8. AI and the Future of Work: Economics, Governance, and Policy

Course Positioning

A multidisciplinary course on how AI changes tasks, occupations, firms, wages, education, institutions, law, and public policy.

Learning outcomes

Analyze AI impact at the task level rather than making simplistic job replacement claims.
Understand augmentation, automation, deskilling, reskilling, labor displacement, productivity, wage effects, and inequality risks.
Design workforce transition plans for teams, firms, schools, and local economies.
Compare governance approaches: company policy, national regulation, standards, audits, procurement rules, and international principles.
Evaluate AI adoption through both productivity and social legitimacy lenses.
Create a responsible AI workforce strategy for a real organization or sector.

Course Design Snapshot

Positioning: A multidisciplinary course on how AI changes tasks, occupations, firms, wages, education, institutions, law, and public policy.
Audience: Business leaders, HR leaders, educators, policymakers, students, economists, consultants, union leaders, and civic organizations.
Duration: 8 weeks, 1-2 sessions per week.
Prerequisites: No coding required. Comfort with reading reports and discussing evidence.
Format: Case studies, task analysis, labor-market frameworks, governance design, policy simulation, and scenario planning.

Expanded Topic-by-Topic Coverage

Module 1. From jobs to tasks

Module focus: From jobs to tasks: exposure, complementarity, substitution, tacit knowledge, and why occupations are bundles of activities. Primary live activity or lab: Decompose five jobs into tasks and classify AI exposure.

Topics and coverage

exposure

What it means: define exposure clearly and connect it to the module focus: From jobs to tasks: exposure, complementarity, substitution, tacit knowledge, and why occupations are bundles of activities.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

complementarity

What it means: define complementarity clearly and connect it to the module focus: From jobs to tasks: exposure, complementarity, substitution, tacit knowledge, and why occupations are bundles of activities.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

substitution

What it means: define substitution clearly and connect it to the module focus: From jobs to tasks: exposure, complementarity, substitution, tacit knowledge, and why occupations are bundles of activities.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

tacit knowledge

What it means: define tacit knowledge clearly and connect it to the module focus: From jobs to tasks: exposure, complementarity, substitution, tacit knowledge, and why occupations are bundles of activities.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

why occupations are bundles of activities

What it means: define why occupations are bundles of activities clearly and connect it to the module focus: From jobs to tasks: exposure, complementarity, substitution, tacit knowledge, and why occupations are bundles of activities.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Decompose five jobs into tasks and classify AI exposure.
Learners produce: Decompose five jobs into tasks and classify AI exposure.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 2. Productivity and firm redesign

Module focus: Productivity and firm redesign: copilots, agents, workflow automation, management layers, and organizational bottlenecks. Primary live activity or lab: Map a department before and after AI adoption.

Topics and coverage

copilots

What it means: explain how copilots changes the interaction between human intent, model behavior, external information, and final output.
What to cover: inputs, constraints, examples, output format, grounding, iteration, failure modes, and when a human must intervene.
Demonstration: show a weak attempt, a stronger structured attempt, and a reviewed final version with explicit checks.
Evidence of learning: learners create a reusable prompt, schema, retrieval note, or workflow pattern and test it on at least two examples.

agents

What it means: explain how agents changes the interaction between human intent, model behavior, external information, and final output.
What to cover: inputs, constraints, examples, output format, grounding, iteration, failure modes, and when a human must intervene.
Demonstration: show a weak attempt, a stronger structured attempt, and a reviewed final version with explicit checks.
Evidence of learning: learners create a reusable prompt, schema, retrieval note, or workflow pattern and test it on at least two examples.

workflow automation

What it means: show where workflow automation appears in the learner's real workflow and which parts are judgment-heavy versus draftable.
What to cover: current workflow, pain points, AI-assisted steps, human review checkpoints, quality standard, and ownership of the final decision.
Demonstration: convert one messy real-world input into a structured brief, draft, analysis, checklist, or next action.
Evidence of learning: learners produce a reusable template or playbook entry that can be used after the course.

management layers

What it means: define management layers clearly and connect it to the module focus: Productivity and firm redesign: copilots, agents, workflow automation, management layers, and organizational bottlenecks.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

organizational bottlenecks

What it means: define organizational bottlenecks clearly and connect it to the module focus: Productivity and firm redesign: copilots, agents, workflow automation, management layers, and organizational bottlenecks.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Map a department before and after AI adoption.
Learners produce: Map a department before and after AI adoption.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 3. Skills and education

Module focus: Skills and education: reskilling, assessment, lifelong learning, credentialing, and AI literacy across age groups. Primary live activity or lab: Design a skill transition pathway for one occupation.

Topics and coverage

reskilling

What it means: define reskilling clearly and connect it to the module focus: Skills and education: reskilling, assessment, lifelong learning, credentialing, and AI literacy across age groups.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

assessment

What it means: define assessment clearly and connect it to the module focus: Skills and education: reskilling, assessment, lifelong learning, credentialing, and AI literacy across age groups.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

lifelong learning

What it means: define lifelong learning clearly and connect it to the module focus: Skills and education: reskilling, assessment, lifelong learning, credentialing, and AI literacy across age groups.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

credentialing

What it means: define credentialing clearly and connect it to the module focus: Skills and education: reskilling, assessment, lifelong learning, credentialing, and AI literacy across age groups.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

AI literacy across age groups

What it means: define AI literacy across age groups clearly and connect it to the module focus: Skills and education: reskilling, assessment, lifelong learning, credentialing, and AI literacy across age groups.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Design a skill transition pathway for one occupation.
Learners produce: Design a skill transition pathway for one occupation.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 4. Labor economics

Module focus: Labor economics: wages, bargaining power, inequality, winner-take-most markets, and geographic concentration. Primary live activity or lab: Debate whether AI raises average productivity while widening inequality.

Topics and coverage

wages

What it means: define wages clearly and connect it to the module focus: Labor economics: wages, bargaining power, inequality, winner-take-most markets, and geographic concentration.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

bargaining power

What it means: define bargaining power clearly and connect it to the module focus: Labor economics: wages, bargaining power, inequality, winner-take-most markets, and geographic concentration.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

inequality

What it means: define inequality clearly and connect it to the module focus: Labor economics: wages, bargaining power, inequality, winner-take-most markets, and geographic concentration.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

winner-take-most markets

What it means: connect winner-take-most markets to the data lifecycle from source and structure through analysis, interpretation, and decision-making.
What to cover: source reliability, missing or biased data, leakage, assumptions, calculations, and the difference between correlation and decision-ready evidence.
Demonstration: walk through a small dataset or example table and mark the checks required before trusting the result.
Evidence of learning: learners produce a short analysis note that includes assumptions, limitations, and verification steps.

geographic concentration

What it means: define geographic concentration clearly and connect it to the module focus: Labor economics: wages, bargaining power, inequality, winner-take-most markets, and geographic concentration.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Debate whether AI raises average productivity while widening inequality.
Learners produce: Debate whether AI raises average productivity while widening inequality.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 5. Governance inside organizations

Module focus: Governance inside organizations: acceptable-use policy, procurement, audits, documentation, human review, and accountability. Primary live activity or lab: Draft a company AI use policy for one department.

Topics and coverage

acceptable-use policy

What it means in this course: define acceptable-use policy in operational terms, not as an abstract principle.
What to cover: sensitive data boundaries, affected stakeholders, approval paths, documentation, and what Business leaders, HR leaders, educators, policymakers, students, economists, consultants, union leaders, and civic organizations must never delegate blindly to AI.
Use case: present one acceptable use, one borderline use, and one prohibited use, then ask learners to justify the classification.
Evidence of learning: learners add a risk control, review step, or escalation rule to their course project.

procurement

What it means: define procurement clearly and connect it to the module focus: Governance inside organizations: acceptable-use policy, procurement, audits, documentation, human review, and accountability.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

audits

What it means: define audits clearly and connect it to the module focus: Governance inside organizations: acceptable-use policy, procurement, audits, documentation, human review, and accountability.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

documentation

What it means: define documentation clearly and connect it to the module focus: Governance inside organizations: acceptable-use policy, procurement, audits, documentation, human review, and accountability.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

human review

What it means: define human review clearly and connect it to the module focus: Governance inside organizations: acceptable-use policy, procurement, audits, documentation, human review, and accountability.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

accountability

What it means: define accountability clearly and connect it to the module focus: Governance inside organizations: acceptable-use policy, procurement, audits, documentation, human review, and accountability.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Draft a company AI use policy for one department.
Learners produce: Draft a company AI use policy for one department.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 6. Public policy

Module focus: Public policy: education policy, competition policy, data governance, labor protections, public sector AI, and safety standards. Primary live activity or lab: Run a policy simulation for schools, SMEs, or healthcare workers.

Topics and coverage

education policy

What it means in this course: define education policy in operational terms, not as an abstract principle.
What to cover: sensitive data boundaries, affected stakeholders, approval paths, documentation, and what Business leaders, HR leaders, educators, policymakers, students, economists, consultants, union leaders, and civic organizations must never delegate blindly to AI.
Use case: present one acceptable use, one borderline use, and one prohibited use, then ask learners to justify the classification.
Evidence of learning: learners add a risk control, review step, or escalation rule to their course project.

competition policy

What it means in this course: define competition policy in operational terms, not as an abstract principle.
What to cover: sensitive data boundaries, affected stakeholders, approval paths, documentation, and what Business leaders, HR leaders, educators, policymakers, students, economists, consultants, union leaders, and civic organizations must never delegate blindly to AI.
Use case: present one acceptable use, one borderline use, and one prohibited use, then ask learners to justify the classification.
Evidence of learning: learners add a risk control, review step, or escalation rule to their course project.

data governance

What it means in this course: define data governance in operational terms, not as an abstract principle.
What to cover: sensitive data boundaries, affected stakeholders, approval paths, documentation, and what Business leaders, HR leaders, educators, policymakers, students, economists, consultants, union leaders, and civic organizations must never delegate blindly to AI.
Use case: present one acceptable use, one borderline use, and one prohibited use, then ask learners to justify the classification.
Evidence of learning: learners add a risk control, review step, or escalation rule to their course project.

labor protections

What it means: define labor protections clearly and connect it to the module focus: Public policy: education policy, competition policy, data governance, labor protections, public sector AI, and safety standards.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

public sector AI

What it means: define public sector AI clearly and connect it to the module focus: Public policy: education policy, competition policy, data governance, labor protections, public sector AI, and safety standards.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

safety standards

What it means in this course: define safety standards in operational terms, not as an abstract principle.
What to cover: sensitive data boundaries, affected stakeholders, approval paths, documentation, and what Business leaders, HR leaders, educators, policymakers, students, economists, consultants, union leaders, and civic organizations must never delegate blindly to AI.
Use case: present one acceptable use, one borderline use, and one prohibited use, then ask learners to justify the classification.
Evidence of learning: learners add a risk control, review step, or escalation rule to their course project.

Practice and evidence of learning

Learners complete or discuss: Run a policy simulation for schools, SMEs, or healthcare workers.
Learners produce: Run a policy simulation for schools, SMEs, or healthcare workers.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 7. Ethics and rights

Module focus: Ethics and rights: surveillance, bias, transparency, explainability, dignity, consent, accessibility, and inclusion. Primary live activity or lab: Audit a workplace AI tool for fairness and worker impact.

Topics and coverage

surveillance

What it means: define surveillance clearly and connect it to the module focus: Ethics and rights: surveillance, bias, transparency, explainability, dignity, consent, accessibility, and inclusion.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

bias

What it means in this course: define bias in operational terms, not as an abstract principle.
What to cover: sensitive data boundaries, affected stakeholders, approval paths, documentation, and what Business leaders, HR leaders, educators, policymakers, students, economists, consultants, union leaders, and civic organizations must never delegate blindly to AI.
Use case: present one acceptable use, one borderline use, and one prohibited use, then ask learners to justify the classification.
Evidence of learning: learners add a risk control, review step, or escalation rule to their course project.

transparency

What it means: define transparency clearly and connect it to the module focus: Ethics and rights: surveillance, bias, transparency, explainability, dignity, consent, accessibility, and inclusion.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

explainability

What it means: define explainability clearly and connect it to the module focus: Ethics and rights: surveillance, bias, transparency, explainability, dignity, consent, accessibility, and inclusion.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

dignity

What it means: define dignity clearly and connect it to the module focus: Ethics and rights: surveillance, bias, transparency, explainability, dignity, consent, accessibility, and inclusion.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

What it means: define consent clearly and connect it to the module focus: Ethics and rights: surveillance, bias, transparency, explainability, dignity, consent, accessibility, and inclusion.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

accessibility

What it means: define accessibility clearly and connect it to the module focus: Ethics and rights: surveillance, bias, transparency, explainability, dignity, consent, accessibility, and inclusion.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

inclusion

What it means: define inclusion clearly and connect it to the module focus: Ethics and rights: surveillance, bias, transparency, explainability, dignity, consent, accessibility, and inclusion.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Audit a workplace AI tool for fairness and worker impact.
Learners produce: Audit a workplace AI tool for fairness and worker impact.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 8. Scenario planning

Module focus: Scenario planning: optimistic, turbulent, unequal, and regulated AI futures. Primary live activity or lab: Present a workforce transition strategy with milestones and safeguards.

Topics and coverage

optimistic

What it means: define optimistic clearly and connect it to the module focus: Scenario planning: optimistic, turbulent, unequal, and regulated AI futures.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

turbulent

What it means: define turbulent clearly and connect it to the module focus: Scenario planning: optimistic, turbulent, unequal, and regulated AI futures.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

unequal

What it means: define unequal clearly and connect it to the module focus: Scenario planning: optimistic, turbulent, unequal, and regulated AI futures.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

regulated AI futures

What it means: define regulated AI futures clearly and connect it to the module focus: Scenario planning: optimistic, turbulent, unequal, and regulated AI futures.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Present a workforce transition strategy with milestones and safeguards.
Learners produce: Present a workforce transition strategy with milestones and safeguards.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Core labs and builds

Task exposure mapping lab for teaching, accounting, law, HR, sales, software, healthcare, and manufacturing.
Workforce transition lab: redesign roles without erasing human accountability.
Governance lab: acceptable-use policy, risk tiers, review boards, and training requirements.
Policy lab: local reskilling plan for SMEs, colleges, or government departments.

Capstone

Create a sector-specific AI workforce transition plan. It should include affected tasks, augmentation opportunities, risk of displacement, reskilling plan, governance model, measurement framework, and policy recommendations.

Assessment design

Task exposure analysis.
Workforce redesign memo.
Governance policy draft.
Final transition plan presentation.

Recommended tools and datasets

Task inventories, job descriptions, workforce survey templates, governance checklists, policy report readings, role redesign canvases, risk matrices.

Instructor notes

The course should emphasize that AI adoption is not only a technology transition. It is an institutional transition involving incentives, skills, law, trust, and distribution of gains.

Instructor Build Checklist

Prepare one short demo for each module and one learner activity that creates a saved artifact.
Prepare examples that match the audience, local context, and likely tools learners can access.
Add a verification step to every AI-generated output: factual check, source check, data sensitivity check, and quality review.
Keep a running portfolio folder so each module contributes to the final project or learner playbook.
Reserve time for reflection on what the learner did, what AI did, what was checked, and what remains uncertain.