 minutes)

Interactive AI Understanding Workshop

15 minutes exploration + 5 minutes synthesis

AI Myth-Busting Activity (8 minutes):

Students work in pairs to categorise statements about AI as "Fact," "Fiction," or "Complicated":

Statements to Categorise:
  • "AI can think like humans"
  • "AI is just very sophisticated pattern recognition"
  • "AI will replace all human jobs"
  • "AI can be biased against certain groups of people"
  • "AI is completely objective and neutral"
  • "AI can create art and write stories"
  • "AI understands the meaning of what it says"
  • "AI systems learn from the data they're trained on"
  • "AI is conscious and has feelings"
  • "AI can make decisions that affect real people's lives"

Hands-On AI Exploration (7 minutes):

Students interact with simple AI tools to understand how they work:

Station 1: Language AI — Cultural Claim Source Check

Kaiako preparation: Supply a short, named, public source from the relevant mana whenua, iwi, hapū, or Māori-led authority. If no appropriate source is available, use a non-cultural topic rather than improvising a Māori reference answer.

  • Ask one question that the supplied source can answer directly.
  • Identify one checkable claim in the AI response.
  • Mark it supported, partly supported, contradicted, or not verified, and cite the exact source evidence.
Station 2: Image Recognition
  • Use Google Lens or similar to identify teacher-selected public images
  • Keep the image type constant while changing one feature at a time
  • Record what it recognises, misses, or labels without enough evidence
Station 3: Recommendation Systems
  • Examine Netflix, Spotify, or TikTok recommendations
  • Discuss how the algorithm "learns" preferences
  • Consider what influences these recommendations

Synthesis Discussion (5 minutes):

Whole class discussion to build shared understanding:

  • What surprised you about how AI actually works?
  • What can AI do well? What are its limitations?
  • How is AI different from human intelligence?
  • What questions do you still have about AI?

Phase 2: Authority and Evidence — What Can This Source Establish? (25 minutes)

Senior Source and Authority Audit

Set the boundary (3 minutes)

Mātauranga Māori and machine-learning output are not equivalent systems to be scored against one another. An AI output can be inspected as a generated claim. It cannot define mātauranga Māori, validate it, or decide who may share it.

Audit two texts (10 minutes)

Kaiako preparation: Supply one short, public source from a named mana whenua, iwi, hapū, or Māori-led authority and one AI response to a question that source can answer. Record the source title, author or organisation, date, URL, and stated scope. Do not use restricted knowledge.

  1. Locate authority: Who produced each text, and what authority does that person or organisation claim?
  2. Check scope: Is the source speaking for a named place, iwi, hapū, organisation, project, or purpose?
  3. Trace evidence: Which claims can be tied to exact source evidence?
  4. Mark limits: Which claims remain unsupported, outside the source’s scope, or subject to permission?

Build the studio evidence ledger (8 minutes)

ClaimNamed source or authoritySupported, contradicted, or not verified?Scope or permission limit
AI claim 1
AI claim 2

Set design constraints (4 minutes)

  • Which AI claim may be used because the named evidence supports it?
  • Which claim must remain “not verified”?
  • Who would need to be consulted before applying this material locally?
  • What information should stay out of the AI system because permission is absent?

Studio output: Add one evidence-backed claim, one explicit limit, and one named next authority or source to the team evidence log.

Phase 3: Source & Authority Framework Development (25 minutes)

Evidence-Bound AI Ethics Workshop

Build the source-and-authority framework (15 minutes):

Groups use Te Mana Raraunga’s published principles, or a more relevant named Māori-led source supplied by the kaiako. These cards restate checkable requirements from that source; they are not universal definitions of Māori values.

Authority

Applied to AI: Whose data or knowledge is involved, and who may decide how it is used?

  • Who currently makes the decision?
  • Who may challenge, change, or stop the system?
  • Which decision cannot be made in this classroom?
Provenance and purpose

Applied to AI: What source, purpose, context, and parties are recorded for the data and output?

  • Where did the data come from?
  • Why was it collected?
  • What context is absent or cannot be inferred?
Consent, accountability, and benefit

Applied to AI: What permission exists, who answers for harm, and who receives the benefit?

  • Can consent be withdrawn?
  • Who can inspect, correct, or remove data?
  • What safeguard follows directly from the source?
Scope and uncertainty

Applied to AI: What conclusion can the source support, and what remains outside it?

  • Which people or context does the source actually cover?
  • Which claim needs another source?
  • Who holds the next decision?

AI System Evaluation Practice (10 minutes):

Groups use their framework to evaluate one teacher-supplied case with a named evidence pack:

Scenario Options (each group chooses one):
  • Social Media Algorithm: How Facebook/Instagram decides what content to show users
  • Job Application Screening: AI that filters job applications before humans see them
  • Medical Diagnosis AI: AI that helps doctors identify diseases from symptoms or images
  • Predictive Policing: AI that predicts where crimes are likely to occur
  • Educational AI Tutor: AI that provides personalized learning support to students
Evaluation Process:
  1. System understanding (3 mins): What does the evidence establish about what the system does?
  2. Source and authority audit (5 mins): Apply the four cards and cite the relevant evidence.
  3. Recommendation (2 mins): Name one source-backed safeguard, one uncertainty, and who holds the next decision.

🌅 Whakamutunga - Reflection & Closing

AI Ethics Commitment & Evidence Check (5 minutes)

Personal AI Ethics Reflection:

Students complete individual reflection:

  1. What is one thing about AI that you understand differently now?
  2. Which named source most changed your judgement, and what is its scope?
  3. How might you approach AI tools differently after this lesson?
  4. What questions about AI and ethics do you want to explore further?

Closing Circle - Evidence and Limits:

Students share one claim they can now support and one limit they must keep visible.

Exit rule: A fluent AI answer is still a claim. Keep its source, scope, uncertainty, and authority boundary visible.

📊 Assessment & Next Steps

Formative Assessment - Today's Evidence:

  • Conceptual Understanding: Accuracy in AI myth-busting and system exploration
  • Source and authority analysis: Quality of the claim ledger, source use, scope limits, and named next authority
  • Source and authority: Accurate use of named sources, visible scope, and decisions reserved to the relevant authority
  • Critical Thinking: Sophistication of AI system evaluation

Preparation for Lesson 2:

  • AI Observation: Notice and document 3 examples of AI in your daily life
  • Bias Investigation: Find one example of AI bias or error in news/social media
  • Source reflection: Choose one named source about an affected group; record what it establishes and what further evidence would be needed.

🛠️ Teacher Resources & Adaptations

AI Tools for Classroom Exploration:

  • ChatGPT/Claude: Free language AI for testing responses
  • Google Lens: Image recognition for cultural object testing
  • Teachable Machine: Simple tool for students to train their own AI
  • AI Safety Resources: Partnership on AI educational materials

Cultural Consultation Support:

  • Authority and sources: Use a named, public mana whenua, iwi, or hapū source for local claims; seek guidance from the relevant authority rather than asking an adviser to “validate” a generic mātauranga discussion
  • Te Reo Integration: Incorporate relevant Māori tech vocabulary
  • Iwi Tech Leaders: Connect with Māori professionals in tech industry
  • Cultural Protocols: Ensure respectful handling of cultural knowledge

Differentiation Strategies:

  • Tech Experience Levels: Pair tech-savvy with less experienced students
  • Cultural Knowledge: Welcome diverse cultural backgrounds while centreing Māori perspectives
  • Learning Preferences: Offer visual, kinesthetic, and discussion-based options
  • Extension Activities: Advanced students can research specific AI applications