A useful iNaturalist observation has three qualities: the organism is clearly visible in the photograph, the location is recorded, and the habitat context is noted. All three are achievable with a phone.
Install the iNaturalist app and create a free account. Enable location services. The NZ node is at inaturalist.nz; the global platform is at inaturalist.org. Both feed the same global dataset. NZ-based identifiers tend to see observations submitted through inaturalist.nz more quickly.
Any living organism counts: plant, insect, bird, spider, lichen, fungus. Do not disturb or collect. The photograph is the observation.
Fill the frame with the organism. Get as close as the focus allows. Photograph from multiple angles. Capture whatever features distinguish this organism: wing patterns, leaf shape, bark texture, body segments.
Place a finger, coin, or ruler alongside the organism for at least one photograph. Scale is critical for invertebrates and small plants where size alone resolves the identification.
Step back and photograph where the organism was found. This provides context the AI uses for identification and tells researchers something about the conditions at the site.
In the app's notes field, record: substrate (soil, rock, bark, water), light conditions, and any behaviour observed. Brief notes are enough.
Submit from the field if signal allows. Otherwise save as draft and submit at school. Both are equally valid. Your observation becomes part of the permanent global biodiversity record.
Expert identifiers may respond within hours. Two agreeing identifications upgrades your observation to research grade and adds it to the Global Biodiversity Information Facility dataset used in scientific research worldwide.
The same organism identified by three different tools will often produce different results. That difference is the learning task, not an inconvenience to be resolved.
Image recognition trained on biodiversity photographs. Location aware: narrows candidates to species recorded in your region. Expert verified: the identification community confirms or corrects the AI suggestion. Observations are permanent, citable records in a global dataset.
A language model generating text from patterns in training data. Most general AI chatbots have limited or no image analysis. No location awareness. No expert verification. No persistent record. Output is generated text, not a verified identification.
Written by domain specialists for specific organism groups: NZ Birds Online for birds, NIWA for freshwater organisms, NZ Flora for plants, DOC species profiles, NZ Fungi. Curated and updated. The reference standard against which the other two tools are evaluated.
| Organism group | iNaturalist | Gen AI chatbot |
|---|---|---|
| Birds | Good for common NZ species | Variable: reasonable for well-known adults; less reliable for juveniles and regional forms |
| Plants | Good; variable for regional forms | Variable: struggles with introduced species and locally distinct forms |
| Invertebrates | Good when location is provided | Frequently unreliable; errors rarely acknowledged as uncertain |
| Fungi | Variable; specialist verification important | Unreliable; visually similar species routinely confused |
| Marine species | Good for adults; variable for juveniles | Unreliable for juveniles and regional forms |
An identification is the entry point, not the destination. Once students know what they found, they can ask what finding it here means. The place story framework turns any iNaturalist observation into an inquiry about the site.
"I found [organism] at [location and brief habitat description, e.g. a sunny rock face in coastal scrub near Napier]. What does finding this organism here tell me about this place?"
"What habitat does [organism] need to survive? What does its presence here suggest about the quality and character of that habitat?"
"Is [organism] under pressure in New Zealand? What land use changes or introduced species most affect it, and what does finding it here suggest about those pressures at this site?"
"What might this location have looked like 100 years ago? What might it look like in 50 years if current trends continue?"
These prompts build on what students observed and photographed in the field. The iNaturalist observation is the anchor for most prompts. Where gen AI is used alongside iNaturalist, the comparison between the two outputs is the learning task, not the AI output itself.
Submit your iNaturalist observation. Then describe your photograph to a gen AI chatbot and ask what it thinks the organism is. Write down both answers. Did the tools agree? Which answer do you trust more, and why?
Look at the species page on iNaturalist for your organism. Then ask a gen AI chatbot: "What does [organism] eat?" Compare the answers. Which one tells you more about where the organism actually lives?
Try submitting a photograph to iNaturalist with location turned off. Then try the same photograph with location on. Does the identification change? Ask a gen AI chatbot: "Why does knowing where a photograph was taken help with identifying a living thing?"
Tell a gen AI chatbot what your group found: "We found [organism] near [place name]. What does this tell us about that place?" Share your answer with the class. Does anything surprise you?
Compare iNaturalist and a gen AI chatbot for one bird, one plant, and one invertebrate from your field session. Which organism type showed the biggest gap in accuracy? What does that pattern suggest about how each tool was built and what it was trained on?
Find your submitted iNaturalist observation online and look at where else this organism has been recorded in NZ. Ask a gen AI chatbot: "Why would conservation researchers want to track where this organism appears and disappears over time?" What would a gap in the distribution map suggest?
Take your most interesting identification and work through the full place story sequence: habitat needs, ecological role, current threats, land use history. At each step, note where the gen AI chatbot is confident and where it hedges or qualifies. What does the hedging tell you?
Ask a gen AI chatbot to explain what "research grade" means in iNaturalist. Then read the actual iNaturalist help documentation at inaturalist.org. Where do they agree? Where does the AI simplify or miss something important?
Document your three-tool comparison for at least three organisms of different types. What pattern do you see in where iNaturalist and gen AI agree versus diverge? What does that pattern reveal about the training data, design assumptions, and appropriate use cases of each tool?
What can iNaturalist observation data contribute that professional biodiversity surveys cannot? What can professional surveys provide that citizen science cannot? Ask a gen AI chatbot, then compare its answer with at least one published source on citizen science data quality. Where does the AI oversimplify?
Choose an organism from your field session with a known NZ conservation status. Examine its iNaturalist distribution map. Ask a gen AI chatbot how distribution data from citizen science could inform a conservation management decision for this species. Evaluate the response against DOC or IUCN sources for accuracy and completeness.
Identify a biodiversity question answerable using iNaturalist data collected over multiple seasons. Specify: the question, organisms to record, minimum observation quality, sampling locations, analysis method, and the threshold that would constitute a meaningful result. Use a gen AI chatbot as a thinking partner throughout the design, but document where you accepted, modified, or rejected its suggestions and why.
| Level | Years 0–6 | Years 7–10 | Years 11–13 |
|---|---|---|---|
| 1 | Student makes and submits at least one iNaturalist observation from the field and can name the organism the app identified. Understands the organism was real, in a real place, and the observation has been added to a global record. | Student makes and submits iNaturalist observations for at least two organisms. Records the iNaturalist identification and compares it with a gen AI chatbot response for at least one organism. | Student makes and submits observations for multiple organisms across at least two organism types. Records identifications from all three tools and begins a structured comparison table. |
| 2 | Student links the organism found to a simple claim about the place: "We found [organism] on a sunny rock face, which means it needs warmth and shelter" or equivalent. Can explain that the organism was really there, not in a photograph or book. | Student explains what the organism's presence indicates about the habitat: light, moisture, temperature, or land use history. Connects the iNaturalist identification to a specific claim about conditions at the site. | Student constructs a causal account connecting land use, habitat quality, and organism presence using field data and the place story prompt framework. Identifies where gen AI claims require verification against authoritative NZ sources. |
| 3 | Student compares what iNaturalist said with what a gen AI chatbot said about the same organism and can explain in simple terms why the two tools gave different answers. Identifies which tool they trust more and gives a reason. | Student documents a three-tool comparison for at least one organism: iNaturalist, gen AI chatbot, and one authoritative NZ source. Identifies where the tools agree and where they differ, and explains what the difference reveals about how each tool works. | Student analyses the three-tool comparison across multiple organism types. Draws conclusions about the conditions under which each tool is reliable and evaluates the implications for using AI in biodiversity and conservation contexts. |
| 4 | Student explains what being outside and finding a real organism added that a photograph, video, or AI chatbot could not: the specific place, the actual light and conditions, the experience of finding something alive in its habitat. | Student articulates what direct field observation provides that secondary sources cannot: independently collected data with a specific location, date, and time, along with habitat context. Explains why those properties matter for a claim about the character or health of a place. | Student reflects on the difference between field-collected citizen science data, professional survey data, and AI-generated explanation. Identifies what each can and cannot constitute as evidence in an environmental science or conservation context. |
| 5 | Student checks back on their iNaturalist observation and can explain that it will be reviewed by expert identifiers and used by researchers. Generates one question they would like to investigate at the same place in a different season or year. | Student checks for expert identifications on submitted observations and formulates a testable monitoring question: what would need to change in the data to conclude that the place's ecological health had improved or declined? | Student designs a monitoring protocol using iNaturalist as the data collection platform: specifies organisms to record, minimum observation quality including research grade threshold, sampling locations and frequency, and a hypothesis about what future change would look like and why. |