How to use
Read the relevant week's section before the lesson. Use the Socratic prompts to deepen discussion rather than moving to answers quickly. The misconception notes help you anticipate and redirect without dismissing students.
Kaiako resource • Unit 7 AI Literacy • Weeks 1–5 • Discussion facilitation
Teacher Discussion Guide: Unit 7 AI Literacy
This is a kaiako-facing resource. It supports teachers in facilitating rich, culturally safe, and intellectually challenging discussions about AI, digital ethics, data sovereignty, and the intersection of mātauranga Māori with digital technology across the five weeks of Unit 7.
Free discussion guide, premium lesson adaptation
Use this guide as your facilitation base. Open Te Wānanga if you want a custom discussion prompt set, a bilingual version of key questions, or an adapted version for a specific year level or class context.
Ngā Whāinga Akoranga / Learning Intentions
- Kaiako are learning to facilitate structured discussions about AI ethics that are culturally safe and intellectually rigorous.
- Kaiako are learning to identify and respond to common student misconceptions about AI without shutting down inquiry.
- Kaiako are learning to centre Māori and Pacific perspectives in AI discussions without placing burden on students from those communities.
Paearu Angitu / Success Criteria
- I have reviewed the weekly discussion prompts before each lesson and selected the most appropriate ones for my class.
- I can name at least two common AI misconceptions and have a prepared response strategy for each.
- I can facilitate a discussion that surfaces Māori perspectives without relying on Māori students to speak for their culture.
Kaiako planning snapshot
- Best timing: Read the week-by-week section the evening before each lesson. The misconception notes are particularly important to review in advance.
- Grouping: Whole-class Socratic discussion works best in Weeks 1–2. By Weeks 3–5, small-group fishbowl discussions give more ākonga air time.
- Prep for Māori perspectives: Familiarise yourself with Te Hiku Media's Papa Reo project and Te Mana Raraunga (temanararaunga.maori.nz) — having specific examples ready avoids generalisation.
- Differentiation: Scaffold entry-level students with concrete examples first. Extension students benefit from being asked to argue the opposite position.
- Pastoral note: If recent incidents involving racism or digital harm have occurred in your school, consider a briefer discussion format and consult your dean before proceeding with Weeks 2–3 content.
Week 1 — What is AI? How does it actually work?
Focus: Building accurate mental models. Most students arrive with either sci-fi robot images or complete mystery. The goal is concrete understanding before ethics.
Key discussion questions
- If an AI makes a mistake, who is responsible — the AI, the programmer, the company, or the person who used it?
- What is the difference between a calculator and an AI? (Probe: can a calculator learn? Can it make predictions it was not specifically programmed to make?)
- Where have you already used AI today without realising it?
Socratic depth prompts
- "What would you need to see to change your mind about that?"
- "Can someone build on what [student] said — or push back on it?"
- "If that is true, what follows from it? What would we need to do differently?"
Week 2 — AI Bias: who does AI work well for, and who does it fail?
Focus: Moving from "AI is objective" to "AI reflects its training data and the choices of its designers." Concrete examples are essential before abstract discussion.
Key discussion questions
- If an AI tool works less accurately for Māori faces or Māori names, is that the AI's fault, or is it a design choice? What is the difference?
- Who decides what "normal" looks like when training data is collected? What groups are likely to be over-represented? Why?
- Should an AI tool that produces biased outputs be banned, fixed, or disclosed? What are the trade-offs in each approach?
Māori perspectives to bring in
- Te Hiku Media's Papa Reo — a Māori-owned, Māori-controlled AI speech model trained exclusively on te reo Māori data. Contrast with commercial voice AI that performs poorly on te reo.
- Te Mana Raraunga — the Māori Data Sovereignty Network, which argues Māori data should be governed by Māori institutions, not extracted by corporations.
- Ask: "What does mana tell us about who should control data about Māori communities?"
Common misconceptions
- "AI is just a computer — it can not be racist." Response: bias in AI is structural, not intentional. If training data reflects historical inequalities, the model will reproduce those inequalities at scale, regardless of programmer intent.
- "We should just make AI colour-blind." Response: removing demographic information can hide bias rather than fix it — and prevents targeted testing. Equity requires being able to see disparities.
Week 3 — Digital Ethics: what should AI be allowed to do?
Focus: Moving from technical facts to values-based reasoning. Students apply ethical frameworks to real AI scenarios. Avoid letting discussion collapse into "AI is good" vs "AI is bad" — push for nuanced position-taking.
Key discussion questions
- Should AI be used to make decisions about bail, job applications, or school admissions? What safeguards would make that acceptable — or is there no acceptable version?
- If an AI company has the technical ability to read all your messages, should "can" equal "should"? Who decides?
- What ethical obligations do AI developers have to communities affected by their technology — even communities they have never met?
Māori perspectives to bring in
- Kaitiakitanga applied to data: just as kaitiaki of a river are responsible for its health, those who hold data about a community carry obligations to that community — not just to the user of the data.
- Mana whenua and data sovereignty: who has the right to collect and use data about Māori land, language, genealogy, or health?
- Ask: "If a company uses data about te reo Māori to train an AI without asking Māori communities, is that a breach of Te Tiriti? Why or why not?"
Week 4 — Privacy, Surveillance, and Data
Focus: Making privacy tangible. Students often think privacy is only about "hiding bad things." The goal is understanding privacy as a precondition for autonomy, dignity, and self-determination.
Key discussion questions
- "If you have nothing to hide, you have nothing to fear." What is wrong with this argument? Who benefits from people believing it?
- When your school uses an AI tool that records student data, who owns that data? Who should?
- Should governments be allowed to use AI surveillance in public spaces? What would your answer depend on?
Managing sensitive discussions
- AI replacing jobs: Frame as "what should we do about this?" rather than "will this happen?" The question is governance, not prediction. Avoid fatalism.
- Surveillance: Some students (especially those with family experience of state surveillance) may have strong responses. Acknowledge that surveillance harms are not equally distributed.
- Personal data: Some students may feel exposed or anxious when they realise how much data they generate. Redirect to action: "What can we do? What choices do we actually have?"
Week 5 — AI and the Future: mātauranga Māori meets digital technology
Focus: Synthesis and vision. Students move from critique to possibility — what does ethical AI look like? What does AI that serves Māori values look like? How can ākonga themselves shape digital futures?
Key discussion questions
- If you were on the team designing an AI tool for Aotearoa, what values would you require it to reflect? How would you test whether it reflected them?
- Te Hiku Media built Papa Reo — an AI trained on te reo by Māori, governed by Māori. What made that different from a commercial company building the same thing? Why does ownership matter?
- Is it possible to build AI that genuinely reflects tikanga Māori values? What would that require? What would it prevent?
Closing synthesis prompt
Ask students to complete this sentence: "Before this unit I thought AI was ___. Now I think ___. The most important thing I want to do with this knowledge is ___." This makes learning visible and orients ākonga toward action — the core purpose of Phase 4 of the guided inquiry.
Common student misconceptions — quick reference
| Misconception | Why students hold it | How to redirect (not dismiss) |
|---|---|---|
| AI is neutral and objective | AI feels mathematical and therefore impartial | Show that data reflects human choices — what was counted, by whom, when |
| AI is sentient or "understands" things | Anthropomorphism; media portrayals of AI | Explain pattern-matching vs comprehension; ask what "understanding" actually requires |
| Bias only matters if someone intended it | Fairness is seen as about intent, not outcome | Use the structural racism analogy: systems can produce unequal outcomes without any individual being malicious |
| Focusing on AI harms is just being negative about technology | Pro-technology culture; AI enthusiasm in media | Reframe: understanding problems is how we build better tools. Critical thinking is a sign of engagement, not rejection |
| Privacy only matters if you are doing something wrong | Common cultural myth; often modelled by adults | Privacy is a precondition for autonomy. Ask: would you be comfortable if everyone could read your messages right now? Why not? |
| AI replacing jobs is inevitable and there is nothing we can do | Fatalism; technology presented as ungovernable force | Redirect to policy and collective choice — societies have always made decisions about which technologies to adopt, regulate, or ban |
When discussions become heated — quick guide
If a student makes a harmful or racist comment
- Interrupt calmly and immediately: "Let me pause us there."
- Name the harm specifically without attacking the person.
- Restate the class values: we critique systems, not people.
- Follow up privately after class; check in with affected students.
If discussion escalates into argument
- Pause: "Strong feelings show this matters. Let us think carefully."
- Return to evidence: "What does the research actually show?"
- Structural move: go to one-at-a-time ground rule, or write then share.
- If needed, park the topic and return the next day with more structure.
If you do not know the answer
- Say so: "I do not know — let us find out together."
- Never speak for Māori perspectives you do not have authority over.
- Offer to bring a guest speaker or share a documented expert voice.
Supporting Māori and Pasifika students
- Never require Māori students to represent all Māori views.
- Provide resources where Māori voices are already documented.
- Check in privately: "How are you finding this unit? What do you need?"
- Celebrate Indigenous tech excellence — Te Hiku Media, Te Mana Raraunga.
Hononga Marautanga / Curriculum Alignment
This guide supports the Digital Technologies and Social Sciences learning areas. The discussion frameworks align with NZ Curriculum principles around inclusion, diversity, and critical thinking. Kaiako are expected to ground AI ethics discussions in tikanga Māori values — particularly manaakitanga (care for wellbeing), whanaungatanga (relationships), and kaitiakitanga (guardianship). The Socratic discussion approach builds the Key Competencies Thinking and Relating to Others. The Te Mātaiaho curriculum refresh emphasises culturally responsive pedagogy and student agency — both are foregrounded in this unit.
Aronga Mātauranga Māori
In mātauranga Māori, knowledge comes with relational responsibility. When a kaiako facilitates discussion about how AI affects Māori communities, they are entering relational territory — they are not only a neutral information provider but a participant in a conversation that matters to people in the room. Whakaaro (careful thinking) and tika (correctness, doing right) require the kaiako to prepare well, speak carefully, and make space for voices that know more than they do. This guide is designed to help kaiako do that: prepare well, anticipate problems, centre the right voices, and create a space where ākonga feel safe to engage with genuinely difficult ideas about digital power, data sovereignty, and AI futures in Aotearoa.
Ngā Ākonga Katoa — Inclusion and Accessibility
These AI ethics topics involve emotionally sensitive material. Universal Design for Learning (UDL) principles suggest offering multiple means of engagement:
- Neurodiverse learners (ADHD, dyslexia, autism): Provide a written summary of key discussion questions before the lesson; allow movement breaks; offer the option to respond in writing rather than verbally; avoid cold-calling on sensitive topics. For students with dyslexia, ensure any printed material uses a readable font at 12pt minimum.
- ESOL / ELL students: Pre-teach key vocabulary (bias, algorithm, sovereignty, data, ethics) with definitions in plain English; pair with a discussion buddy; allow extra processing time before asking for contributions.
- Accessibility: Ensure any video content has captions enabled; provide printed handouts as an alternative to on-screen content; check that the classroom seating arrangement allows all students to participate in discussion without physical barriers.
- Executive function support: Break complex discussion questions into smaller parts; give thinking time (2–3 minutes silent reflection) before discussion opens.
- Emotional safety: Any student who finds the content distressing may step out without penalty — arrange a check-in protocol with the school counsellor if needed.
Resources already provided / Ngā Rauemi Hono — Related Unit 7 Kaiako Resources
- AI Bias Detection Lab (student handout) — use in Week 2
- AI News Analysis (student handout) — use in Weeks 2–3
- AI Tech Crossword — vocabulary consolidation, Week 1 or review
- Guided Inquiry Phase 3 — student analysis scaffold, Weeks 3–4
- Guided Inquiry Phase 4 — student conclusion scaffold, Week 5
- AI Cultural Bias Testing Protocol — structured cultural testing, Week 2
- Te Mana Raraunga — Māori Data Sovereignty Network (external)
Facilitation pathways
Tīmata: Use one or two prepared questions per week. Rely on the misconception
notes. Keep discussions shorter and more structured (think-pair-share before open forum).
Paerewa: Use the full set of discussion questions. Open the floor after
initial pair/small-group discussion. Draw in documented Māori voices via resources provided.
Tūāpae: Facilitate fishbowl discussions, invite guest speakers, and give
students ownership of designing and facilitating Week 5 discussion themselves.
Kōrero Āpiti · Kaiako Notes
Kia mōhio · Facilitator reflection after discussion:
Curriculum alignment
- Digital Technologies — Progress Outcome: Students understand how digital systems store, represent, and transmit data and consider the ethical implications of digital technologies on society.
- Digital Technologies — Progress Outcome: Students can identify and describe the key components of digital systems and how they interact.