Best for
Unit 7 introductory lesson — before students use any AI tools. Sets the conceptual and ethical foundation for the rest of the unit.
Digital Technologies & AI Ethics • Unit 7 • Years 9–11 • Concepts
Introduction to Large Language Models
What is an LLM, and how does it actually work? Understanding the mechanics behind AI language tools is the first step toward using them responsibly — and evaluating them critically.
Free concepts handout, premium localisation path
Want this adapted for your school's specific AI tools or local context? Te Wānanga can localise it — adding examples from the digital tools your students actually use and connecting to community digital sovereignty initiatives.
- Add locally relevant examples of AI tools students encounter at home and school.
- Connect to your school's digital citizenship policy and acceptable use agreements.
- Integrate with your kura's approach to te reo Māori and mātauranga Māori in tech.
Kaiako planning snapshot
- Use length: 40–50 minutes. Concept sections (~15 min), paired discussion (~10 min), reflection questions (~20 min).
- Grouping: Read-aloud or silent read for concept sections; reflection questions individual. Brief whole-class discussion at the end.
- Prep: No tech required. If students have used ChatGPT or similar before, that prior experience is useful discussion material — ask them what they noticed about how it responded.
- Differentiation: Entry: complete questions 1 and 2 only. On-level: all four questions. Extension: research one real-world example of LLM bias or data underrepresentation and bring it to the next lesson.
- Neurodiversity support: The process flow (tokenization → processing → generation) is a visual sequence — draw it on the board for students who benefit from visual anchors. Allow verbal responses for reflection questions if preferred.
Resources already provided
- Conceptual explanation of LLM architecture (training data, parameters, prompting)
- Step-by-step process flow: tokenization → processing → generation
- Critical considerations — bias, environment, cultural sensitivity, privacy
- Mātauranga Māori framing: reo as taonga and data sovereignty
- Four structured reflection questions with response spaces
All content is provided. No supplementary materials needed to run this lesson.
Ngā Whāinga Akoranga / Learning Intentions
- We are learning to explain how a Large Language Model works — what it is trained on, how it generates output, and what its limitations are.
- We are learning to evaluate AI tools critically — recognising bias, data gaps, and cultural considerations.
- We are learning to connect digital technology to mātauranga Māori values, including data sovereignty and language as taonga.
Paearu Angitu / Success Criteria
- I can describe what an LLM is and explain the difference between training data, parameters, and prompting.
- I can give at least two reasons why LLM outputs should be evaluated critically.
- I can explain one way that te reo Māori or mātauranga Māori might be underrepresented in AI training data — and why this matters.
Curriculum alignment / Te Marautanga o Aotearoa
This handout develops students' ability to understand digital systems and evaluate their societal impact — connecting to the NZ Curriculum's Digital Technologies strand and the broader values of critical thinking, equity, and cultural identity.
Why this matters in Aotearoa
In Māori worldview, reo is a taonga — a treasure. Language carries culture, identity, and knowledge across generations. AI language models are trained almost entirely on English-language internet data. This means they often cannot handle te reo Māori well, may reproduce colonial framings of Aotearoa's history, and may generate content that misrepresents or flattens mātauranga Māori. Understanding how LLMs work is not just a tech skill — it's a foundation for digital tino rangatiratanga.
He aha te LLM? / What is a Large Language Model?
A Large Language Model is a type of artificial intelligence trained on a massive amount of text data. Its core function is predicting the next most likely word in a sequence — which enables it to generate human-like text, answer questions, translate languages, and much more.
Training Data
LLMs are trained on vast datasets — internet text, books, articles. This is how they "learn" language patterns. But the data may not represent all languages, cultures, or perspectives equally. What voices are missing?
Parameters
Internal variables the model uses to make predictions. Models are measured by parameter count — from millions to hundreds of billions. More parameters does not automatically mean better or more culturally aware output.
Prompting
The input you give — your question or instruction. The clarity and specificity of your prompt significantly affects the quality and reliability of the output you receive.
Te ara haere / How it works — step by step
1
Text Input
You type a question or prompt
2
Tokenization
The model breaks your text into smaller pieces called tokens
3
Processing
The model uses its parameters to understand context and predict likely responses
4
Generation
It predicts and outputs the most statistically likely response — not necessarily the most accurate or culturally aware
Ngā āhuatanga hira / Critical considerations
Bias and fairness
LLMs reflect the biases in their training data. Indigenous knowledge and non-English perspectives are frequently underrepresented.
Environmental impact
Training and running large models requires enormous computational energy. This has a real environmental cost.
Cultural sensitivity
How well do these models understand and respect mātauranga Māori and other indigenous knowledge systems? Often poorly.
Privacy and data
What happens to data you share? Data sovereignty — the right of communities to control their own data — is a live issue for Māori.
Whakaaro hōhonu / Reflection questions
Answer in your own words. Use the spaces below — do not use an AI tool to answer questions about AI tools.
1. How might the training data of an LLM affect its understanding of Māori perspectives and mātauranga Māori?
2. What are two potential benefits and two potential risks of using LLMs in education?
3. In te ao Māori, reo (language) is a taonga. What does this suggest about how we should treat AI-generated text in te reo Māori?
4. What three questions would you ask before trusting information from an AI model?
Entry, on-level, and extension pathway
Entry
Complete questions 1 and 2. Draw or label the 4-step process flow. Discuss your answers with a partner before writing.
On-level
Complete all four reflection questions with specific examples where possible. Aim for 3–4 sentences per answer.
Extension
Research a real documented case of AI bias or indigenous language underrepresentation. Write a paragraph connecting it to the concepts here and bring it to the next lesson.
Hononga Marautanga · Curriculum Alignment
Digital Technologies — Hangarau Matihiko
Level 4–5: Understand how digital systems work; explore how AI models are trained on data; evaluate the implications of AI-generated text for accuracy, bias, and cultural representation.
Social Sciences — Tikanga ā-Iwi
Level 3–4: Understand how technology shapes relationships, power, and identity within communities; evaluate the impacts of digital innovation on society and culture.
Aronga Mātauranga Māori
In te ao Māori, reo is a taonga — language carries culture, whakapapa, and tikanga across generations. Large language models are trained overwhelmingly on English-language data, which means te reo Māori is underrepresented and often mishandled. Understanding how LLMs work is foundational to digital tino rangatiratanga: the right of Māori communities to govern, protect, and define how their language and knowledge appear in AI systems. Kaitiakitanga of language is not a metaphor — it is a live responsibility in the age of AI.
📋 Teacher Planning Snapshot
Ngā Whāinga Ako — Learning Intentions
Students will develop critical digital literacy by examining the ethical dimensions of AI systems, exploring how kaupeka matihiko (digital technologies) reflect and shape our values, and connecting concepts of tino rangatiratanga (self-determination) to digital sovereignty and data rights in Aotearoa.
Ngā Paearu Angitū — Success Criteria
- ✅ I can identify ethical issues within AI systems and explain their real-world impact.
- ✅ I can apply a te ao Māori lens to evaluate digital technologies and their effects on communities.
- ✅ I can articulate what digital sovereignty means and why it matters for tangata whenua.
Differentiation & Inclusion
Scaffold support: Provide worked examples of AI bias scenarios with entry-level sentence starters. Offer extension tasks requiring students to research and present a case study of algorithmic injustice affecting indigenous communities.
ELL / ESOL: Pre-teach key digital technology vocabulary (algorithm, bias, data, sovereignty). Allow students to discuss concepts in home language before writing in English.
Inclusion: Use accessible formats with clear headings and visual supports. Neurodiverse learners benefit from structured ethical frameworks (e.g. decision trees) to navigate complex AI ethics scenarios.
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. Discuss the risks of algorithmic bias replicating colonial harm.
Prior knowledge: Best used after introductory digital technology concepts. No specialist prior knowledge required for entry-level engagement.