Unit 7: Digital Technologies & AI Ethics
Navigating the Future of Artificial Intelligence
This senior-level unit provides a critical and practical introduction to Large Language Models (LLMs) and Artificial Intelligence through integrated collaboration, combining technological literacy with Māori Data Sovereignty, ethical reasoning, and community-centred innovation.
Deliberate, reason, reflect, and act
This edition foregrounds explicit ethics teaching, structured kōrero, evidence-based deliberation, and personal reflection. Ākonga build a defensible ethical position before committing to community action.
- Approach: Critical literacy seminar
- Duration: Five 75-minute lessons
- Scaffolding: Discussion protocols, ethics frameworks, and reflection prompts
- Outcome: A personal ethics framework and community action commitment
Whakatūwhera - Unit Opening
As we stand at the dawn of the AI age, we must approach these powerful technologies with both wisdom and caution. This unit grounds AI learning in mātauranga Māori principles, ensuring that technology serves our communities while protecting what is sacred.
"Mā te huruhuru ka rere ai te manu" - It is the feathers that allow the bird to fly. Knowledge and ethics are our feathers in the digital realm.
📋 Kaiako Planning Snapshot
Ngā Whāinga Akoranga — Learning Intentions
- Ākonga will understand how AI systems work at a conceptual level — including data, algorithms, training, and bias.
- Ākonga will critically analyse the ethical implications of AI, including surveillance, privacy, algorithmic bias, and tino rangatiratanga.
- Ākonga will apply mātauranga Māori values — kaitiakitanga, manaakitanga, and whanaungatanga — to evaluate and design responsible digital technologies.
- Ākonga will take an informed position on a real AI ethics issue and communicate it persuasively.
Paearu Angitu — Success Criteria
- I can explain how AI learns from data and why bias in training data matters.
- I can identify at least two real-world AI ethics dilemmas and explain the harms and benefits.
- I can connect a Māori value (e.g., kaitiakitanga) to a specific AI design decision.
- I can argue a position on an AI ethics issue using evidence and ethical reasoning.
Entry / On-Level / Extension
- Entry: Guided case study with structured questions; visual explainers of AI concepts; ethics frameworks provided as scaffolds.
- On-level: Independent case study analysis; structured debate or persuasive essay; AI design challenge with ethical constraints.
- Extension: Research a real AI system and write a policy recommendation; create an AI ethics charter for your school community.
Inclusion Guidance
- ESOL / ELL learners: Glossaries for AI and ethics terminology. Use real-world examples close to students' experience. Oral discussion before written output.
- Neurodiverse learners / ADHD: Break the ethics case study into clear steps. UDL principle: visual flowcharts for AI processes; choice between written, oral, or multimedia presentation.
- Dyslexia: Audio-text versions of readings; annotated diagrams instead of dense text; voice recording accepted for analysis tasks.
Ngā Akoranga - Lesson Sequence
AI Foundations
Introduction to Large Language Models and foundational understanding of AI systems.
Ethics & Bias
Critical analysis of AI bias and ethical frameworks for responsible technology use.
Data Sovereignty
Exploring data ownership from a Te Ao Māori perspective and Indigenous knowledge protection.
🎯 Curriculum Links | Te Hononga ki te Marautanga
Phase 4 | English — Language Studies
"Ethical use of media and digital texts involves respecting intellectual property, recognising bias, representing diverse perspectives, and participating responsibly in online environments."
Phase 4 | Technology — Design, Make, and Innovate
"Smart systems in wearable technologies provide positive opportunities for innovation and raise ethical and legal considerations around data use, privacy, and responsible design."
Key Competencies
- Thinking: Students evaluate real AI systems critically — not as neutral tools but as designed artefacts encoding particular values and interests
- Participating & Contributing: The ethics position paper and design challenge ask students to engage as citizens with a stake in the AI decisions being made around them
- Relating to Others: Understanding whose communities are harmed by algorithmic bias builds empathy grounded in evidence, not sentiment
📊 Assessment Framework
Formative Assessment
- After Lesson 1 — Exit ticket: "In one sentence, explain why the data used to train an AI matters." Checks conceptual understanding before the bias analysis in L2.
- After Lesson 2 — Case study check: Student names a specific AI bias, explains who is harmed and how, and identifies who made the design decision. Observable against LI 2.
- Before Lesson 3 discussion — Position commit: Students write their position on their chosen AI issue (3 sentences) before the full kōrero. Locks in their reasoning so the discussion doesn't just anchor to the first confident voice.
Summative Assessment — AI Ethics Position Paper
300–500 word argument on one real AI ethics issue (facial recognition in policing, AI in healthcare, generative AI and creative rights, algorithmic hiring, social media content moderation, or surveillance capitalism). Must include: a clear claim, at least two pieces of evidence, one acknowledged counterargument, and application of at least one Māori value (kaitiakitanga, manaakitanga, or whanaungatanga) to the analysis.
| Criterion | Emerging | Developing | Proficient | Advanced |
|---|---|---|---|---|
| Claim & Position | Opinion stated but not argued | Position clear; some reasoning | Specific, defensible claim with structured argument | Nuanced claim that acknowledges complexity; limits of own position stated |
| Evidence Quality | General claims; no sources | One cited example | Two credible examples; relevance explained | Multiple sources; evidence weighed, not just stacked |
| Ethical Reasoning | "This is wrong/right" with no framework | Uses one ethical lens; applies it partially | Applies one framework consistently; counterargument acknowledged | Two frameworks compared; own position explicitly reasoned, not assumed |
| Tikanga Application | Māori value named but not applied | Value applied generally to the topic | Value applied to a specific design decision or policy | Value used to identify what a responsible alternative would look like |
🚀 Extension Activities | Ngā Mahi Torohī
Entry — AI Bias Diary
Over one week, students notice and record two moments when AI affects their daily life (recommendations, search results, autocorrect, filters, content moderation). For each: name the AI system, describe what it did, and write one sentence about who benefits and who might be harmed. Low stakes, high noticing — builds the observational habit before the analytical work.
Developing — Counter-Design Brief
Students identify a real AI product with a documented bias problem (facial recognition, hiring tools, content recommender, predictive policing). They write a 400-word counter-design brief: what specific change to the training data, design process, or governance would make it more equitable? The brief must name one Māori value and explain concretely how it would change the product.
Mastery — Community Consultation Report
Students design and conduct a two-question survey (three or more participants from their whānau or community) asking what AI they already interact with and what concerns they have. They analyse the results and write a one-page "Community AI Concerns Report" addressed to a named decision-maker (school board, local council, tech company). The report proposes one specific policy change grounded in the consultation findings and Māori data sovereignty principles.
🔗 Unit Progression & Next Steps
Learning Arc: Students move from encounter (what is AI, through a Māori lens?) → critical analysis (who is harmed by bias?) → reasoned positioning (what do I actually think, and why?) → creation (what would a responsible alternative look like?) → commitment (what kind of digital future do I want to help build?). The unit is not a technology skills course — it is an ethics and citizenship course about the most consequential technology of students' lifetimes.
Lesson Sequence
- 📖 Lesson 1: AI Through a Te Ao Māori Lens — Students encounter AI not as a neutral tool but as a designed artefact encoding values. The te ao Māori frame asks: who decides what gets built, whose knowledge counts, and what is made invisible? Foundational concepts (data, training, inference, bias) are introduced in service of this critical question.
- 📖 Lesson 2: AI Bias & Algorithmic Justice — Students analyse documented cases of AI bias specifically affecting Māori, Pacific, and non-white communities — facial recognition failure rates, hiring algorithm discrimination, content moderation that removes indigenous language content. Ethical vocabulary is built: harm, accountability, transparency, fairness.
- 📖 Lesson 3: AI Ethics in Practice — Students apply three frameworks to a single live AI dilemma: consequentialism (who benefits, who is harmed?), virtue ethics (what would a person of integrity do?), and tikanga Māori (does this embody kaitiakitanga, manaakitanga?). They commit to a reasoned position before the full-class kōrero.
- 📖 Lesson 4: Culturally Responsive Design — Students shift from critique to creation. Applying kaitiakitanga (responsible stewardship of data), manaakitanga (technology that uplifts rather than surveils), and whanaungatanga (design that strengthens relationships), they design an AI feature that genuinely serves a Māori or Pacific community need — with authentic constraints, not cultural decoration.
- 📖 Lesson 5: Māori Digital Futures — Students present their ethics positions and design proposals. The unit closes with a personal AI ethics charter: what kind of digital future do I want to help build, and what will I do differently because of what I now know? Students leave with a framework they own, not borrowed conclusions.
Beyond the unit: Students are already using AI systems daily. The skills practised here — ethical analysis, evidence-based reasoning, value-grounded design, and comfort with uncertainty — apply to every subsequent engagement with technology. This unit is not a capstone; it is a foundation.