US DOL AI Literacy Framework: What It Covers and How Workers Can Use It

Short answer

• The US DOL AI Literacy Framework is voluntary guidance from the US Department of Labor that describes what basic AI literacy means for American workers and how training programs should teach it. It was issued on 13 February 2026 by the Employment and Training Administration through Training and Employment Notice No. 07-25, known as TEN 07-25. The framework defines AI literacy as a foundational set of competencies that enable individuals to use and evaluate AI technologies responsibly, with a primary focus on generative AI. It is built around two parts: five foundational content areas that describe what workers should learn, and seven delivery principles that describe how training should be delivered. The framework is guidance for program design, not a law and not a mandate. It does not require employers, schools, or training providers to follow it, it does not create a federal AI literacy certificate, and it does not promise a job or a pay increase. Its value is practical. It gives workers, employers, colleges, apprenticeship programs, and workforce agencies a shared vocabulary for judging whether AI training is complete, realistic, and useful in a real job. This article explains each content area and each delivery principle in plain language, shows how different groups can use the framework, and explains its limits, so you can use it as a checklist rather than treating it as a rulebook.

What the Framework Is, and What It Is Not

Start with the distinction that matters most. The framework is voluntary guidance to support the evaluation of high quality AI literacy content and its effective integration into education and workforce systems. The notice that issued it says its purpose is to issue the framework as a resource for program design and to encourage expanded AI literacy training across the public workforce and education systems. That wording matters because it sets the framework apart from a regulation. A regulation imposes legal duties and can be enforced. This framework encourages, guides, and informs. An employer that ignores it breaks no federal AI literacy law by that fact alone, and a training provider that uses a different structure is not automatically in violation of anything. Workplace policies, contracts, state rules, and sector requirements can still apply to a particular employer or program, but those obligations come from those other sources, not from TEN 07-25 itself. If a course or a vendor claims that the DOL framework legally requires your employer to buy a particular product, that claim should be treated with skepticism and checked against the framework itself.

The framework is also a starting point by its own description. The Department of Labor says it is putting the framework forward as a starting point while acknowledging that it will evolve over time based on stakeholder input, advances in AI capabilities, and changes in labor market dynamics. Readers should therefore date stamp any use of it. The version explained here is the version issued on 13 February 2026. Later updates, if issued, could change examples, emphasis, or structure. Before building a long term curriculum on it, check the current DOL advisory page for TEN 07-25 to confirm whether a newer version has replaced it.

A third clarification concerns level. AI literacy in this framework means a foundational level of knowledge and skill that all workers and students should have as AI becomes embedded across the economy. It is the baseline for engaging with AI tools in any job. It is not the same as advanced technical skill. Many roles will require more advanced capabilities beyond this foundational level, such as managing and building AI systems. The framework itself makes that point. So completing a basic AI literacy course should not be presented, by a worker or by a provider, as proof of expert level AI skill. It is proof of a foundation, and foundations are valuable precisely because later, role specific learning can build on them.

Where the Framework Came From

The framework did not appear in isolation. TEN 07-25 places it in a chain of federal actions in 2025 and early 2026. On 23 April 2025, Executive Order 14277, Advancing Artificial Intelligence Education for American Youth, was issued. On 26 August 2025, the Department of Labor issued Training and Employment Guidance Letter No. 03-25, which encouraged the use of Workforce Innovation and Opportunity Act funding to help youth and adults develop AI skills. That guidance letter proposed preliminary components of AI literacy, including foundational content areas and effective delivery practices. TEN 07-25 then expanded on those components. In parallel, in July 2025, the US Department of Education released a Dear Colleague Letter with guidance on using AI to improve education outcomes and announced a supplemental grantmaking priority on advancing AI in education. The framework also supports two broader administration documents named in the notice: America’s Talent Strategy: Building the Workforce for the Golden Age, and Winning the Race: America’s AI Action Plan. Understanding that background helps explain the framework’s audience. It was written mainly for the public workforce and education systems, including state workforce agencies, workforce boards, American Job Centers, community colleges and tribal colleges, apprenticeship agencies, Job Corps centers, and state education agencies, as well as employers and training providers that work with those systems. Workers and job seekers are also named as an audience, because the framework is meant to help them understand what AI literacy means for their careers and how to build it proactively.

The Definition at the Center

The Department of Labor defines AI literacy as a foundational set of competencies that enable individuals to use and evaluate AI technologies responsibly, with a primary focus on generative AI, which is increasingly central to the modern workplace. Two words in that definition deserve attention: use and evaluate. The framework is not only about operating a tool. It is equally about judging what the tool produces. A worker who can generate a polished draft in seconds but cannot spot an error in it is only half literate by this definition. Similarly, a worker who understands AI in theory but has never practiced giving it clear instructions or checking its output has not yet met the practical side of the definition.

The primary focus on generative AI also deserves a plain explanation. The framework recognizes that AI covers many technologies, from older rule based systems to machine learning models and computer vision tools, and that these systems do not all operate the same way. It places primary emphasis on generative AI because, in the Department’s judgment, the most transformative and widespread applications of AI in current workplaces are generative AI tools. When many people say AI in everyday conversation, they are often referring specifically to generative AI. The framework reflects that reality. That focus does not mean other kinds of AI are unimportant. It means the starting curriculum should prioritize the tools most workers are most likely to meet first: tools that generate text, analyze data, recognize images, and work across text, audio, and visual formats.

The framework also addresses a terminology problem that confuses many readers. Some discussions use terms such as AI proficiency or AI fluency. In some cases, those terms imply different levels of mastery. In other cases, they are used as alternative language for the same concept. The Department’s point is practical: saying that a job needs AI literacy is not enough on its own. Employers and other stakeholders may need to define the specific AI skills and the depth of knowledge, or levels of proficiency, appropriate for each role and context. A receptionist, a warehouse supervisor, a nurse, and a marketing assistant may all need AI literacy, but the examples, tools, and risks that matter to each of them will differ. A good training program therefore starts from the framework and then adds role specific detail, rather than treating the five content areas as a finished job description.

The Five Foundational Content Areas

The first section of the framework sets out five foundational content areas. Think of them as the what of AI literacy: the knowledge and skills a worker needs. Each area comes with examples in the framework text. The examples are illustrative, not an exhaustive list of everything a worker could ever need to know. They are useful because they show what good coverage looks like in each area.

1. Understand AI Principles

This area is about developing a clear grasp of what artificial intelligence is and how it works. For workers, it does not require technical mastery. It requires the vocabulary and mental models needed to understand how current AI tools operate, so that use of those tools is confident and accurate rather than guesswork.

The framework gives five examples. First, pattern recognition and probabilistic outputs: AI systems generate responses by identifying statistical patterns in data, which can result in different outputs from the same input. That is why asking the same question twice can produce two different answers, and why an AI output should be treated as a generated response, not as a retrieved fact. Second, capabilities and modalities: common AI capabilities include generating text, analyzing data, and recognizing images, across different input and output formats such as text, audio, or visual content. Third, training and inference: training builds the AI model using large datasets, while inference is how the model generates outputs in real time workplace applications. Fourth, hallucinations and accuracy limits: AI can produce confident but incorrect outputs, making it critical to verify results and avoid overreliance. Fifth, human design and oversight: every AI system reflects human decisions about data, goals, and parameters, requiring users to understand where human judgment is still essential.

For a worker, this area answers questions such as: Why did the tool give me this answer? Why might it be wrong even though it sounds confident? What did humans decide when this system was built, and where do I still need to decide? Without this foundation, later skills are fragile. A worker who does not understand that outputs are probabilistic may treat a fluent paragraph as verified truth. A worker who does understand it will naturally check, compare, and qualify.

2. Explore AI Uses

This area is about understanding how AI is being used across real workplace settings. Workers benefit from exposure to practical applications that show how AI tools can support tasks, augment decision making, and streamline workstreams. Because AI use varies widely by industry, occupation, and context, exploration builds familiarity and judgment. It helps workers recognize when and how to apply AI effectively, and where human input remains essential.

The framework examples cover five kinds of use. Productivity tools help workers draft written documents, create draft presentation outlines, or analyze reports, allowing them to move more efficiently through common tasks in a workflow. Information support uses AI to answer questions, surface relevant background information, or create learning content tailored to specific workplace needs. Creative assistance generates initial drafts of marketing copy, naming ideas, graphic options, or other creative assets that workers can then refine and improve. Task specific applications solve targeted problems, such as writing code snippets, transcribing audio, automating data entry, or organizing complex schedules. Decision support systems generate recommendations, risk assessments, or forecasts that help inform and augment human decision making.

The important distinction in this area is between support and substitution. In every example, the AI output is something a worker reviews, refines, or decides about. Exploration should therefore include examples of poor fits as well as good fits. A useful training exercise is to take a real task from the worker’s own job and ask: Where would AI help here, where would it add risk, and where would it add nothing at all? That kind of exploration builds judgment faster than a generic list of tools.

3. Direct AI Effectively

This area is about understanding how to interact with AI systems in ways that produce useful and relevant results. Because most AI tools depend heavily on the input they receive, users must learn how to provide clear instructions, include necessary context, and guide the system toward better outcomes. Directing AI effectively does not require coding skills. It requires a mental model for how to frame prompts, share information, and iterate strategically to improve the quality of responses.

The framework examples are practical. Contextual framing means providing background information, intended audience, tone, or specific goals so the response better matches the user’s needs in different workplace scenarios. Prompting techniques mean structuring prompts clearly, using step by step instructions, and specifying formats or outputs, which allows workers to unlock more advanced or precise capabilities. Supplying relevant input data means understanding when and how to include the most relevant data, supporting materials, or examples to improve accuracy and usefulness, while still respecting the responsible use rules covered in the fifth content area. Iterating on outputs means treating AI interaction as an ongoing process, using follow up prompts to clarify, refine, or reframe results until they meet the desired standard or purpose. Avoiding vague or misleading prompts means recognizing how prompt clarity and word choice affect outcomes and adjusting the approach to avoid ambiguity.

Readers who want a deeper practical treatment of this skill can compare it with our guide to free AI courses for beginners, which explains how to choose a course that includes practice rather than only explanation. The framework’s point is the same: directing AI is a practiced skill, not a trick. A single clever prompt is less valuable than a habit of giving context, checking the first result, and refining it in a second and third pass.

4. Evaluate AI Outputs

This area is about learning how to assess the quality and usefulness of AI generated outputs. While AI can accelerate work and surface helpful insights, the results it produces still require thoughtful review. Workers need the ability to evaluate whether an output is accurate, complete, and appropriate for the task, applying their own knowledge and judgment to determine how best to use or refine what the AI has provided. This evaluation skill ensures that workers remain in control of the process and that AI is used as a support tool, not as a final authority.

The framework examples give this area a clear checklist. Verifying factual accuracy means cross checking AI generated outputs against trusted sources or known information to identify false claims, outdated references, or fabricated content. Assessing completeness and clarity means reviewing outputs to ensure they fully address the task or question and are expressed in a clear, actionable, or usable form for the intended audience. Spotting gaps or logical errors means identifying missing steps, flawed logic, or faulty assumptions that may make the output unreliable or misleading. Aligning with strategic intent means evaluating outputs based on whether they achieve the desired goal, support the right message, and are fit for purpose in a specific task or workflow. Applying human judgment means layering in personal expertise, context, and discretion when deciding how to interpret, use, or revise AI generated content.

In practice, evaluation is where most workplace harm from AI is prevented or allowed. A report with a fabricated citation, a customer reply that promises something the business cannot do, or a schedule that silently drops a step can all look polished. The framework therefore treats evaluation as a core skill for every worker, not as an advanced extra for specialists. A training program that teaches prompting but never teaches verification is incomplete by the framework’s own standard.

5. Use AI Responsibly

This area is about understanding the boundaries of appropriate use, both to safeguard information and to ensure outputs are applied ethically and effectively. It includes recognizing the limits of AI authority, protecting sensitive data, complying with workplace or legal requirements, and maintaining accountability for outcomes. Workers should be equipped with practical instincts and organizational awareness to use AI tools in ways that are secure, appropriate, and aligned with professional standards.

The framework examples are direct. Protecting sensitive information means understanding what types of data should not be entered into AI tools and how to prevent accidental disclosure of confidential information. Following workplace policies and rules means being aware of and following organizational policies around AI use, including guidance related to specific tools or contexts. Avoiding misuse or harm means being aware of how AI tools can be used inappropriately, whether for plagiarism, impersonation, or harm, and knowing how to report issues. Managing context specific risks means understanding how risk varies across different tasks, audiences, or sectors, and applying greater scrutiny or caution in higher stakes settings. Maintaining accountability means workers remain responsible for the decisions and outputs they produce with AI tools and should avoid treating AI responses as final or authoritative without review.

That last point is worth repeating in plain terms. If you send it, sign it, publish it, or act on it, it is your work, even if an AI tool drafted it. Responsible use is therefore not only about privacy settings. It is about ownership. A worker who checks a draft, corrects it, and stands behind it is using AI responsibly. A worker who forwards an unchecked output to a customer, a manager, or a regulator is not, even if the tool produced it quickly. For readers thinking about the wider footprint of their online work, our article on the amazing digital footprint is a useful companion, because data entered into a tool can travel further than the worker who entered it intended.

The Seven Delivery Principles

The second section of the framework sets out seven delivery principles. If the content areas are the what, these principles are the how. They describe how AI literacy training should be designed and delivered so that it actually changes what workers can do, rather than only what they can describe. A program can cover all five content areas on paper and still fail if it delivers them as abstract lectures with no practice, no context, and no follow up. These principles are the framework’s answer to that failure.

1. Enable Experiential Learning

AI literacy is most effectively developed through direct, hands on use. Workers build confidence and understanding not by reading about AI in the abstract, but by using it in real world contexts to solve actual tasks. Experiential learning accelerates skill development by helping users see how their inputs shape outputs, refine their instincts through trial and error, and build a mental model for how to work productively with AI. It also makes training more engaging and memorable, while allowing workers to immediately apply what they are learning in ways that feel relevant and valuable to their day to day responsibilities.

The framework examples include real world task integration, such as embedding AI tools into day to day tasks such as writing, research, or scheduling; interactive prompt exercises, including practice with poorly written examples so workers can see how phrasing, specificity, and structure affect outcomes; live feedback and iteration, where users receive real time feedback on AI outputs; side by side human comparisons, asking participants to compare AI generated work to human created work or to their own previous outputs; and progressive difficulty levels, designing activities that begin with simple use cases and advance toward more complex workflows.

The practical test for any course is simple. Does the learner actually use an AI tool during the training, on a task that resembles their real work, and receive feedback on the result? If the answer is no, the course may still be informative, but it does not meet this principle. Readers choosing a course should ask for a sample exercise before enrolling, not only a syllabus.

2. Embed Learning in Context

AI literacy becomes more impactful when it is delivered in ways that are directly relevant to the worker’s job, industry, or existing training experience. Embedding AI literacy into familiar settings helps reduce friction, increase uptake, and reinforce how AI fits into existing workflows. Contextualized learning also supports retention by anchoring new concepts to real world scenarios that workers understand, making the content feel more actionable and less abstract.

The framework examples include industry specific examples, aligning instruction with the tools, use cases, and terminology most relevant to a given sector, such as health care, manufacturing, transportation, or retail; occupational tasks and workflows, teaching AI literacy through real job functions and activities that workers perform; employer specific alignment, embedding content within the systems, culture, and goals of a particular employer, including their internal AI tools and policies; training program integration, delivering AI literacy as part of existing Registered Apprenticeships, career and technical education curricula, short term credentialing programs, or reskilling efforts; and cohort specific considerations, adjusting delivery style, pace, and references to match workers’ experience, familiarity with technology, or career stage.

This principle explains why a generic AI course can feel unhelpful even when its content is accurate. A nurse, a machinist, and a retail supervisor do not need the same examples, even though they need the same five content areas. Context is what turns a general framework into usable training. Our guide to what is an apprenticeship explains one of the existing structures the framework names, and it is a good place to see how new content can be embedded in an established pathway rather than bolted on as a separate course.

3. Build Complementary Human Skills

AI tools do not function as standalone capabilities with fixed value. They are amplifiers of human input, and their effectiveness depends heavily on the skills, knowledge, and judgment of the people who design, manage, and interact with them. AI literacy efforts are best delivered when they demonstrate AI’s augmentation of human capabilities such as critical thinking, creativity, communication, and domain expertise. When workers understand how to combine AI’s capabilities with their own insights and instincts, they unlock far greater potential than either could deliver alone.

The framework examples include critical thinking integration, pairing AI use with exercises in problem solving and reinforcing human judgment as central to AI supported decisions; creative development exercises, using AI tools to brainstorm, generate variations, or remix ideas, then applying personal creativity to select, refine, or improve the results; communication refinement, using AI to draft content while teaching workers how to revise AI generated material for tone, clarity, persuasiveness, or appropriateness for the audience; values based decision scenarios, practicing navigation of ambiguous situations where humans must apply a combination of organizational, legal, or personal values to act on AI outputs; and domain expertise amplification, emphasizing how the value of AI increases when workers bring subject matter knowledge or workflow understanding to shape and assess results.

This principle is a useful corrective to two opposite errors. The first error is treating AI as a replacement for judgment, which leads to unchecked outputs. The second error is treating AI as a threat to judgment, which leads to avoidance. The framework’s position is that AI amplifies the judgment that is already there. A worker with strong domain knowledge will get more from an AI tool than a worker without it, because the knowledgeable worker can prompt better and evaluate better. Training that builds AI skill while neglecting the human skill underneath it will therefore hit a ceiling quickly.

4. Address Prerequisites to AI Literacy

AI literacy efforts can only be successful if learners have the foundational tools and access needed to engage with training. For some workers, this may include digital literacy skills, device access, or broadband connectivity, especially in settings where AI tools require stable internet access or use non intuitive interfaces. Programs should proactively identify and address these barriers, ensuring that participants have what they need to complete training and apply AI tools confidently in their daily work. By treating prerequisites as integral to program design, AI literacy efforts can reach more people and deliver better outcomes.

The framework examples include evaluating baseline readiness, starting with simple diagnostics to evaluate whether participants have the digital familiarity needed to begin using AI tools effectively and to identify any barriers; integrating digital literacy skills, offering light touch refreshers or resources on device use, app navigation, or browser tools for participants who need them; considering options for access support, such as public computer labs, mobile first content, or asynchronous formats where device or broadband gaps exist; considering bandwidth flexibility, using training materials that may be more compatible with low bandwidth environments and mobile devices where feasible; and acknowledging different starting points, building delivery models that accommodate a range of skill levels and learning speeds without assuming prior experience.

This principle matters for equity as well as for effectiveness. A program that assumes every worker has a recent laptop, a fast home connection, and confidence with new software will silently exclude the workers who most need a supported first step. Our article on literacy and digital literacy explains the broader foundation this principle rests on. A provider that screens for prerequisites, offers a refresher, and provides a low bandwidth option is not lowering the standard. It is making the standard reachable.

5. Create Pathways for Continued Learning

Foundational AI literacy is only the starting point. As AI tools evolve and become more integrated into the workplace, workers will need clear opportunities to deepen their skills, pursue specialized training, or transition into AI related occupations. AI literacy programs should establish visible routes for participants to build on what they have learned, whether that means developing technical skills, learning to use job specific AI tools, or preparing for career advancement. Connecting workers to next step resources ensures that AI literacy is not a one time event, but a sustained capability that grows alongside the technology.

The framework examples include advancing to AI proficiency, helping participants move from basic AI literacy usage to more advanced AI proficiency, including more directly managing complex AI systems; encouraging builder and entrepreneurship pathways, supporting workers who want to go beyond using AI tools to building their own AI powered solutions, including through entrepreneurship; designing stackable learning models, structuring training in layers that build from foundational literacy to deeper skills in areas such as data handling, AI tool configuration, or prompt engineering; offering occupation specific progressions, aligning continued learning with the specific tasks, tools, and responsibilities associated with different job roles or career stages; and supporting pathways into AI related careers, highlighting next steps for workers interested in transitioning toward AI centric occupations, such as AI product specialists, prompt engineers, or data analysts.

For a worker, the question to ask at the end of any introductory course is: What comes next, and who will help me get there? A course that ends with no next step, no recommended follow up, and no way to practice further is a dead end, even if its first lessons were good. A course that points to a next level, a role specific module, or a workplace project is following this principle. Readers planning that next step may find our guide to how to create a study plan useful for turning a vague intention to keep learning into a dated, testable plan.

6. Prepare Enabling Roles

AI literacy efforts are more successful when the people supporting workers, such as managers, trainers, mentors, or career counselors, are equipped with the right knowledge and tools to guide others effectively. These individuals are not just secondary learners. They require tailored approaches to AI literacy that reflect their unique roles in enabling others. Whether reinforcing concepts in training, encouraging workplace adoption, or helping workers navigate career pathways, these roles shape how AI tools are understood and used. Designing delivery specifically for these enablers ensures that the broader environment actively supports confident and sustained AI use.

The framework examples include train the trainer models, equipping instructors, coaches, or facilitators with targeted AI literacy content and methods to deliver, reinforce, and contextualize learning for others; manager upskilling, providing AI literacy focused on use cases relevant to team oversight, change management, and integrating AI tools into daily operations; career navigation support, tailoring AI literacy for career counselors or mentors so they can guide learners on how AI tools impact job search, career growth, and evolving skill needs; peer learning champions, identifying and training peer leaders with the right framing to serve as accessible, informal sources of support and enthusiasm within teams; and human resources and learning and development alignment, ensuring those leading key learning functions understand how to embed AI literacy across onboarding, upskilling, and internal mobility pathways.

This principle explains why some workplace rollouts stall even when individual workers are enthusiastic. If managers do not know what good AI use looks like in their team, they cannot approve it, correct it, or model it. If trainers have not practiced the tools themselves, they cannot answer the questions learners actually ask. Training the enablers first, or at least alongside the wider workforce, is therefore not an administrative extra. It is part of the design.

7. Design for Agility

AI technologies evolve at a pace unlike previous workplace tools. New capabilities, platforms, and use cases emerge every few months, while older tools become obsolete just as quickly. For workforce programs, this means AI literacy cannot be treated as a fixed curriculum. Training must be designed with built in mechanisms for adaptation, so content and delivery stay current with the technology landscape. Agility ensures programs remain relevant over time and that workers leave with skills that match the tools they will actually encounter on the job.

The framework examples include continuous content updates, building delivery systems that allow for regular refreshes of tools, examples, and instructional content to reflect current AI capabilities; feedback driven iteration, using learner input and real world outcomes to revise delivery methods and content based on what is working in practice; modular content design, structuring training in flexible units that can be swapped, expanded, or reordered as new needs or technologies emerge; responsive use case selection, revisiting and revising scenarios periodically to ensure alignment with the latest workplace applications; and outcome driven iteration, evaluating whether participants are gaining practical, transferable AI skills and using those insights to adapt and refine delivery strategies.

For a worker or an employer choosing a program, agility can be checked in a simple way. Ask when the material was last updated, how often it is reviewed, and whether examples name tools that are still current. A course built around a single tool that has since changed its features, pricing, or interface, with no update since, is a warning sign. A program built in modules, with a stated review cycle, is much more likely to stay useful. The framework itself models this principle by presenting itself as an evolving starting point rather than a final answer.

How Different Groups Can Use the Framework

The framework text includes audience considerations for four groups. Those considerations are worth summarizing separately, because the same document is used differently depending on who is reading it.

Workers can use the framework to understand what AI literacy means for their career and how to build these skills proactively. The framework suggests starting by identifying routine tasks in current work, such as drafting emails, summarizing reports, or organizing data, and experimenting with an AI tool to complete those tasks. That comparison, between the AI output and how the worker would normally approach the work, shows where AI saves time and where human oversight and judgment are especially important. As confidence grows, workers can explore AI tools relevant to their specific industry, look at job postings to see how employers are describing AI related expectations, and seek training that builds from foundational literacy toward role specific proficiency. Note the caution here. Looking at job postings is a way to learn employer language, not proof that a particular course will lead to a particular job. Postings describe what employers say they want. Hiring decisions depend on many other factors, and no framework can guarantee an outcome.

Employers can use the framework to build AI literacy across their workforce, preparing employees to work effectively and responsibly with AI tools. The framework suggests starting by reviewing current workflows where AI tools are emerging, such as drafting reports, analyzing data, or responding to customers, and assessing how basic AI literacy can help employees work more effectively with those tools. Employers should identify specific tasks where AI can augment employee capabilities, determine what level of AI literacy different roles require, encourage simple hands on practice built around common workplace tasks, provide staff with clear internal guidance on appropriate AI use, and identify roles that may require deeper proficiency. Industry associations can also use the framework to create shared approaches or support member organizations in building AI ready workforces. The employer version of the framework is therefore a workforce planning tool, not a purchasing list. It helps an employer decide what to teach, to whom, and in what order, before deciding which vendor or platform to use.

Education and training providers can use the framework to integrate AI literacy into their programs, equipping learners with skills to succeed in AI enabled workplaces. Many providers recognize the need to integrate AI literacy but may be uncertain about what content to teach, how to assess competency, or how to keep pace with rapidly evolving technology. The framework provides a common foundation to help providers design effective instruction across different programs. Community colleges, online learning platforms, Registered Apprenticeship programs, and other providers can use it to guide curriculum development, whether integrating AI literacy as a standalone module or embedding the content within existing learning pathways. Providers can start by reviewing existing curricula to identify where AI literacy content can be integrated, then provide learners with hands on experience using AI tools to complete tasks relevant to their field of study, add examples tied to specific industries or job roles, incorporate exercises that build judgment about AI outputs, and create clear pathways from foundational literacy to deeper role specific skills. Partnering with local employers can help providers understand which AI tools and applications are most relevant to regional labor market needs.

State and local agencies can use the framework to advance AI literacy across the public workforce and education systems, preparing students and job seekers for evolving labor market needs while addressing employer demand for AI ready workers. The framework helps agencies integrate AI literacy into Workforce Innovation and Opportunity Act services and other programs, while adapting to regional labor market needs. Agencies can start by assessing how workers and employers in their region are adopting AI tools, identifying opportunities to integrate AI literacy content into existing workshops, orientations, or career navigation services, working with local training providers and employers to align content with regional industry needs, ensuring staff are equipped to discuss AI tools with customers, and identifying how AI literacy can strengthen reemployment, upskilling, and talent matching efforts. The DOL industry driven skills training fund article in this series explains one funding context readers may encounter when agencies discuss training investment, although funding rules and availability change and should always be checked in the current program announcement, not assumed from an earlier article.

A Related Starting Point: The Text Message Course

One concrete example helps show how the framework can reach workers who do not have easy access to classroom training. A related starting point described in a California Employment Development Department notice dated 3 April 2026 is Make America AI Ready, a free text based course. That notice says individuals can enroll by texting READY to 20202, that phone numbers collected during enrollment are used only to deliver the course and are not shared or sold, and that the seven day course delivers short daily lessons by text message, requiring about 10 minutes a day. It was developed through a partnership between the Department of Labor and Arist to ensure accessibility for individuals who may not have computers or high speed internet. Topics listed in that notice include what AI is and what it can and cannot do, how AI can support everyday work, how to give effective instructions to AI tools, how to evaluate AI generated information, and how to use AI responsibly and securely, with optional resources to continue building AI skills or explore AI related career paths.

That course maps closely to the framework’s content areas, and it also illustrates the prerequisites principle in action: a text message format removes the need for a laptop or a broadband connection. Two cautions still apply. First, this article reports the course as described in the April 2026 notice. Availability, enrollment details, and content may have changed since then, so readers should confirm current details through the Department of Labor before relying on the enrollment instructions. Second, a seven day introductory course is a starting point, not the whole framework. It can build familiarity and confidence, but the framework’s deeper skills, especially evaluation and responsible use in a specific job, need practice in that job’s real tasks after the course ends.

Using the Framework as a Personal Checklist

A worker does not need to wait for an employer or a college to adopt the framework. The five content areas and seven delivery principles can be turned into a personal audit with five questions. First, can I explain in plain language how the AI tool I use most often works, including why it can be confidently wrong? Second, can I name three tasks in my own job where AI helps, and one task where it should not be used without close human review? Third, can I write a clear prompt with context, audience, and desired format, and then improve the result in a second pass? Fourth, can I check an AI output against a trusted source, spot a gap or a logical error, and decide whether it fits the purpose? Fifth, do I know what data I must not enter, what my workplace policy says, and who I would tell if I saw misuse? If the answer to any question is no, that content area is the next learning priority, and the delivery principles suggest how to learn it: by doing a real task, in context, with feedback, and with a next step planned.

A short practice plan can follow that audit. Week one can focus on principles and uses: try the tool on two routine tasks, compare the output with personal work, and note where it helped and where it misled. Week two can focus on directing and evaluating: rewrite the same prompt three ways, check each output against a trusted source, and keep the version that survives checking. Week three can focus on responsible use: read the workplace AI policy, list the data types that must stay out of external tools, and practice redrafting a sensitive draft without pasting the sensitive content itself. Week four can focus on a next step: choose one role specific skill, such as a tool used in the worker’s industry, and find a follow up course or workplace project that uses it. That plan is general guidance for using the framework, not a program prescribed by the Department of Labor. Its value is that it follows the framework’s own principles: hands on, contextual, judgment centered, and continuing.

Questions to Ask Before Choosing a Course or Provider

The framework also gives buyers a useful set of questions. Does the course cover all five content areas, or does it teach prompting only and call that literacy? Does it include hands on practice on real tasks, with feedback, or is it lecture only? Are examples drawn from the learner’s industry or job, or are they generic? Does it teach evaluation and responsible use with the same weight as generation? Does it check prerequisites, such as digital literacy and device access, and offer support where needed? Does it state a clear next step after the foundation, and does it explain how its content is updated as tools change? Does it train managers or trainers as well as front line workers, where a workplace rollout is involved? A provider that answers those questions clearly is already aligning with the framework, even if it never names it. A provider that cannot answer them, or that promises a guaranteed job outcome from a basic literacy course, should be treated with caution. For job search uses specifically, our guide to find a job using ChatGPT explains what AI tools can and cannot do in applications, resumes, and interview preparation, and it is a useful reality check alongside any course marketing.

Limits, Gaps, and Honest Criticisms

A fair explanation of the framework must include what it does not settle. First, it is general by design. It provides a common foundation across industries, roles, and contexts, and it explicitly allows flexibility and adaptation. That generality is a strength for a national starting point, but it means the framework alone cannot tell a hospital, a factory, or a school district exactly what to teach. Industry specific guidance is an area the Department says it welcomes input on and may expand into. Until such guidance appears, providers must do the contextual work themselves, and two programs that both claim alignment with the framework can still differ greatly in quality.

Second, the framework does not define assessment in detail. It supports the evaluation of high quality content, and it names judgment, verification, and accountability as skills, but it does not set a national test, a passing score, or a credential. That leaves an open question for employers and educators: How will they know that a worker has actually reached the foundational level? Portfolios, observed tasks, and checked outputs are more informative than a multiple choice quiz about AI vocabulary, but the framework does not mandate any particular method. Buyers should therefore ask how a provider assesses learning, rather than assuming that completion of a course equals competence.

Third, the framework’s emphasis on generative AI, while understandable given current workplace use, can narrow attention if read carelessly. Other AI systems, including decision support tools, data analysis systems, and automated scheduling or screening tools, also affect workers, and their risks are not always the same as those of a text generator. A thorough program should at least acknowledge those systems and explain that evaluation and accountability apply to them too, even when the examples focus on generative tools.

Fourth, access barriers remain. The framework names digital literacy, device access, and broadband as prerequisites, but naming a barrier does not remove it. A worker without a suitable device, a stable connection, or confidence with new software still needs a concrete route into training, and that route depends on local provision, employer support, and public programs that vary by place. The text message course is one attempt to lower the barrier, but it is an introduction, not a substitute for supported, job specific practice.

Finally, the framework will age. Its own agility principle applies to itself. Tools, capabilities, and labor market needs will change, and the Department has said the framework will evolve. Any article, course, or curriculum that treats the February 2026 version as permanent risks becoming outdated. The practical response is to treat the framework as a living reference: check for updates, review examples against current tools, and keep the underlying questions, about accuracy, judgment, and accountability, even as the tools change.

FAQ

What is the US DOL AI Literacy Framework?

It is voluntary guidance issued by the US Department of Labor on 13 February 2026 through Training and Employment Notice No. 07-25. It defines AI literacy, sets out five foundational content areas, and sets out seven delivery principles for AI education and skill development training in workforce and education systems.

Is the framework a law that employers must follow?

No. The framework provides voluntary guidance for program design. It does not impose a new legal requirement by itself. Other laws, workplace policies, contracts, or state rules may still apply to a particular employer or situation, but those duties come from those sources, not from TEN 07-25.

What are the five content areas?

They are understand AI principles, explore AI uses, direct AI effectively, evaluate AI outputs, and use AI responsibly. Together they cover what AI is, where it is used, how to prompt it, how to check its output, and how to use it within safe and accountable boundaries.

What are the seven delivery principles?

They are enable experiential learning, embed learning in context, build complementary human skills, address prerequisites to AI literacy, create pathways for continued learning, prepare enabling roles, and design for agility. Together they describe how training should be delivered so that it is practical, relevant, and able to stay current.

Does completing AI literacy training guarantee a job?

No. AI literacy is a foundation that can help a worker use AI tools more effectively and discuss AI related expectations with employers, but hiring depends on many factors, including role requirements, experience, and employer decisions. No basic literacy course should be sold as a guarantee of employment or earnings.

Who is the framework for?

It is written for workers, employers, training providers, teachers and faculty, state and local agencies, and other workforce and education system stakeholders. Workers can use it to plan their own learning, employers and providers can use it to design programs, and agencies can use it to align training with regional labor market needs.

How does AI literacy differ from AI proficiency or AI fluency?

The framework treats literacy as a foundational level that all workers and students should have. Proficiency and fluency are sometimes used to imply deeper levels of mastery, and sometimes as alternative words for the same concept. The practical point is to define the specific skills and depth needed for a particular role, rather than relying on a single label.

Where can I start learning right away?

A practical first step is to audit personal skills against the five content areas, practice on a real routine task, check the output against a trusted source, and then choose a follow up course or workplace project that adds role specific practice. Readers who want a low barrier introduction can also check current details of the Department of Labor’s Make America AI Ready text message course, while confirming that its enrollment details are still current before relying on them.

Will the framework change?

The Department of Labor presents it as a starting point that will evolve based on stakeholder input, advances in AI capabilities, and changes in labor market dynamics. Readers and providers should check the current DOL advisory page for TEN 07-25 before treating the February 2026 version as the latest word.

Sources & references

• US Department of Labor, Employment and Training Administration, Training and Employment Notice No. 07-25, The US Department of Labor's Artificial Intelligence Literacy Framework, 13 February 2026, advisory page at dol.gov/agencies/eta/advisories/ten-07-25, opened 4 November 2026.
• US Department of Labor, Attachment I to TEN 07-25, The Department of Labor's Artificial Intelligence Literacy Framework, full framework text as archived by the National Transportation Library, opened 4 November 2026.
• California Employment Development Department, Workforce Services Information Notice WSIN25-24, DOL Free Artificial Intelligence Literacy Course, 3 April 2026, describing the Make America AI Ready text message course, opened 4 November 2026.