How the AI Agility Challenge meets, exceeds, and completes the U.S. Department of Labor's AI literacy framework.
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 that pattern at scale, unless programs go further than operation.
People can operate AI tools competently. This is the federal standard, and it is necessary. Every institution should meet it.
Understand AI principles · Explore AI uses · Direct AI effectively · Evaluate AI outputs · Use AI responsibly
Everything in AI literacy, plus the human capabilities that make it productive, sustainable, and durable: judgment about when to use AI at all, collaboration systems others can reuse, verification habits, and the professional standards to defend the output.
Human agency · Human skills · Healthy habits · Value creation · Workflow design · Adaptive capability · Collaborative intelligence · Purpose
AI literacy produces people who can use the tools. AI agility produces people who get more capable over time, not less.
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.
AI Agility Challenge is a bundle. It includes Human-First AI Literacy, Human × AI Fluency, and the AI Agility core, because the capabilities the DOL describes are built across all three rather than in any one of them.
The crosswalk below names which program each element comes from. Every learner in the Challenge receives all three.
humanskills.ai conducted this analysis. It is a self-assessment against the published framework, not a federal certification, and the DOL does not certify or endorse training providers.
Content areas
A foundational component of AI literacy is developing a clear grasp of what artificial intelligence is and how it works.DOL TEN 07-25, Content Area 1
Met. Built through applied practice rather than lectures.
Met. Learners apply AI to their own work rather than case studies.
Exceeds. Direction is treated as a system to be built and reused, not a prompt to be written once.
Exceeds. Evaluation extends past accuracy into whether the work is defensible and worth the reader's time.
Exceeds. A dedicated program, Responsible and Ethical AI, extends this further for organizations that need it.
Delivery principles
Every module pairs a short lesson with applied practice against the learner's own work. Twenty applied exercises across the Agility core alone, and the program ends with a capstone the learner defends under questioning.
Exceeds. Practice, not simulation.
Exercises are personalized to role, industry, and the work in front of the learner. Virgil, the agentic learning guide, adapts each exercise to that context.
Met.
Critical thinking, creativity, communication, values-based decisions, domain expertise.DOL TEN 07-25, Delivery Principle 3
The DOL names five. Our programs are built against a structured human skills taxonomy that covers each of them explicitly, with dedicated modules on decision quality, adversarial thinking, perspective-taking, and professional judgment.
Exceeds.
No technical prerequisites. The foundational programs are included, so a learner with no prior AI experience starts at the beginning rather than being excluded. Available in the learner's own language, in any browser. No paid AI subscription required.
Met.
A published capability model from L0 through L7, programs available now across the first three levels, and further programs on the roadmap. Every completer receives twelve months in the Directing Intelligence community with continuing applied content.
Met.
Leaders develop the same capability alongside their teams rather than receiving a separate briefing. Private team cohorts run against the organization's own context so managers and staff build shared language.
Met.
Twenty-one revisions since 2025, driven by the pace of model change and by learner feedback. Tool-agnostic throughout: learners build capabilities that transfer across providers rather than proficiency in one product.
Exceeds. Agility is structural, not a claim.
The DOL framework describes what workers should be able to do. It does not describe how they develop that capability. The research does.
Goal articulation, critical evaluation, and knowing when to trust and when to override. The same capabilities that make human collaboration work.
Sidra and Mason, 2025. International Journal of Human-Computer Interaction.
People who regulate their own thinking while using AI outperform those who simply understand how AI works.
Atchley et al., 2024. Cognitive Research: Principles and Implications.
In a study of 776 professionals, individuals working with AI matched the performance of human teams, but the effect depended on collaborative skill.
Dell'Acqua, Mollick, and Lakhani, 2025. Harvard Business School Working Paper 25-043.
Without deliberate practice, AI fluency becomes AI dependency.
Sandhaus et al., 2024. British Journal of Educational Technology.
Learning to use AI productively is not a technical training problem. It is a human development problem.
We publish an eight-level model of Human × AI capability. The DOL framework describes the first two levels well. The programs below carry a learner through the third and beyond.
Ninety days of access. Asynchronous, with cohort and self-paced options. Tool-agnostic. Every course meets WCAG 2.2 AA.
AI literacy is table stakes. AI agility is the foundation for directing intelligence.
