Students find a favourite outdoor spot on the school grounds, or at the visit site, and sit there quietly. While there, they draw the space and note everything they can see, smell and hear, and how the space makes them feel.
Back in class, or under a tree nearby, a few students share what they drew and noted. Common threads reliably emerge: a favourite tree, garden or patch of flowers, and a shared feeling of calm or escape from noise.
From there, open a short discussion: why do spaces like these matter, and whose responsibility is it to protect them? This is where students first connect physical sense with emotion, ahead of any formal science.
Choose the method that matches your site. For a stream, the full kick sampling and macroinvertebrate identification technique is covered in the Stream Macroinvertebrates protocol, use it directly as this block's technique. For lakes, rivers, estuaries and wetlands, the facilitator brings the water sampling and ecological survey method suited to that site.
Encourage students to get into the water where it is safe to do so. Wading in, looking under rocks and handling specimens builds curiosity that observing from the bank does not.
Weave in the wider learning as it happens: tallying and measurement, report writing, and communicating findings to the group.
Give each group a large piece of cardboard, a hot glue gun and one rule: only natural materials collected from the ground (dead leaves, grass, flowers, sticks) may be used to build a model of the water body and its surrounds.
What emerges is what students learned, made physical. Riparian buffer zones along stream edges, fish passages through culverts, farm fences set well back from wetlands. Many groups also rebuild their favourite spot from Block One into the model.
Each group presents their model to the class, explaining the features they included and why.
Three layers of primary experience already happened outside: sensing, investigating, building. Here AI's job is to sharpen the thinking behind those layers, not to replace any of them. Prompts draw directly on students' Block One notes, Block Two data and Block Three models.
Describe your favourite outdoor space to a gen AI chatbot exactly as you noted it. Ask it to turn your notes into a short paragraph. Does it capture the place the way you remember it? What did it miss that only you would know?
Ask a gen AI chatbot why places like your favourite spot matter, and why people might want to protect them. Compare its answer with what your class discussed. What did your class say that the AI did not?
Ask a gen AI chatbot to explain in simple words what a riparian buffer is and why a farmer might leave one. Use the answer to help decide what to build into your model.
Tell a gen AI chatbot what your model shows. Ask it to suggest one question a visitor might ask about your model, then practise your answer.
Take your Block One notes about how your outdoor space made you feel. Ask a gen AI chatbot to connect that feeling to an ecological or wellbeing concept, such as sense of place. Does the concept match what you actually felt, or does it flatten it?
For each stewardship feature in your model, ask a gen AI chatbot what problem it solves and what happens if it is left out. Check the answer against what you observed in Block Two.
Ask a gen AI chatbot how the science method your class used would need to change at a different type of water body, lake versus stream versus estuary. Which of its suggestions hold up for New Zealand conditions?
Ask a gen AI chatbot to role play a sceptical visitor asking why your group's model matters. Practise your response, then note one question it asked that you had not considered.
Choose one stewardship feature in your model. Ask a gen AI chatbot to summarise the evidence for its effectiveness, such as buffer width and pollutant reduction. Locate one primary source, NIWA, DOC or your regional council, that supports or challenges the summary.
Ask a gen AI chatbot to describe what a student might feel sitting in a favourite outdoor space. Compare its answer with your own Block One notes. Write a short reflection on what an AI generated description can and cannot substitute for.
Ask a gen AI chatbot to outline the costs and benefits of your model's stewardship feature, from a landowner's perspective as well as an ecological one. Evaluate whether its analysis is balanced.
Using this protocol as a model, ask a gen AI chatbot to help draft a three block day for a different environment, forest, coastline or urban stream. Critique its draft against what actually made this day work.
| Level | Years 0–6 | Years 7–10 | Years 11–13 |
|---|---|---|---|
| 1 | Student names at least one thing they saw, smelled or heard in their favourite outdoor space, and can point to it in their drawing. | Student records sensory observations and one clear feeling associated with the space, using specific detail rather than general description. | Student produces a structured sensory and emotional account of the space that could stand as a baseline observation for a place based study. |
| 2 | Student says why their space matters to them and gives one reason it might need protecting. | Student explains the link between their sensory experience and the ecological or wellbeing value of the space, using at least one concept from class. | Student argues, with reasoning, for why sense of place strengthens or does not strengthen environmental stewardship, drawing on their own data. |
| 3 | Student compares what they wrote about their space with what a gen AI chatbot wrote, and says in simple terms what was different. | Student documents where an AI generated account of feeling or place matched their own experience and where it did not, and explains why. | Student critically evaluates the limits of AI generated affective or place based content against their own primary sensory data. |
| 4 | Student explains what sitting at the real spot gave them that a photo or description could not. | Student articulates what direct sensory and field experience contributes to scientific and stewardship reasoning that secondary sources or AI cannot replicate. | Student reflects on the difference between lived field experience, collected data and AI generated synthesis as forms of evidence in an environmental context. |
| 5 | Student builds at least one stewardship feature into their model and can explain in one sentence what it is for. | Student's model includes several stewardship features, each justified by evidence from Block Two, and proposes one action their own school could take. | Student designs a stewardship proposal, grounded in field data and evidence, suitable for presenting to a real audience such as a school board, council or landowner. |