Before the visit, look at the site on the map. What do you expect to find there? How large do you think the site is? How far is it from school? What can you already tell from the satellite image?
At the site, notice what the map suggested and what it could not show. What surprises you? What is different from the satellite view? What is missing from the map that is obvious in person?
Back in the classroom, use the distance and area tools to test your predictions. How close were your estimates? What does the measured result tell you that your intuition could not?
Put the map data alongside what you observed in person. What can satellite imagery tell you that a site visit cannot? What can a site visit tell you that satellite imagery cannot?
Open ArcGIS MapMaker and search the address or name of the place you are visiting. Zoom in until the key features of the site are clearly visible. Share the view on a class display before departing.
Ask students to record what they expect to find based on the satellite image alone. Set specific questions:
Before using the measuring tools, ask students to estimate: How far is it from school to the site? How large is the sampling area, the stream reach, or the beach section you will study? Record estimates. These predictions are the data the cycle depends on.
Ask students: what questions do you have about this place that the satellite image cannot answer? Record these before departure. They become the observational focus at the site.
When students arrive, give them two or three minutes to orient themselves using their printed or remembered satellite view. Ask: what do you recognise from the map? What is different from what you expected?
The satellite image is recent but not live. It has no sound, no smell, no texture, no movement. Direct students to record specifically what they are experiencing that the map could not have told them:
Where practical, ask students to physically pace out a distance they estimated before the visit: the width of the stream, the length of the beach section, the distance from the gate to the sampling site. Record their paced estimates. These will be compared to map measurements back in the classroom.
Ask students to photograph features they want to find again on the map when they return: a distinctive rock formation, the bend in the stream, the far edge of the field. These images anchor the map comparison back in the classroom.
Return to ArcGIS MapMaker and use the distance tool to measure the distances students predicted and paced at the site. Use the area tool to calculate the size of features they explored. Compare measured values against estimates and paced distances. The three-way comparison — prediction, paced estimate, map measurement — is the data set.
| Protocol | Map activity |
|---|---|
| Stream Macroinvertebrates | Find the stream reach, measure its width, identify upstream land use |
| Marine Environment | Measure the beach section, identify coastal features, compare tidal zones |
| Punakaiki | Measure the site, identify blowholes, explore the coastal landform |
| Abel Tasman | Measure track sections, identify coastal and bush zones, compare habitats |
| Any outdoor protocol | Find the site, predict features, measure the field area, compare map to experience |
These prompts are anchored in two things students now have: what they observed at the site, and what the map measured. The AI conversation should always start from that specific field data. Generic questions about the environment produce generic answers. Questions that begin "we visited [place] and found [specific things]" produce responses students can actually interrogate.
Tell a gen AI chatbot two things you saw on the map before your visit, and one thing you discovered at the site that the map could not tell you. Ask: "Why can a satellite map not show everything about a place?" Listen to the answer and say whether you think it is right.
Tell a gen AI chatbot your prediction of how big or how far something was, and then the real measurement from the map. Ask: "Why do people often guess sizes and distances wrong?" Does the AI's reason match why you think your guess was off?
Tell a gen AI chatbot what you saw, heard, smelled, and touched at the place you visited. Ask it: "What do those things tell you about what this place is like?" Compare what the AI says to what you actually experienced.
Ask a gen AI chatbot about the kinds of animals or plants you might expect to find at the place you visited. Then compare the AI's list to what you actually observed. What did the AI include that you did not see? What did you find that the AI did not mention?
Tell a gen AI chatbot your three predictions about the site before you visited, and whether each was confirmed or overturned by the experience. Ask: "What kinds of things about a place can you predict from a satellite map, and what kinds can you not?" Evaluate the AI's answer against what you found.
Share your three measurements with a gen AI chatbot: your prediction, your paced estimate, and the ArcGIS measurement. Ask: "Why do these three methods give different answers, and which is most reliable for each purpose?" Evaluate the AI's reasoning.
Return to the satellite map of your field site and look at what surrounds it. Describe what you see to a gen AI chatbot and ask: "What do the surrounding land uses tell us about the pressures this site faces?" Compare the AI's response to what you actually observed at the site.
Tell a gen AI chatbot what you observed at the site and ask it to help you design a simple plan for monitoring whether anything changes over time. What would you measure? How often? What would count as a significant change? Evaluate whether the plan it suggests is actually doable at your site.
Ask a gen AI chatbot: "In what contexts do environmental scientists, urban planners, and conservationists use satellite imagery as primary data, and what are the known limitations of that data?" Then apply each limitation to what you observed at your field site: where would satellite data have been misleading?
Compare your three distance or area measurements: your initial estimate, your paced field estimate, and the ArcGIS tool result. Ask a gen AI chatbot to explain the sources of error in each method. Evaluate the AI's explanation against what you know about how each measurement was made.
Use ArcGIS to zoom out from your field site and describe the broader landscape to a gen AI chatbot: the land use upstream or upwind, the proximity to urban or agricultural areas, the visible drainage patterns. Ask: "What does this landscape context predict about the conditions we would expect to find at the site?" Compare the AI's prediction to your actual field findings.
Ask a gen AI chatbot to compare the kinds of evidence available from satellite imagery, from sensor networks, and from direct field observation. Then write a short argument for why field based data collected by students at a specific site and time has scientific value that none of the remote sources can replicate.
| Level | Years 0–6 | Years 7–10 | Years 11–13 |
|---|---|---|---|
| 1 | Student finds the field site on the map before the visit, identifies at least two features visible from the satellite image, and can locate the same features when they arrive at the site. | Student navigates to the field site, identifies key spatial features, and records at least two predictions about the site based on the satellite view before departing. | Student analyses the satellite image of the site before the visit, records specific predictions about site conditions, and identifies features that the map suggests but cannot confirm. |
| 2 | Student identifies at least one thing the satellite map showed correctly and one thing the map could not tell them, based on their experience at the site. | Student systematically compares their map predictions to their field observations, noting where each prediction was confirmed, overturned, or incomplete. | Student produces a structured comparison of map predictions against field observations, identifying specific conditions the satellite data could not capture and explaining why. |
| 3 | Student makes a prediction about a distance or area before the visit, paces an estimate at the site, and uses ArcGIS to measure it back in the classroom. Can say which of the three values was largest or smallest. | Student completes the predict-pace-measure cycle for at least one distance and one area, and can explain in their own words why the three values differ and which method is most reliable for each purpose. | Student conducts a measurement error analysis across the three methods, draws conclusions about the appropriate use of each in an environmental science context, and evaluates the reliability of ArcGIS measurements as field data. |
| 4 | Student uses a gen AI chatbot to explore a question raised by the gap between the map and the field experience, and can say whether the AI's answer added to or contradicted what they found out by being there. | Student uses a gen AI chatbot to investigate the land use context visible on the satellite map, evaluates the AI's response against field observations, and identifies at least one claim they would want to verify using another source. | Student analyses satellite imagery as a data source using a gen AI chatbot as a thinking partner, critically evaluates the AI's account of remote sensing limitations, and applies those limitations explicitly to their own field site and data. |
| 5 | Student can explain in their own words why looking at the map before and after a visit makes the outdoor experience richer, and can describe one thing they want to investigate on the map before their next trip outside. | Student designs a simple monitoring protocol using both map and field methods: specifies what to measure, when, at which location on the map, and what change in either the map data or field observations would indicate something worth investigating further. | Student designs a field and remote sensing inquiry: specifies research question, data sources (satellite imagery, field measurement, and AI generated context), collection methods, and a reasoned argument for why direct field observation cannot be replaced by either remote sensing or AI generated explanation in this context. |