Human Futures Lab | One Planet, Many Contexts
ChangeEd · Collaboration for Educational Change
A ChangeEd global collaboration

Human Futures Lab

One Planet, Many Contexts brings students and educators together to investigate a real sustainability challenge, use AI critically and responsibly, take meaningful local action, and learn from communities around the world.

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Central question
How can we use AI—without surrendering human judgement—to understand and improve a sustainability challenge in our community?
Human capacities are the purposeJudgement, empathy, creativity, resilience and responsibility become visible through action.
Educators lead the evolutionTeachers adapt, experiment, document and contribute professional knowledge.
AI supports—not replaces—thinkingStudents verify outputs, identify omissions and retain responsibility for decisions.
One planet, many contextsA shared global concern is explored through different local realities and forms of knowledge.
The project in one view

Global coherence without global uniformity

Every team follows a common inquiry process, but selects a locally relevant challenge. A school may investigate water, food, energy, transport, biodiversity, waste, climate resilience, sustainable spaces or another issue that matters in its community.

Begin with local reality

Students observe, listen, measure and identify a challenge that is visible, meaningful and appropriate to their context.

AI

Interrogate the technology

AI becomes one source among several. Students ask what it knows, what it misses, what must be verified and which choices remain human.

Act, learn and adapt

Teams test a realistic response, study what happens and value honest learning over polished claims of success.

Using intelligence. Exercising judgement. Creating a human future.

Proposed Human Futures Lab tagline
The unifying framework

Purpose, people and practice

The three project preferences become parts of one coherent model rather than competing alternatives.

Purpose · The why

The Future Human Curriculum

Education develops judgement, empathy, creativity, systems thinking, adaptability, purpose and ethical responsibility through authentic challenges.

People · The who

Educators as Evolutionary Agents

Educators are trusted to design, experiment, adapt, collaborate and build knowledge from what happens in real classrooms and communities.

Practice · The how

Real-World AI Pathways

Students encounter AI through real applications, ethical questions, community evidence and decisions whose consequences extend beyond an assignment.

Sustainability is multidimensional

Environmental, social, cultural, economic and ethical consequences belong in the same inquiry.

Knowledge comes in many forms

Scientific data, official statistics, professional expertise, local experience and community knowledge each have value and limitations.

Technology is never neutral

Participants examine privacy, bias, access, environmental costs, power, authorship and unintended consequences.

Action can remain modest

A carefully tested first step is more credible than claiming to solve a complex global problem completely.

Comparison requires humility

International partners explore differences in context without ranking communities or transferring solutions unchanged.

Failure can produce knowledge

A transparent account of an unsuccessful intervention may be more valuable than a superficial success story.

The common inquiry process

Discover → Investigate → Interrogate AI → Imagine → Act → Reflect → Connect

Every team follows the same seven-stage journey. The structure supports meaningful exchange while allowing different ages, resources, subjects and community priorities.

1

Discover

Observe the local environment, listen to people and identify a sustainability challenge that genuinely matters.

2

Investigate

Combine measurements, interviews, local knowledge, reliable sources and accessible data.

3

Interrogate AI

Test AI explanations and proposals. Identify unsupported claims, missing perspectives and contextual errors.

4

Imagine

Generate possibilities and examine feasibility, fairness, sustainability and unintended consequences.

5

Act

Implement a manageable intervention, prototype, recommendation, experiment or community response.

6

Reflect

Evaluate evidence, revise assumptions and document how student and educator thinking changed.

7

Connect

Share the local story, compare contexts and contribute learning to the Human Futures Atlas.

Global Futures Circles

International exchange that improves local decisions

Participating teams are connected with a small number of age-aligned partners from contrasting contexts. Partners do more than display finished products: they question evidence, reveal assumptions and help one another refine possible actions.

Exchange 1: What challenge is visible in our context?
Exchange 2: What does our evidence—and the AI—suggest?
Exchange 3: What did we try, and what changed?
Required evidence: One assumption or decision revised through international dialogue.
🌍

The Human Futures Atlas

A global map combining local challenge portraits, evidence, AI critiques, actions, outcomes and lessons for other communities.

🧭

Student–educator teams

Students exercise agency while educators provide pedagogical guidance, safeguards, access to expertise and space for responsible experimentation.

🤝

Community knowledge partners

Families, specialists, organisations, local authorities and knowledge holders help students understand consequences that technology alone cannot reveal.

Common project outputs

Five forms of evidence from every participating team

The final presentation is only one element. The project makes the quality of inquiry, human judgement, local action and professional learning visible.

1

Local Challenge Portrait

The issue, its context, the people affected and why it matters locally.

2

AI and Evidence Log

Prompts, outputs, verification, errors, omissions, revisions and final human decisions.

3

Action or Prototype

A realistic intervention, experiment, recommendation, redesigned process or tested idea.

4

Human Capacities Portfolio

Evidence of judgement, empathy, creativity, collaboration, adaptability and agency.

5

Educator Learning Note

A concise action-research reflection on pedagogy, assessment, conditions and adaptation.

What authentic assessment should examine

?

Quality of questions

Did the inquiry move beyond a superficial description of the issue?

Evidence and verification

Were claims tested through multiple sources and forms of evidence?

Human judgement

Can students explain why they accepted, rejected or changed AI-supported ideas?

Systems thinking

Were environmental, social, cultural and economic consequences considered?

Ethical responsibility

Did the team consider rights, fairness, privacy, inclusion and possible harm?

Adaptation

Did evidence, feedback or unexpected conditions lead to meaningful revision?

Meaningful action

Was something tested, changed, communicated or contributed beyond the classroom?

Reflection

Can students and educators explain how their understanding changed?

Participant learning library

Foundational resources for students and educators

Participants do not need to read every document. The project can provide short learning cards and selected excerpts based on these authoritative sources.

Human capacities

OECD Learning Compass 2030

A framework connecting agency, wellbeing, knowledge, skills, attitudes and values while emphasizing local contextualisation.

Open resource ↗
Sustainability

UNESCO Greening Curriculum Guidance

Age-responsive, action-oriented guidance for climate and sustainability learning.

Open resource ↗
Student AI literacy

UNESCO AI Competency Framework for Students

A human-centred progression through AI understanding, ethics, applications and creation.

Open resource ↗
Teacher development

UNESCO AI Competency Framework for Teachers

Competencies for human-centred thinking, ethics, AI foundations, pedagogy and professional learning.

Open resource ↗
Rights and safety

UNICEF Guidance on AI and Children

Principles for safety, privacy, fairness, transparency, inclusion, wellbeing and accountability.

Open resource ↗
Responsible use

UNESCO Guidance for Generative AI

Human-centred guidance for age appropriateness, privacy, institutional responsibility and meaningful educational use.

Open resource ↗
Environmental impact

UNEP — AI Lifecycle and the Environment

A framework for considering infrastructure, energy, water, materials, waste and wider environmental effects.

Open resource ↗
Visible thinking

Harvard Project Zero Thinking Routines

Simple structures for observation, evidence, perspective-taking, interpretation and reflection.

Open resource ↗
Implementation

EEF School’s Guide to Implementation

A practical approach to contextual adaptation, implementation behaviours, evidence and iterative improvement.

Open resource ↗

Optional evidence and data pathways

Climate, weather and environmental change
NASA Climate Change, NOAA Climate Data Resources and the World Bank Climate Change Knowledge Portal can help teams compare local observations with longer-term patterns.
Sustainable Development Goals
The United Nations SDG Global Database provides indicators, definitions and country-level trends. Teams should still examine whether broad national indicators represent their local reality.
Water, food and agriculture
UN-Water provides water and sanitation information, while FAOSTAT supports investigations of food systems, agriculture, land and production.
Biodiversity and air quality
GBIF provides open biodiversity records. WHO air-pollution resources can support environmental-health inquiries while teaching the difference between measurements and modelled estimates.
Introductory machine learning
Google Teachable Machine can help students examine training examples, classification errors and bias without advanced programming. The learning value lies in investigating why the model fails—not merely producing a model.

Resources the Lab should provide directly

1. Sustainability Challenge Selector
A matrix for considering local importance, safety, feasibility, available evidence, community relevance and potential for meaningful action.
2. Human Capacities Framework
Observable examples of judgement, empathy, creativity, collaboration, adaptability, resilience, purpose and agency.
3. AI and Evidence Log
A record of prompts, outputs, claims requiring verification, sources consulted, errors found, revisions made and final human decisions.
4. Community Interview and Consent Guide
Age-appropriate guidance for respectful listening, informed consent, privacy, attribution and reciprocal community participation.
5. Action or Prototype Canvas
A planning tool covering the challenge, stakeholders, assumptions, proposed response, risks, intended evidence and conditions for adaptation.
6. Educator Action-Research Note
A concise format for documenting the pedagogical change, implementation, student experience, evidence, contextual factors and future adaptation.
7. Human Futures Atlas Template
A common submission structure combining context, evidence, AI use, human judgement, local action, outcomes and advice for other communities.
8. Low-Connectivity Participation Guide
Printable materials, shared-device approaches, teacher-mediated AI use and offline research options so that access to technology does not determine participation.
Similar initiatives

Models that can inspire specific parts of the Lab

No single initiative combines all the intended elements. These projects are useful for studying global coordination, student action, evidence, educator leadership, mentoring and responsible AI.

CAP

Climate Action Project

Global classroom coordination, common timing, local investigation and international exchange.

Visit project ↗
DFC

Design for Change

A memorable Feel → Imagine → Do → Share process that moves students from empathy to action.

Visit project ↗
GLOBE

The GLOBE Program

Scientific protocols, student-generated environmental observations and internationally comparable data.

Visit project ↗
ECO

Eco-Schools

Student leadership, sustainability audits, action planning, measurement and wider community involvement.

Visit project ↗
GSP

Global Schools Program

Educator formation, curriculum integration and teachers positioned as sustainability leaders.

Visit project ↗
UNITAR

AI for Social Impact Challenge

A structured pathway connecting AI literacy, ethics, the SDGs, low-connectivity approaches and project design.

Visit project ↗
TECH

Technovation Girls

Mentorship, community problem-solving, technological prototyping and facilitator support.

Visit project ↗
EARTH

The Earth Prize

Expert mentoring, project canvases, international visibility and a global catalogue of student ideas.

Visit project ↗
R&S

Roots & Shoots

Compassionate local action connecting people, animals, communities and ecosystems.

Visit project ↗
WLL

The World’s Largest Lesson

Accessible, multilingual and age-banded resources that connect learning with the Sustainable Development Goals.

Visit project ↗
Possible first pilot

An eight-week global inquiry

The pilot should be structured enough to create a shared experience and flexible enough for different calendars, ages, technologies and school contexts.

Week 1

Enter the Human Futures Lab

Explore human capacities, sustainability, responsible AI and the shared global challenge.

Week 2

Discover the local context

Observe, listen and choose a locally meaningful sustainability challenge.

Week 3

Investigate through evidence

Collect measurements, stories, expert perspectives and reliable background information.

Week 4

Interrogate the AI

Compare AI outputs with local evidence and meet international partners to question assumptions.

Week 5

Imagine and refine

Develop possibilities, assess risks and receive feedback from community and global partners.

Week 6

Act and document

Test a manageable response and record decisions, obstacles, outcomes and unexpected effects.

Week 7

Evaluate and adapt

Study the evidence, revise conclusions and prepare student and educator reflections.

Week 8

Connect and contribute

Exchange learning internationally and publish a contextualised entry in the Human Futures Atlas.

Design commitments

Local relevance
Every team chooses a challenge grounded in its own community and environment.
Responsible AI
AI use is transparent, verified, age-appropriate and subject to human judgement.
Multiple forms of knowledge
Data, research, observation, testimony and community knowledge are examined together.
Educator agency
Teachers adapt the common framework and document what they learn from implementation.
Genuine action
Students test or contribute something beyond producing a presentation.
Honest evidence
Uncertainty, limitations, unintended consequences and unsuccessful attempts can be reported openly.
Global accessibility
No paid AI platform, advanced equipment or constant internet connection is required.
Non-competitive learning
Recognition values judgement, evidence, adaptation, ethical responsibility and contribution.
The central proposition

One shared process. Many local futures.

The Lab does not ask students to imitate a universal solution. It helps them combine evidence, technology, community knowledge and human judgement to make a responsible contribution where they are.

Human capacities give the project purpose.
Educator agency makes it possible.
Responsible AI creates a real-world pathway.
Global exchange turns local learning into shared knowledge.