Phase position
Phase 3 of 4. You should have completed Phase 1 (question and hypothesis) and Phase 2 (data collection) before starting this sheet.
Unit 7 AI Literacy • Years 8–11 • Guided inquiry • Phase 3 of 4 • Print-ready
Guided Inquiry Phase 3: Analysis and Evidence
Phase 3 is where your inquiry gets serious. You have gathered information — now you analyse it critically, test the quality of each source, detect possible bias, and begin building a supported argument. This scaffold will take you from a pile of notes to a clear, evidence-backed position.
Free analysis scaffold, premium argument builder
Use this sheet for the core analysis. Open Te Wānanga if you want a co-created argument plan tailored to your specific topic, audience, or assessment format.
Ngā Whāinga Akoranga / Learning Intentions
- We are learning to evaluate the credibility and bias of information sources.
- We are learning to construct a supported argument using multiple pieces of evidence.
- We are learning to identify counterarguments and respond to them using reasoning.
Paearu Angitu / Success Criteria
- I have evaluated at least four sources for credibility, type, and possible bias.
- I have identified at least three pieces of evidence that support a clear claim.
- I have written a counterargument and a response to it.
My inquiry topic (from Phase 1)
My inquiry question
My original hypothesis
1. Source evaluation table
For each source you collected in Phase 2, fill in the row. Credibility rating: 1 = low, 2 = moderate, 3 = high.
| Source name | Type (AI tool / news / report / book) | Credibility rating (1–3) | Why you rated it this way | What it tells you about your topic |
|---|---|---|---|---|
2. Bias detection checklist
Review your sources. For each indicator below, note which of your sources shows this pattern (if any).
| Bias indicator to look for | Which source? (or "none found") | How does this affect what you trust? |
|---|---|---|
| Only one type of person or group is quoted or mentioned | ||
| The source has a commercial interest in how AI is seen | ||
| Evidence from affected communities (e.g., Māori, Pasifika) is missing | ||
| The source was produced by the same company whose technology is being discussed | ||
| The language is very emotive — either very positive or very alarming |
3. Argument construction scaffold
A strong argument has four parts: a clear claim, evidence that supports it, a counterargument (what someone might say against you), and your response to the counterargument. Work through each part below.
My claim (one clear sentence that answers my inquiry question)
Evidence point 1
Source:
Evidence point 2
Source:
Evidence point 3
Source:
Counterargument (what someone disagreeing with me might say)
My response to the counterargument
4. Te ao Māori lens on your analysis
Wānanga is a traditional model of collective knowledge evaluation. In wānanga, knowledge is not accepted simply because one person says it is true — it is tested through community discussion, questioning, and the lived experience of those present. Apply this idea to your inquiry: whose voices are missing from your sources? Would those voices change your argument?
Which communities are most affected by this AI issue in Aotearoa?
Are those communities represented in the sources you found?
If not, how does that gap affect how confident you can be in your argument?
Apply one tikanga value (e.g., tika, pono, manaakitanga) to your analysis
What would this value ask you to check or change about your argument or the sources you are using?
Hononga Marautanga / Curriculum Alignment
This scaffold aligns with the Digital Technologies learning area — specifically the strands on designing and developing digital outcomes and understanding ethical implications of AI. It also connects to the Social Sciences inquiry process: students gather, analyse, and evaluate information to build supported conclusions. The source evaluation and argument-construction components directly practise the NZ Curriculum Key Competency Thinking — forming and defending reasoned positions.
Aronga Mātauranga Māori
The concept of wānanga as a model of collective knowledge evaluation is central to this phase. In traditional wānanga settings, knowledge was not validated by a single authority — it was tested through the community's collective reasoning, experience, and values. This contrasts with Western academic traditions that may privilege certain types of sources (peer-reviewed journals, government data) while invisibilising community and Indigenous knowledge. Encouraging students to ask whose knowledge is being counted — and whose is being missed — is a mātauranga Māori practice of epistemological accountability. In AI contexts, this matters because the data used to train AI systems often reflects dominant cultural assumptions while erasing Indigenous perspectives and contexts.
Resources already provided / Ngā Rauemi Hono
What to print: one copy per student. Students keep this alongside their Phase 2 notes. All referenced resources are provided as separate handouts — see links below.
- Guided Inquiry Phase 4 — conclusion and action scaffold (next step)
- AI Bias Detection Lab — structured bias testing to strengthen evidence
- AI News Analysis — source evaluation practice
- AI Ethics and Bias — conceptual support
- Teacher Discussion Guide — facilitation support for the analysis discussion
Pathways through Phase 3
Tīmata: Complete the source evaluation table for two sources and fill in the
claim and two evidence points.
Paerewa: Complete all sections including the bias checklist and
counterargument.
Tūāpae: Complete all sections with depth, add a second te ao Māori value, and
identify one additional source that would strengthen your argument further.
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.