Investigate, audit, then design
This edition uses an inquiry cycle and project checkpoints. Ākonga gather evidence about AI systems, audit who benefits or is harmed, and turn that analysis into a culturally responsive design proposal.
- Approach: Project inquiry and design studio
- Duration: Five 75-minute lessons plus project work
- Scaffolding: Evidence logs, audit tools, and design checkpoints
- Outcome: AI ethics analysis and a culturally responsive design proposal
A 6–8 week Digital Technologies unit for Years 11–13 providing a critical and practical introduction to AI through Māori Data Sovereignty, ethical reasoning, and community-centred innovation.
Students don't just learn about AI — they interrogate it. They examine whose values are encoded in algorithms, how Indigenous data is at risk, and what genuinely Māori-centred digital futures might look like. The unit culminates in students designing culturally-responsive technology proposals for real communities.
"Mā te huruhuru ka rere ai te manu"
It is the feathers that allow the bird to fly. Knowledge and ethics are the feathers we need in the digital age.
AI systems trained on biased data reproduce and amplify that bias at scale. For Māori communities, this means technologies that misrecognise te reo, fail to reflect tikanga, and make decisions — in health, criminal justice, welfare — that disadvantage tangata whenua. Students who understand this can advocate for change. And design better alternatives.
Ngā Whāinga Ako — Learning Intentions
- Identify how AI systems encode assumptions and values, and evaluate the real-world impact of algorithmic bias on communities — particularly Māori and Pacific communities
- Explain what Māori Data Sovereignty means in practice, using the CARE Principles and Māori Data Governance models as frameworks
- Apply a te ao Māori lens to evaluate existing digital technologies and propose design changes that would better serve tangata whenua
- Design a technology prototype or proposal that embeds tikanga Māori values (manaakitanga, kaitiakitanga, whanaungatanga) into its architecture and governance model
Ngā Paearu Angitū — Success Criteria
- I can identify ethical issues within AI systems and explain their real-world impact on specific communities
- I can apply a te ao Māori lens to evaluate digital technologies and their effects on tangata whenua
- I can articulate what digital sovereignty means and why it matters — using examples from Te Hiku Media, Māori health data, and iwi governance
- I can design a technology proposal that embeds Māori values into its architecture, not just its interface
Entry / On-level / Extension
- Entry: Provide worked examples of AI bias scenarios with entry-level sentence starters. Focus on Lessons 1–3. Use structured comparison tables (Western tech values vs. Māori values) before asking students to write independently.
- On-level: Complete the full 5-lesson sequence. Produce an AI ethics analysis (Lessons 2–3) and a cultural design proposal (Lessons 4–5). Include feedback cycles with peers before final submission.
- Extension: Research a specific case of algorithmic injustice affecting Indigenous communities (e.g. biased facial recognition, health-risk algorithms, welfare prediction tools). Write a submission-style report addressed to a government agency or tech company, using Te Tiriti obligations as the framework for critique.
Inclusion Guidance
- ELL / ESOL: Pre-teach key digital technology vocabulary (algorithm, bias, data, sovereignty). Allow students to discuss concepts in home language before writing in English. Many students from Pacific and Asian backgrounds will have rich perspectives on cultural technology tensions — create space for this.
- Neurodiversity: Use accessible formats with clear headings and visual supports. Neurodiverse learners benefit from structured ethical frameworks (e.g. decision trees, weighted criteria matrices) to navigate complex AI ethics scenarios. Break the design project into weekly milestones.
- Mātauranga Māori lens: Connect AI ethics to tikanga Māori values — particularly kaitiakitanga of data (who owns and controls information about Māori communities) and the principle of manaakitanga in how technologies should serve people equitably. Te Hiku Media's voice recognition work is an outstanding NZ example.
Audited Technology statements — Phase 4
Digital content and data must be created, stored, and shared ethically and legally, respecting privacy, attribution, intellectual property, and local cultural considerations.
Technology · Phase 4
Technological outcomes can raise ethical and legal issues such as privacy, data security, bias, and fairness, which affect individuals and society.
Technology · Phase 4
Key Competencies
- Thinking: Critical analysis of AI systems requires students to move from "does it work?" to "who does it work for, and who does it harm?" — a fundamentally different kind of thinking
- Using Language, Symbols & Texts: AI documentation, data sovereignty frameworks (CARE Principles), technical design briefs, and government submissions are all texts this unit teaches students to read and produce
- Relating to Others: Culturally-responsive design requires genuinely understanding community needs — not projecting assumptions onto them
- Participating & Contributing: The design proposal asks students to address a real community need and propose a real governance model for the technology they design
- Lesson 1: AI Foundations — How LLMs Work & What They Assume Weeks 1–2
- Lesson 2: Ethics & Bias — Whose Values Are Encoded? Weeks 3–4
- Lesson 3: Māori Data Sovereignty Week 5
- Lesson 4: Cultural Design — Embedding Tikanga Week 6
- Lesson 5: Digital Futures — Māori Sovereignty in 2050 Weeks 7–8
| Weeks | Lesson | Big Question | Key Activity |
|---|---|---|---|
| 1–2 | AI Foundations | "He aha te atamai mīharo?" | Introduction to Large Language Models and how AI systems work — and what assumptions they make. Students audit familiar AI tools (Google Search, Spotify recommendations, ChatGPT) for embedded values and cultural blindspots. |
| 3–4 | Ethics & Bias | "Mā wai ērā uara?" | Critical analysis of AI bias — facial recognition failures, biased hiring algorithms, health prediction disparities. Students evaluate specific cases affecting Māori and Pacific communities and use the "who benefits, who decides" framework. |
| 5 | Data Sovereignty | "Ko wai ngā rangatira o ērā raraunga?" | The CARE Principles (Collective benefit, Authority to control, Responsibility, Ethics). Te Hiku Media's Pūoro Kōrero voice recognition project as a case study in Māori-controlled AI. Māori health data governance and what tino rangatiratanga looks like in a database. |
| 6 | Cultural Design | "He aha te manaakitanga matihiko?" | Culturally-responsive design for Māori communities. How do you build manaakitanga into an interface? What does kaitiakitanga look like in a database? Students begin designing a technology prototype with explicit cultural requirements embedded in its architecture. |
| 7–8 | Digital Futures | "Ko tōna āhua ake, he aha?" | Envisioning Māori digital sovereignty in 2050. Students present their design proposals and reflect on what it would take to build a genuinely decolonised digital future — not just diverse tech, but different tech, with different governance. |
AI Ethics Analysis
A critical analysis of one specific AI system or algorithm that affects Māori or Pacific communities. Students identify the bias mechanism, trace its real-world impact, and evaluate it against both Western ethics frameworks and tikanga Māori values. 600–900 words or equivalent multimedia. Sources must include at least one academic or official document (research paper, government report, or CARE Principles documentation).
Cultural Technology Design Proposal
A design proposal for a technology that serves a specific Māori community need, with explicit cultural requirements embedded in its architecture (not just its interface). Must include: problem statement, community context, design principles drawn from tikanga, prototype or wireframe, and governance model specifying who controls data and decisions. Presented to the class as a design critique.
Te Hiku Media is the anchor example for this unit: Their Pūoro Kōrero project trained a te reo Māori voice recognition model using only community-consented data, controlled entirely by the iwi. Google offered to help — and was refused, because the data sovereignty model mattered more than the speed of the outcome. This is the most important case study in the unit: it shows students what principled digital sovereignty looks like in practice. RNZ and Te Hiku Media's own website have good accessible material.
The design proposal (Lessons 4–5) needs to embed tikanga, not just reference it: The most common mistake is students who write "this app will respect manaakitanga" without explaining how the data governance model, the interface decisions, or the community engagement process actually enacts that value. Push students to be specific: what does it mean that the data is collectively owned? Who decides what the model is trained on? What happens to the data if the company is sold?
AI in the classroom: Many students will use AI tools to help with this unit — that's fine and worth acknowledging openly. Ask them: did the AI understand the te ao Māori context? Where did it get it wrong? Turning that interrogation into learning is exactly what the unit is for.
Facial recognition bias resources: The MIT study showing 34.7% error rate for dark-skinned women (vs. 0.8% for light-skinned men) is freely available and widely cited. Joy Buolamwini's TED talk "How I'm Fighting Bias in Algorithms" is an accessible 9-minute entry point for Lessons 1–2.