Alignment Report
How the AI Agility Challenge meets, exceeds, and completes the
U.S. Department of Labor's AI Literacy Framework.
Part 1
Educators have seen this pattern before. Digital literacy. Information literacy. Data literacy. Each time, institutions taught people to operate the tools, declared them literate, and watched the competency decay. AI literacy is about to repeat this pattern at scale. Unless we go further.
Students can operate AI tools competently. The DOL standard. Necessary. Every institution should meet it.
AI Agility includes everything in AI Literacy, plus the human capabilities that make it productive, sustainable, and career-defining.
Decide when and whether to use AI. Judgment, not dependency.
20 skills employers hire for: critical thinking, collaboration, discernment.
Sustainable practices that protect focus, relationships, and meaning.
From completing tasks to creating outcomes that matter.
Redesigning how work gets done. Promotable, not just productive.
Building new skills continuously. Adapt instead of retrain.
AI collaboration is socio-cognitive. Same skills as good teamwork.
Connecting AI use to what matters. Direction, not just activity.
AI Literacy produces graduates who can use the tools.
AI Agility produces graduates who get more capable over time, not less.
Part 2
On February 13, 2026, the U.S. Department of Labor published TEN 07-25, the first federal AI literacy framework. Five content areas and seven delivery principles. Below: how the AI Agility Challenge maps to each one.
| DOL Sub-Area | AI Agility Element |
|---|---|
| Pattern recognition & probabilistic outputs | ✓Module 1.3: Test same prompt across tools, observe different outputs. |
| Capabilities and modalities | ✓Module 1.3: Writing, research, code, creative, data tools compared. |
| Training and inference | ✓Module 2.1: Inference vs. reasoning in AI behavior. |
| Hallucinations and accuracy | ✓Module 2.2: Evaluate, verify, refine. Module 2.4: Audit trust. |
| Human design and oversight | ✓Module 1.4: AI-First vs Human-First decision design. |
| DOL Sub-Area | AI Agility Element |
|---|---|
| Range of applications | ✓20 modules: writing, analysis, creative, advisory, data, workflow design. |
| Industry-specific uses | ✓Every exercise personalized to the learner's industry and role. |
| Emerging applications | ✓Modules 4.3, 4.4, 3.5: Creativity, capability building, analytics. |
| DOL Sub-Area | AI Agility Element |
|---|---|
| Prompt design | ✓Module 1.1: ROAC. Module 2.1: CROW. Module 4.1: Structured blocks. |
| Context and constraints | ✓Module 2.3: Chunking, Refinement, Curating, Knowledge Injection. |
| Iterative refinement | ✓Module 2.2: Four-stage elevation. Module 3.1: Meta-prompting. |
| Collaborative interaction | ✓Module 1.2: Command vs. collaborative prompting. |
| DOL Sub-Area | AI Agility Element |
|---|---|
| Accuracy assessment | ✓Module 2.2: Verify stage. |
| Bias detection | ✓Module 3.4: Bias-checking habits. |
| Professional standards | ✓Module 2.2: Personalize stage. |
| DOL Sub-Area | AI Agility Element |
|---|---|
| Privacy | ✓Module 3.4: Privacy rules. Module 2.4: Behavior audit. |
| Transparency | ✓Module 3.4: Documentation standards. |
| Ethics | ✓Module 2.4, 2.5: Trust, wellbeing. |
20 applied exercises, each using the learner's actual work. Not simulation.
Every exercise personalized to role, industry, and context.
DOL names five. The HumanAI Taxonomy: 8 domains, 64 skills, 512 micro-skills, 5,120 building blocks, 765 researchers.
No technical prerequisites. Native language. Any browser.
Four courses over 18-24 months. One-year learning community.
Leaders develop AI fluency alongside their teams.
14 versions in 16 months. Tool-agnostic. Durable human skills layer.
Part 3
The DOL framework tells you what workers should be able to do. It doesn't explain how they develop that capability.
Goal articulation, critical evaluation, knowing when to trust and when to override. The same capabilities that make human collaboration work.
Users who regulate their thinking while using AI outperform those who simply know how AI works.
776 professionals at P&G. Individuals with AI matched human teams, but only with collaborative skills.
Without deliberate practice, AI fluency becomes AI dependency.
Learning to use AI productively is not a technical training problem. It is a human development challenge.
Part 4
Delivery Principle 3 names five human skills. One page. That same list has appeared in every strategic plan for the past decade.
Intentionality
Self-Determination
Human Judgment
Situational Awareness
Navigating Uncertainty
Collective Agency
Human × AI
Responsible AI
Part 5
The AI Agility Challenge is the most evolved digital learning experience available for developing these capabilities.
Exercise is calibrated for each learner. Agency levels, capability maturity, language choices, difficulty, and session engagement.
The Agentic Learning Guide stays within the prescribed design of each exercise without exceeding its role in developing capabilities.
Every participant joins our learning community for a full year, extending learning well beyond the modules themselves.
Personalized to industry, role, and context. Consistent learning outcomes delivered through individual experiences.
The Agentic Learning Guide detects the learner's capability level and agency in AI collaboration. Then it adjusts scaffolding levels automatically.
As AI capabilities evolve, the community builds momentum with new skills. Fresh content via articles, videos, and learning modules.
14 releases since 2025, driven by AI's pace of change and daily user feedback from thousands of learners.
When the learner needs to think, the guide steps back. Reflection, metacognition, and self-assessment woven into every exercise.
Builds momentum through ongoing opportunities to practice, share, and grow alongside peers building the same capabilities.
Part 6
These are the skills we help people build, while the 18-month window of opportunity is open.
Human × AI Collaboration
0-6 months
Process Integration
0-6 months
Redesigning Work
6-12 months
Systems & Scale
12-24 months
AI Agility
Twenty modules over 90 days (average: 5.5 weeks). Asynchronous. Short video then applied practice using real work. Tool-agnostic. Privacy by architecture.
