AI Through a Te Ao Māori Lens
Use the activities below as evidence for a facilitated seminar: when is AI appropriate, who has authority to decide, and which values should constrain its use? Ākonga finish by recording an initial ethical position and one question they need to investigate.
- Scaffold: Think–pair–share, teacher-led concept checks, and an ethics reflection frame
- Seminar outcome: Initial position statement with evidence and an open question
🌅 Opening & Source Protocol
🎥 Media Anchor
Video: How AI Systems Work
Watch plan: play the first 7 minutes (the neuron-and-layers intuition) — the rest of the video goes deeper into the mathematics and is optional extension.
- Which AI capability in this lesson is most useful in education contexts?
- Where are the key limitations students must keep in mind?
Source protocol: evidence before interpretation.
Opening Protocol (5 minutes)
- Name the source: Identify who produced it and the audience or project it represents.
- Keep the boundary: Separate what the source states from what an AI system or learner infers.
- 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 synthesisAI 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:
📄 Handout Support: Use Introduction to LLMs for background on how language AI works, and Prompt Engineering 101 for effective question techniques.
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 the claim supported, partly supported, contradicted, or not verified, and cite the exact source evidence.
Document: What can the named source establish? What remains uncertain? Who would hold authority over any local or restricted knowledge?
Observe:
- Does the output identify its source and scope?
- Does it acknowledge that Māori knowledge and practice are not identical across iwi and hapū?
- Does it admit what it cannot verify?
Station 2: Image Recognition
- Use Google Lens or similar to identify ordinary classroom objects
- Compare a clear image with a cropped, rotated, or poorly lit image
- Do not upload taonga, restricted material, or images without permission
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)
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.
- Locate authority: Who produced each text, and what authority does that person or organisation claim?
- Check scope: Is the source speaking for a named place, iwi, hapū, organisation, project, or purpose?
- Trace evidence: Which claims can be tied to exact source evidence?
- Mark limits: Which claims remain unsupported, outside the source’s scope, or subject to permission?
Build a claim ledger (8 minutes)
| Claim | Named source or authority | Supported, contradicted, or not verified? | Scope or permission limit |
|---|---|---|---|
| AI claim 1 | |||
| AI claim 2 |
Make the ethics decision (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?
Output: One evidence-backed claim, one explicit limit, and one named next authority or source.
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:
- System understanding (3 mins): What does the evidence establish about what the system does?
- Source and authority audit (5 mins): Apply the four cards and cite the relevant evidence.
- 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:
- What is one thing about AI that you understand differently now?
- Which named source most changed your judgement, and what is its scope?
- How might you approach AI tools differently after this lesson?
- 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 Bias & Justice):
- AI Observation: Notice and document 3 examples of AI in your daily life (Netflix recommendations, phone autocorrect, social media feeds)
- Bias Investigation: Find one news article or social media post about AI bias or errors
- Source reflection: Choose one named source about an affected group; record what it establishes and what further evidence would be needed.
- Optional: Try the AI Cultural Bias Testing Protocol on one AI tool
Next lesson builds on today's AI understanding to examine how bias enters systems and affects different communities unequally.
🛠️ 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
Kaiako Planning Snapshot
Digital Technologies / Social Studies — AI Ethics & Te Ao Māori — Years 9–10
Learning context: Digital Technologies and ethical reasoning through a te ao Māori lens (Years 9–10).
Ngā Whāinga Akoranga — Learning Intentions
- Explain how AI systems work at a conceptual level and identify their ethical dimensions
- Analyse AI bias and algorithmic justice through a te ao Māori and tikanga framework
- Evaluate the implications of AI for tino rangatiratanga, Māori data sovereignty, and digital futures
Inclusion & Accessibility
ESOL/ELL: pre-teach AI vocabulary using visual analogies and familiar examples. Neurodiverse learners: offer unplugged alternatives to all digital tasks. Accessibility: all interactive digital activities have non-screen analogue alternatives.
Audited curriculum connections
- Te Mātaiaho (2025) · Technology · Phase 4 (Years 9–10) · Knowledge: “Technological outcomes can raise ethical and legal issues such as privacy, data security, bias, and fairness, which affect individuals and society.”