Lessons from the 2026 GTE Conference – Part 2: Powerful Geography: Tools, Data and Enquiry (Used with Judgement)
If climate education formed the moral and emotional spine of GTEConf26, then questions of tools, data and enquiry formed its disciplinary backbone. Across multiple sessions, a consistent message emerged: powerful geography does not come from having more resources, but from making better geographical decisions.
Core message: Trainees do not need to master every tool, dataset or platform. They need to learn how to exercise disciplinary judgement: choosing when, why and how tools and data sharpen geographical thinking — and when they do not.
Sessions that fit this theme
- Supporting trainees’ considered use of Oak Geography – Graeme Schofield
- GIS‑T: Teaching about Climate Change with the help of GIS – Sophie Wilson
- Open geographical data for use in teacher education – Karl Donert
- Geographical enquiry using aerial photographs and maps – Anne Rakstad Pettersen
- Taking a close look at microfibres: exploring air quality in the classroom – Verity Jones
- The potential of AI to transform geographical education – Cyrus Golding
What these GTEConf26 sessions argued (quick evidence snapshots)
1) Resources do not teach — teachers do.
Graeme Schofield was explicit that Oak Geography should not be understood as a “finished curriculum” or a substitute for professional judgement. Instead, Oak was presented as a free, optional, adaptable curriculum resource, designed to lower workload and reduce disadvantage, but always requiring teacher decision‑making.
Crucially for mentors, he named three legitimate trainee uses of Oak:
- Adapting lessons (as all teachers do)
- Developing subject knowledge (e.g. fieldwork, GIS)
- Interrogating curriculum sequencing (“why this, why now?”)
He was equally clear about the risks: linear lesson structures, necessary for national online resources, should often be edited, truncated or re‑ordered by trainees who know their pupils’ prior learning and context. The professional act is not using Oak — it is adapting it with intent.
2) GIS and data should create thinking, not just interaction.
Sophie Wilson and Karl Donert both argued that data literacy is now central to geographical education, but warned against superficial use. GIS, dashboards and open datasets are powerful only when they enable pupils to compare, decide, explain and justify.
Wilson demonstrated curated GIS hubs (e.g. Europe Observed Mean Temperatures, Mapmaker, Esri Teach with GIS) that:
- make climate and environmental data local and comparable
- allow layering to reveal spatial relationships
- reduce teacher workload through curation, not creation
Donert extended this argument into open data and teacher data literacy, showing how European tools (YouthMetre, D3, European Values Atlas) allow pupils to work with real, contested datasets and explore controversial issues (e.g. migration, inequality, climate attitudes) through evidence rather than assertion.
Across both sessions, a shared principle emerged: If a tool does not change the quality of the geographical thinking, it should be removed.
3) Enquiry needs structure — and restraint.
Anne Rakstad Pettersen’s Norwegian classroom research offered a valuable corrective to unstructured “discovery learning”. Her study showed that enquiry succeeds when teachers actively scaffold it.
Using aerial photographs and maps, pupils:
- observed land‑use change over time
- created representations (maps)
- compared, discussed and explained patterns
Key findings for mentors:
- Teacher modelling matters, but must be brief
- Familiar local areas lower the threshold — but may also limit curiosity
- Pupils need explicit geographical questions to move beyond description
- Teachers play a crucial role in sustaining academic discussion
This reinforces an important message for trainee development: enquiry is not the absence of teaching — it is carefully designed teaching.
4) Small‑scale, authentic data builds disciplinary confidence.
Verity Jones’ microfibres project showed how low‑tech, high‑impact enquiry can transform pupils’ understanding of invisible environmental processes.
Using simple petri dishes and microscopes, pupils collected real air‑quality data within their own school, revealing:
- spatial variation (e.g. photocopiers, staff rooms)
- links between human activity and environmental exposure
- misconceptions about pollution and “clean” spaces
Equally important was the affective dimension: careful framing prevented fear, emphasised uncertainty, and foregrounded what data can and cannot yet tell us. This session modelled how authentic data collection can sit at the heart of geography without expensive technology.
5) AI’s real potential lies in amplifying pupil voice, not replacing thinking.
Cyrus Golding deliberately resisted an AI “how‑to” session. Instead, he reframed the conversation around knowledge production and voice, using the Routes Journal as an example of how pupils can become authors of geographical knowledge.
His provocation for teacher educators was clear: the danger is not that pupils will use AI, but that geography teaching becomes de‑personalised and de‑politicised. AI should therefore be approached critically — as a tool that might support drafting, synthesis or publication — while thinking, questioning and positionality remain human work.
What this means for mentors and providers (5 practical moves)
Move 1 — Treat resource use as a mentoring conversation, not a compliance check.
When a trainee uses Oak, Esri, or an AI‑supported resource, ask:
- What did you keep, remove or change — and why?
- What do pupils know now that they didn’t before?
- Where did you exercise judgement?
The presence of adaptation is a positive indicator of professionalism, not a weakness.
Move 2 — Require every tool to earn its place.
Introduce a simple mentoring rule:
If a map, dataset or platform does not help pupils decide, compare or explain, it is not yet doing geographical work.
This prevents “click‑through GIS” and supports purposeful use of technology.
Move 3 — Model enquiry explicitly, then step back.
Encourage trainees to:
- model observation, categorisation and questioning briefly
- name what they are doing (“I’m simplifying here because…”)
- then hand intellectual responsibility to pupils
This reflects the balance identified in Pettersen’s research between guidance and independence.
Move 4 — Normalise working with imperfect, real data.
Support trainees to use:
- partial datasets
- uncertainty ranges
- conflicting indicators
This is particularly powerful in climate, population and inequality units, and aligns with Donert’s emphasis on critical data literacy.
Move 5 — Keep AI framed as assistive, not authoritative.
In mentor conversations, focus less on “Can trainees use AI?” and more on:
- how they explain limitations to pupils
- how they preserve authorship and voice
- how they maintain disciplinary rigour
Quick diagnostic you can run in a 20‑minute mentor meeting
Tool test:
Ask the trainee to justify one resource they used by answering:
- What geographical question did this help pupils answer?
- What would have been lost without it?
Enquiry test:
Ask pupils’ work:
- Is there evidence of claim + evidence + explanation — or just activity?
Judgement test:
Ask the trainee:
- What would you do differently if you taught this lesson again tomorrow?
Common pitfalls (and how to avoid them)
Resource substitution.
Avoid replacing teacher thinking with “better resources”. Tools should amplify, not replace, professional judgement.
Over‑extended enquiry.
Too much modelling flattens thinking; too little leaves pupils stranded. Coach for brevity and precision.
Tech‑led lesson design.
Start with the geographical question, not the platform.
AI as shortcut.
Reframe AI as a drafting or organising aid — never as a source of geographical authority.
Ready‑to‑use “artefacts” you can drop in your review
- The “Resource Justification” prompt (mentor use)
- Why this resource?
- Why now?
- Why this way, for these pupils?
- One‑map / one‑dataset rule
Limit trainees to one key visualisation per lesson, paired with a written claim + caveat.
- Enquiry sentence stem for pupils
“The data/map suggests ___, because ___.
However, this may be limited by ___.”
If you would like to explore any of these ideas further, or obtain session materials and links to the tools mentioned, please get in touch and I will do my best to connect you with session presenters.
secondarygeography@nasbtt.org.uk