This protocol is designed to be used alongside the LEARNZ virtual field trip What Lies Beneath? (mapping241), which follows a hydrographic surveying team aboard the Tupaia as they map the seabed of Tūranganuī-a-Kiwa / Poverty Bay off Gisborne. The field trip is free for registered NZ teachers and covers sonar technology, GIS data collection, LINZ data applications, and the wider uses of seabed data by iwi, government agencies, and marine industries.
The LEARNZ field trip is the preparation layer for the physical visit. Use it in class before going to the water so that students arrive with the key concepts already in place.
Any harbour, estuary, coastal foreshore, or wharf will serve as the field site. The visit does not require access to the water itself — the surface, the harbour infrastructure, and the surrounding environment are the evidence base.
What does the water tell you from here? Water colour and turbidity are indicators of depth, sediment load, and what lies below. Dark water is usually deep; pale or sandy-coloured water may indicate shallows. Ripple patterns reveal current directions. What can you read — and what can you not?
Every piece of harbour infrastructure — wharves, navigation markers, channel buoys, breakwaters, dredged passages — exists because someone knew what the seabed looked like beneath it. Identify the features around you and ask: what seabed knowledge made each one possible?
Who is on or around the water? Fishing boats, ferries, container vessels, recreational craft, kayakers, divers. Each has a different relationship to seabed depth and character. Ask: what would each of these users need to know about what lies beneath, and how would they know it?
Photograph the surface, the harbour, and the surrounding coastline with location enabled. Use iNaturalist to record coastal species — birds, plants, marine invertebrates visible at low tide. Sketch an annotated map of your field site showing what is visible and where the unknown begins.
Before or at the visit, download the LINZ nautical chart for your location from data.linz.govt.nz. Find the depth sounding closest to where you are standing. That number, in metres, is what lies directly beneath you — and it was put there by a process identical to what the LEARNZ field trip describes.
LINZ makes hydrographic data publicly available for all New Zealand coastal waters. Working with this data is the bridge between the virtual field trip, the physical visit, and the geospatial science that connects them.
The LINZ Data Service at data.linz.govt.nz provides free access to New Zealand nautical charts, bathymetric data, and hydrographic survey layers. Students can search for their visit location and explore actual depth soundings, seabed type classifications, and chart features for the water they just stood beside.
The Tūranganuī-a-Kiwa / Poverty Bay seabed survey is viewable in 3D through the LEARNZ Google Earth tour. For schools in Tairāwhiti, this is the data for the actual water near your school. For other schools, it demonstrates what your own regional data would look like if the same survey were conducted locally.
Depth soundings on a chart are point measurements: they tell you the depth at that specific location at the time of survey. Between soundings, the depth is interpolated. Ask students: what lies between the measurements? How does a surveyor decide how many soundings are enough? What does the chart not tell you?
Esri's ArcGIS Online is free for New Zealand schools and allows students to build their own map layers, overlay bathymetric and chart data, and visualise spatial relationships between the seabed, the coastline, and land-based features. The LEARNZ field trip introduces GIS tools that students can begin to use directly here.
These prompts work best after students have completed the LEARNZ field trip and made a physical visit. The central comparison in each prompt is between what a gen AI chatbot knows about seabed mapping in general and what students now know about a specific place: the water they stood beside, the depth sounding they found on the LINZ chart, and the gap between the visible surface and the mapped reality below it. The AI knows the general. Your students know the particular. That gap is the learning task.
Ask a gen AI chatbot to describe what the seabed probably looks like at your visit location. Then look at the LINZ chart depth sounding for the same spot. Did the AI know? How could it find out — and how did the surveyor on the Tupaia find out?
Ask a gen AI chatbot how scientists find out about things that are too deep or too dark to see. Compare the answer with what you learned from the LEARNZ field trip. What tool did the Tupaia use that the AI described — and what did the AI leave out?
Ask a gen AI chatbot who uses seabed maps and why. Then add one user you learned about from the LEARNZ field trip that the AI did not mention. Why might that group be missing from the AI's answer?
Look at the LINZ chart for your visit location. Now think about what you saw at the water's edge: birds, boats, plants, people, smells, sounds. Ask a gen AI chatbot what a nautical chart cannot tell you. How does your list of observations compare with the AI's answer?
Ask a gen AI chatbot to explain the physics of echo sounding and multibeam sonar. Compare the explanation with what you saw demonstrated in the LEARNZ field trip. Where does the AI explanation match the field trip detail? Where does it stay general where the field trip was specific?
Describe your visit location to a gen AI chatbot — the harbour, the water colour and conditions, the infrastructure you observed, and the LINZ depth reading you found. Ask it to describe what the seabed is likely to look like at that point. Then compare its prediction with the LINZ chart data. How accurate was it, and why?
Ask a gen AI chatbot to explain what nautical chart data can and cannot reveal about a marine environment. Compare the answer with the observations your class made at the water's edge — the things you saw that no chart records. What would a complete picture of that location require beyond a depth sounding?
The LEARNZ field trip identifies multiple users of hydrographic data: shipping, iwi, DOC, councils, aquaculture, flood modellers. Ask a gen AI chatbot to rank these stakeholders by how much they depend on accurate seabed data. Challenge the ranking with what you learned from the field trip. What did the AI miss or underweight?
Ask a gen AI chatbot to explain the complete methodology of a modern hydrographic survey: multibeam sonar, LiDAR, mobile laser scanning, and GPS integration. Evaluate the explanation against the DML survey methodology described in the LEARNZ field trip and available on the DML website. Where is the AI account accurate? Where does it lack the precision of professional practice?
Ask a gen AI chatbot what factors affect the accuracy and reliability of seabed survey data. Then evaluate those factors against the specific survey conditions at Tūranganuī-a-Kiwa / Poverty Bay shown in the LEARNZ field trip. Which factors were most significant in this survey? What does the chart not tell subsequent users about the conditions under which it was made?
A hydrographic survey produces a physical model of the seabed. Ask a gen AI chatbot what a seabed map cannot tell you about the marine environment, and what additional data types would be needed for a complete picture. Evaluate the AI's answer against the ecological, cultural, and historical dimensions of the water you visited that are absent from any chart.
You now have three accounts of your visit location: your own physical observations, the LEARNZ virtual field trip representation of the same type of environment, and AI generated explanations of the science. Ask a gen AI chatbot to reflect on what each mode of knowing contributes and what each misses. Then write your own evaluation: where did the AI's reflection fall short of what you actually experienced?
| Level | Years 1–6 | Years 7–10 | Years 11–13 |
|---|---|---|---|
| 1 | Student can name at least one technology used to map the seabed and explain in simple terms why we need maps of the ocean floor. Can say what a depth sounding is and locate one on a LINZ chart. | Student identifies the key technologies used in the survey (echo sounder, multibeam sonar, GPS) and can explain what each measures and why the combination produces a useful 3D map of the seafloor. | Student describes the full technical methodology of a hydrographic survey, identifies the agencies and roles involved, and explains how the resulting data is structured, accessed, and used by different stakeholders. |
| 2 | Student explains why looking at the surface of the water is not enough to understand what lies beneath, and why a purpose-built vessel with specialist technology was needed to produce the seabed map. Connects the survey technology to at least one real use of the data. | Student explains why hydrographic survey data matters across multiple sectors: safe navigation, flood modelling, marine conservation, and iwi rights. Uses specific examples from the LEARNZ field trip and the LINZ chart for their visit location to ground each claim. | Student constructs an account of the relationship between hydrographic data quality and the decisions that depend on it: navigation safety margins, flood risk calculations, aquaculture consenting. Evaluates the consequences of data gaps, inaccuracy, or outdated surveys for those decisions. |
| 3 | Student asks a gen AI chatbot to describe what the seabed looks like at their visit location and compares the answer with what the LINZ chart actually shows. Can say whether the AI knew or was guessing, and explain why the difference matters. | Student uses a gen AI chatbot to interpret chart features or predict seabed conditions at a specific location, then evaluates the accuracy of that interpretation against LINZ data and LEARNZ field trip content. Identifies where the AI generalised rather than responding to specific local data. | Student conducts a systematic comparison of AI generated explanations of hydrographic methodology and data applications with the specific technical content of the LEARNZ field trip and LINZ documentation. Identifies the conditions under which AI is reliable for geospatial science and where specialist data and professional expertise are required. |
| 4 | Student explains what being at the water's edge added that the LEARNZ virtual field trip and AI explanation could not: the scale of the water, the smell of the sea or harbour, the sound of the environment, the experience of standing at the surface while thinking about what lies directly beneath. | Student articulates the difference between knowing about seabed mapping through a virtual field trip and standing at a real harbour or coastal location: the specific sensory and spatial experience of a real place, and the questions that experience generated that the virtual trip did not. | Student reflects on the relationship between three modes of knowing the same environment: physical observation, virtual field trip, and AI generated explanation. Evaluates what each contributes, what each misses, and what the combination produces that no single mode could achieve alone. |
| 5 | Student generates one question about the seabed or coastal environment near their school that they would like answered, and can name at least one person or organisation they would ask to find out. | Student designs a simple local inquiry using LINZ data or Google Earth: identifies a feature of the seabed or coastal environment near their visit location, formulates a question about it, and identifies the data and methods they would use to investigate it further. | Student identifies a gap in the available hydrographic or coastal data for their region, proposes a research question that the gap raises, and outlines the methodology, data sources, and stakeholder engagement that a genuine investigation would require — including who would use the findings and how. |