Use the inquiry cycle as a starting point for planning. Bring your subject expertise, local context and knowledge of your learners to each pathway. Clear learning intentions and thoughtful reflection keep curiosity connected to progress.
Explore example inquiries that connect disciplines and give learning a shared purpose. Adapt the depth, materials and outcomes to your learners.
Look closely at a local habitat to understand the relationships between living things and their environment.
Observe a nearby green space. Record what lives there, compare conditions and map the connections you find.
Build an evidence-based habitat guide or propose a small change that could support local biodiversity.
What did your observations reveal? What would you need to investigate before making a recommendation?
Investigate everyday objects and discover how observation, constraints and experimentation shape an invention.
Choose an everyday problem. Study existing solutions, interview people who experience it and identify the constraints.
Make and test a prototype. Measure what works, document your choices and improve the design with feedback.
Whose needs did your design address? Which evidence helped you decide what to change?
Explore how stories, spaces and shared experiences shape the identity of a community.
Compare maps, collect local stories and examine how a familiar place has changed over time.
Curate a community story map using researched accounts, original writing and carefully chosen places.
Which perspectives are represented? What stories are missing, and how could you learn more?
Trust in AI is not a single judgment. Investigate technical reliability, institutional accountability and human judgment separately. Confidence in one layer does not establish trust in the others.
Can the system perform reliably within a defined task?
Compare an AI-generated explanation against teacher-selected sources. Change the wording of the question and identify errors, unsupported claims and inconsistent answers.
Evidence to collect: verified claims, failure cases and the limits of the task.
Does the organisation show accountable practice?
Review a fictional school AI policy. Ask who is responsible, how decisions can be challenged, what data is used and what happens when harm is reported.
Evidence to collect: clear responsibilities, explanations and a workable route for redress.
Do people exercise care, honesty and judgment?
Discuss a scenario in which an AI recommendation conflicts with a learner's needs. Explain when a person should question it, seek help or decline to use it.
Evidence to collect: reasoning, openness about uncertainty and respect for agency.
Suggested classroom adaptations of the discussion shared by Simon Falk. Use fictional scenarios and teacher-selected material; no personal learner data is needed.