Job postings, training catalogs and course descriptions now ask for AI literacy, usually without saying what it means. The closest thing to a federal definition is the Department of Labor's AI Literacy Framework, issued by its Employment and Training Administration on February 13, 2026, as Training and Employment Notice 07-25. It is voluntary guidance, not a regulation, but it is the reference the public workforce system has been pointed to, and community colleges, apprenticeship sponsors and corporate learning teams are among the audiences it names. Here is what it covers.

The definition

The framework treats AI literacy as a foundational set of competencies that let a person use and evaluate AI tools responsibly, with its main focus on generative AI. It describes literacy as the baseline every worker and student should have, distinct from the deeper skills needed to manage or build AI systems. It also notes that terms such as AI proficiency and AI fluency are used inconsistently, sometimes for higher levels of skill and sometimes as synonyms, and says employers will usually need to spell out which specific skills, and at what level, each role requires.

Five content areas

The framework sorts what a person should know into five areas, each with example topics.

  • Understand AI principles. How generative tools produce answers from statistical patterns, which is why the same prompt can return different results; the difference between training a model and running it; why tools can produce confident but wrong output; and the human choices built into every system.
  • Explore AI uses. Drafting and summarizing documents, answering questions and gathering background, producing first drafts of creative work, task-specific uses such as writing code snippets or transcribing audio, and decision support.
  • Direct AI effectively. Giving a tool context about audience and purpose, structuring prompts, supplying the right input data, treating an exchange as a series of revisions, and avoiding vague requests.
  • Evaluate AI outputs. Checking facts against other sources, judging completeness and clarity, spotting missing steps or flawed reasoning, testing whether a result serves the actual goal, and adding human judgment.
  • Use AI responsibly. Keeping sensitive information out of tools, following workplace policies, avoiding misuse, weighing how risk changes by setting, and remaining accountable for the work that results.

Seven delivery principles

The second half of the framework is about how the training is delivered, which is where it speaks most directly to anyone designing a course or a certificate.

  • Experiential learning: practice on real tasks, with feedback, side-by-side comparisons of AI and human work, and exercises that grow harder.
  • Learning in context: examples drawn from the learner's industry, occupation and employer, and AI content built into existing programs such as Registered Apprenticeships.
  • Complementary human skills: critical thinking, creativity, communication and judgment practiced alongside the tools, with emphasis on how domain expertise makes AI more useful.
  • Prerequisites: checking baseline digital skills first, offering refreshers, and planning for device, broadband and bandwidth gaps.
  • Pathways for continued learning: stackable layers that lead from literacy to proficiency, occupation-specific next steps, and routes into AI-related careers or building tools.
  • Enabling roles: preparing instructors, managers, career counselors, peer champions and HR and learning staff first.
  • Agility: modular content, regular updates and revision based on learner feedback and outcomes.

How it connects to funding

The framework expands on preliminary content areas and delivery practices the department set out on August 26, 2025, in Training and Employment Guidance Letter 03-25, which encourages state and local workforce boards to use Workforce Innovation and Opportunity Act Title I youth, adult and dislocated worker funds for AI skills training. Both documents respond to Executive Order 14277 of April 23, 2025, on AI education. The notice also points users to the department's Competency Model Clearinghouse, which it plans to update with digital and AI competencies.

The department calls this version a starting point and says it will revise the framework as the technology and labor market change. It has asked for input in particular on industry-specific guidance for incumbent worker training, apprenticeships and career and technical education.

Using it

For a program director or a training lead, the five content areas work as a checklist against an existing syllabus, and the delivery principles as a check on how it is taught. For a worker, the content areas describe what a role asking for AI literacy is likely to mean at the baseline level. The full framework is attached to the notice linked below.

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