Assessment tracks progress against this unit's Learning Intentions and Success Criteria (above) and culminates in two authentic capstones — evidence is gathered during the inquiry, not only at the end.

Formative — during the unit (Lessons 1–3). Each lesson carries its own Success Criteria; use them as checkpoints as ākonga work:

• Lesson 1 — understanding what AI is and what it is not: can ākonga recognise both opportunities and threats AI presents, and articulate a te ao Māori perspective on data as a taonga?
• Lesson 2 — the bias case studies (facial recognition error rates, health-risk algorithms): can ākonga identify how bias enters AI systems and explain its disproportionate impact on Māori and marginalised communities?
• Lesson 3 — the ethics framework: can ākonga articulate personal AI ethics principles and apply them to complex real-world scenarios?

Summative — the capstones (Lessons 2–5). Mastery is shown by what ākonga build, not a separate test:

• The AI Ethics Analysis (600–900 words or equivalent multimedia) — a critical analysis of one specific AI system affecting Māori or Pacific communities, tracing the bias mechanism, its real-world impact, and evaluating it against both Western ethics frameworks and tikanga Māori values. Must include at least one academic or official source.
• The Cultural Technology Design Proposal (Lessons 4–5) — a design proposal for technology serving a specific Māori community need, with cultural requirements embedded in its architecture (not just its interface). Includes problem statement, community context, tikanga-based design principles, prototype or wireframe, and a governance model specifying who controls data and decisions. Presented as a design critique to the class.

Three pathways of genuine depth — each extends real unit substance rather than adding busy work, and each ends in something a student produces.

• Entry — go deeper on one bias case. Choose one AI system from the Lesson 2 case studies (facial recognition, health-risk algorithms, or welfare prediction tools) and research its specific impact on an Aotearoa NZ community. Write a one-page analysis connecting the technical bias mechanism to the real community harm.
• Developing — connect data sovereignty to a real organisation. Research Te Hiku Media's Pūoro Kōrero project in depth — how they trained a te reo Māori voice model using only community-consented data and refused Google's offer to help. Compare their data governance model to one other Indigenous data sovereignty initiative internationally (e.g., the CARE Principles, First Nations data governance in Canada). Present the comparison as a briefing document.
• Mastery — take it to a real audience. Write a formal submission to a government agency or technology company, using Te Tiriti o Waitangi obligations as the framework for critiquing a specific algorithmic injustice affecting Indigenous communities. Document the response and what would change in the next draft.

The unit moves ākonga from consumer to critic to designer. It opens by making AI visible as a system built on human choices — not neutral technology but encoded values and assumptions, examined through a te ao Māori lens that treats data as taonga (Lesson 1). From understanding, the arc turns to justice: how do AI systems encode bias, who is harmed, and what does algorithmic injustice look like when it lands on Māori and Pacific communities — through facial recognition error rates, health-risk algorithms, and welfare prediction tools (Lesson 2)? Lesson 3 asks ākonga to move from critique to principles: articulating their own AI ethics framework and applying it to complex real-world scenarios. The final two lessons pivot from analysis to creation. Lesson 4 challenges ākonga to design technology that embeds tikanga into its architecture — not as a surface layer but as the governance model (who controls the data, who decides what the model is trained on, what happens if the company is sold). The anchor case study is Te Hiku Media's Pūoro Kōrero project: te reo Māori voice recognition built entirely under iwi data sovereignty. Everything converges in Lesson 5's digital futures vision — ākonga present their design proposals and reflect on what it would take to build a genuinely decolonised digital future.

Where it leads. The Cultural Technology Design Proposal is built to be enacted, not filed. It hands ākonga a concrete model they can carry into NCEA Digital Technologies standards, a following unit on community action, or a real submission to a tech company or government consultation. The unit's real endpoint is a shift in how rangatahi see the digital systems around them — not as given, but as designed, and therefore as designable on different terms.

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

Differentiation & Inclusion

Entry-level learners: Provide worked examples of AI bias scenarios with sentence starters. Focus on Lessons 1–3. Use structured comparison tables (Western tech values vs. Māori values) before asking ākonga to write independently.

ELL / ESOL: Pre-teach key digital technology vocabulary (algorithm, bias, data, sovereignty). Allow ākonga to discuss concepts in home language before writing in English. Many ākonga from Pacific and Asian backgrounds will have rich perspectives on cultural technology tensions — create space for this.

Accelerated learners: Research a specific case of algorithmic injustice affecting Indigenous communities and write a submission-style report addressed to a government agency or tech company, using Te Tiriti obligations as the framework for critique.

Neurodiverse / Inclusion: Use accessible formats with clear headings and visual supports. Structured ethical frameworks (e.g., decision trees, weighted criteria matrices) help navigate complex AI ethics scenarios. Break the design project into weekly milestones.