01 / UNDERSTAND
See how work is changing.
Understand how AI changes tasks, roles, careers and the capabilities people need.
GLOBAL SHAPERS CHENGDU HUBPRESENTS
Stay in
the Loop.
Build practical AI capability for a changing world of work.
We combine capability assessments, community learning and applied challenges to help young people and early-career workers navigate AI-shaped work.
Real people. Shared learning. Practical change.
Our Reach
120K+Cumulative reachBroader reach through assessments, learning, events and project activities.
Broader reach through assessments, learning, events and project activities.
Explore further5K+Direct participationsDirect participation across programme activities.
Direct participation across programme activities.
Explore further8Countries in the project footprintCountries within the initiative’s wider reach.
Countries within the initiative’s wider reach.
Explore furtherReach and participation figures describe programme activity, not verified capability or employment outcomes.
How we measure03 / THE MODEL
A pathway from change to opportunity.
The loop keeps capability building connected to the world people are trying to navigate.
02 / DIAGNOSE
Know where you stand.
Identify strengths, gaps and priorities across eight capabilities and six developmental profiles.
03 / RESKILL
Build what matters.
Build practical AI fluency, collaboration and judgment through online and offline learning.
04 / APPLY
Turn capability into evidence.
Use challenges, projects and real problems to practise AI-supported work.
05 / LEARN / REPEAT
Reflect, improve and repeat.
Reflect on real-world application, improve and bring learning into the next cycle.
Learn from real work. Bring it back into the Loop.
Why the Loop: rationale, vision and theory of change
See how work is changing.
Translate changes in AI, jobs and the labour market into practical guidance about tasks, roles, careers and capability requirements.
Know where you stand.
Identify strengths, gaps and development priorities across eight capabilities and six developmental AI Work Profiles.
Build what matters.
Develop practical AI fluency, human-AI collaboration, judgment and role-based capabilities through online and offline learning.
Turn capability into evidence.
Use challenges, projects and real-world problems to move beyond learning and demonstrate how AI can be applied to practical work.
Reflect, improve and repeat.
Carry learning from real-world application back into the next cycle of understanding, diagnosis and capability building.
The AI transition should expand opportunity — not widen the gap.
AI is changing tasks, roles and career pathways faster than traditional learning and workforce systems can adapt.
But the ability to respond is uneven. Some people have access to new tools, guidance, professional networks and learning opportunities. Many others do not.
Stay in the Loop exists to make the AI transition more navigable and more inclusive — so people can understand what is changing, build the capabilities that matter, and turn those capabilities into real opportunity.
Our vision
Our vision is a future where AI expands human opportunity rather than deepening the divide between those who can adapt early and those who cannot.
A light theory of change
- Better information
- Clearer capability gaps
- Targeted learning
- Real-world application
- Stronger career and workforce outcomes
The goal is not more AI training. It is better adaptation to an AI-shaped world of work.
04 / THE LOOP IN ACTION
The Loop in Action
Three examples of how communities learn, build and explore AI in practice.
05 / TOOLS THAT POWER THE LOOP
Find your starting point.
Start with a quick reflection, or explore your capabilities through a deeper assessment.
LEVEL 1 · EXPLORE
AI Era Player
6–7 minutes · 7 primary Player Types + the additional Mission Integrator identity · Exploratory · Shareable result
Explore how you currently work with AI.
Start with AI Era PlayerLEVEL 2 · DEEPEN
AI Fluency & Career Growth Assessment
17–22 minutes · 8 capability dimensions · Development report
Understand your strengths, blind spots and development priorities across eight capabilities.
Take the deeper assessmentLearn about the AI fluency assessmentAI fluency isn’t one skill.
The quality of AI-supported work depends on more than knowing how to prompt. Our assessment looks at eight capabilities that shape how people frame problems, collaborate with AI, verify information, exercise judgment, take ownership, learn and create original value.
Read the AI Fluency frameworkEight AI fluency capability dimensions
- Problem Framing & Task Selection
- AI Collaboration & Orchestration
- Verification & Information Literacy
- Explainable Ownership & Accountability
- Domain & Contextual Judgment
- Learning & Capability Transfer
- Synthesis & Original Contribution
- Human Complementarity & Implementation
Six developmental AI Work Profiles
The assessment does not reduce people to a single score. It identifies developmental patterns that help people understand how they currently work with AI — and what to build next.
- Strategic AI Collaborator
- Capable but AI-Dependent User
- Efficient but Fragile Operator
- Creative AI Synthesizer
- Responsible but Underutilized User
- Developing AI Professional
Explore the product experience
06 / THE NETWORK
Connected communities. Local approaches.
9Hubs in the network8Countries in our footprint
Explore the hub network, documented field activities and wider project reach. Involvement and delivery differ across locations.
Nine Global Shapers communities. Shared learning, with each hub contributing in its own way.
Hub networkHub network
Chengdu
China
Part of the Global Shapers community network supporting Stay in the Loop. Each hub takes part in its own way.
Map: Natural Earth · Public domain.
Local adaptation
One model. Many local ways to use it.
The long-term goal is not to export one fixed curriculum. It is to build a model that local communities can adapt to their own workforce and learning context.
LanguageExamplesDeliveryLearning contextLocal workforce needs
07 / EVIDENCE & LEARNING
Evidence & Learning
Research and field conversations help us decide what to assess, teach and test next.
AIGW Policy Observatory
The Missing First Rung
Emerging Signals on Gen Z, AI and Early-Career Development
Explore the evidenceWorld Economic Forum
What AI in education needs next: Lessons from youth leaders across five countries
Explore the evidenceAIGW Policy Observatory
From AI Adoption to Workforce Architecture
Building the Next Phase of Human-Centered Workforce Transformation
Explore the evidenceMore evidence
Featured conversation
Learning to think in the age of AI
Watch on YouTubeMore conversations (3)
Public dialogue keeps local evidence in conversation across communities and contexts.
Cross-hub dialogue
Learning across contexts
Watch on YouTubePublic dialogue
What capability building looks like in practice
Watch on YouTubePublic dialogue
Building the next loop
Watch on YouTube
Continue the conversation.
Explore the updated AIGW Policy Observatory event poster and watch the full conversations.
Watch the featured conversationField conversations and documentation
07 / LISTEN → MEASURE → ADAPT
We listen before we design.
We continuously gather perspectives from young professionals, business leaders, educators and workforce practitioners to understand how AI is changing real work before deciding what people should be assessed or trained on.
40+ FIELD CONVERSATIONS
From field evidence to global dialogue.
Insights from our work on AI, Gen Z and workforce transformation have informed conversations with business leaders and international audiences on how AI is reshaping early-career work, capability development and the future of work.
Cross-hub dialogue, future-of-work conversations, research and public dialogue.
Listening across roles helps us understand what people need to learn, use and repeat.
Generation Z — The Great ExpectationDoes AI Change the Future of Work?
These conversations inform our work; they do not imply endorsement.
We design around evidence — not a fixed curriculum.
As work changes, the capabilities people need will change too. Research, field conversations and public dialogue continuously feed back into the program.
What we measure
We record reach and participation separately, and study changes in capability, application and outcomes. Activity figures do not establish capability gains or improved careers. The definitions and questions below explain how we distinguish these levels and what we are still learning.
- Reach
- Who encountered the initiative
- Participation
- Who directly took part
- Capability
- What people learned or developed
- Application
- What was applied in real situations
- Outcome
- What changed over time
Measurement definitions and testing questions
FROM REACH TO IMPACT
We do not treat participation as impact.
Reach matters. But the next question is whether capability, behaviour and opportunity actually change.
Reach
Who encountered the initiative
Participation
Who directly took part
Capability
What people learned or developed
Application
What was applied in real situations
Outcome
What changed over time
The Impact Ledger
What the numbers mean.
120K+Cumulative reach
Cumulative exposure across assessments, learning, interviews and events — not a count of unique people or demonstrated outcomes.
5K+Direct participations
Participation across programme activities. A person may take part more than once, so this is not a count of unique people.
200Facilitators trained
Facilitators trained to support learning and capability building. Training facilitators is distinct from measuring what participants learn.
30Sessions delivered
Sessions delivered across the initiative. A session count describes activity, not learning or career outcomes.
800+Assessment starts
Assessments started, not completed assessments or unique participants. Starting an assessment does not demonstrate capability improvement.
What we are now testing
We are studying whether learning changes capability, practice and opportunity. These are questions we are exploring, not outcomes we have already demonstrated.
Capability Development
Stronger evidence of effective AI-supported work.
Real-World Application
New skills and workflows applied in study, work or projects.
Career Clarity
Better-informed learning and career decisions.
Learning Transfer
Capability carried into classrooms, organizations and communities.
Institutional Adoption
Tools and learning models reused by hubs, schools and organizations.
The people learn. The program learns.
PeopleUnderstandDiagnoseReskillApplyRepeat
ProgramListenTestMeasureAdaptRepeat
What we learn feeds back into what we assess, teach and test next.
08 / BUILD THE NEXT LOOP WITH US
JOIN THE LOOP.
Capability building gets stronger when more people can add a perspective, a problem, a place or a practical next step.
For individuals
Understand your AI capabilities.
Take the AssessmentFor educators & communities
Bring practical AI capability building to your learners.
- Run an AI literacy activity
- Use the assessment
- Host a workshop
- Build a learning cohort
For organizations
Connect workforce needs with diagnosis, learning and real-world application.
- Run a capability cohort
- Pilot the diagnostic
- Bring a real-world challenge
- Contribute workforce evidence
For Global Shapers
Adapt the model in your community.
- Run the assessment
- Host a dialogue
- Deliver a learning activity
- Localize the model
- Share field evidence