AI literacy and agency
Understand what AI systems do, use them deliberately, verify their output, protect privacy, and keep responsibility visible.
Practice: source audits, tool-use logs, model comparisons, bounded claimsEmerging learning field · working thesis
The Human Agency Economy
An economy should be judged partly by whether people can understand the systems around them, develop meaningful capability, participate in consequential decisions, and contribute knowledge that others can build upon.
AI should increase people’s capacity to learn, reason, create, cooperate, and shape their conditions—not make human judgment invisible or human development expendable.
Learners will be invited to question this proposition, find counterexamples, uncover tensions, and improve the language. The curriculum itself is part of the commons.
Six connected strands
A field built across boundaries
Technical communication
Make difficult ideas accurate, accessible, useful, and appropriately uncertain for a real audience.
Practice: explainers, decision briefs, instructions, diagrams, revision historiesBusiness analysis
Begin with needs and conditions; examine stakeholders, value, constraints, assumptions, risks, and possible change.
Practice: problem frames, interviews, process maps, outcome measuresEthics and judgment
Reason about competing goods, duties, consequences, uncertainty, consent, dignity, and accountable action.
Practice: case comparison, moral imagination, counterargument, appealsSocial conditions and power
Study how institutions, history, incentives, culture, material conditions, and unequal power shape opportunity.
Practice: systems maps, distributional analysis, missing-voice reviewThe knowledge commons
Create knowledge people can access, adapt, challenge, maintain, and govern together.
Practice: open licensing, contribution rules, stewardship, correction pathsAgency as the first “employer”
Contribution is the opportunity.
The initial opportunity is not a conventional job funnel. Agency sponsors meaningful work: investigate a question, improve a framework, test an explanation, facilitate a discussion, or create a learning module the community can reuse.
Open the opportunity profile →What we mean by polymath
Breadth joined to humility.
Not encyclopedic recall, elite identity, or solitary genius. We mean the demonstrated ability to learn across domains, respect disciplinary depth, connect ideas carefully, revise beliefs, and collaborate with people whose knowledge differs.
See the pilot path →From learning to our own content
A transparent content-development cycle
- Ask a consequential question.Begin with a learner, community, or institutional need—not a fashionable topic.
- Investigate across evidence and experience.Use primary sources, lived knowledge, domain expertise, and disclosed AI assistance.
- Make assumptions and disagreement visible.Preserve uncertainty, counterarguments, affected perspectives, and important limits.
- Create and test the learning experience.Write for a defined audience, provide meaningful practice, and gather accessibility and learner feedback.
- Publish into the commons.Choose an appropriate open license, record provenance, invite correction, and name a steward.
- Revise through use.Study whether the material expands understanding and practical agency, then improve it publicly.