Attachment I (Accessible PDF).pdf

ETA Advisory File Text

I-1 ATTACHMENT I The Department of Labor s Artificial Intelligence Literacy Framework Introduction Artificial Intelligence AI is rapidly reshaping the economy and transforming how work gets done. From offices to manufacturing floors hospital wings classrooms and more AI tools are being adopted across sectors changing how tasks are completed and how decisions are made. In an increasingly AI-driven economy every worker will need baseline AI literacy skills to succeed regardless of industry or occupation. The Department of Labor DOL or Department is issuing this AI Literacy Framework to provide a common foundation to guide AI literacy efforts for workers employers training providers teachers and faculty state and local agencies and other workforce and education system stakeholders. The framework identifies foundational content areas and delivery principles that can inform program design while allowing for flexibility and adaptation across industries roles and contexts. This framework builds on the Trump Administration s broader commitment to ensuring American workers are prepared for an AI-driven economy. It directly supports DOL s role in implementing the AI Action Plan and America s Talent Strategy both of which identify AI literacy as a foundational priority to expand across the workforce and education system. It also complements the Department of Education s ED guidance on the use of AI including the July 2025 Dear Colleague Letter and the Secretary s supplemental grantmaking priority on advancing AI in education to develop an AI-ready workforce. DOL intends to support a wide variety of AI literacy efforts and believes programs should retain the flexibility to define the specific approach that best drives measurable AI skill development outcomes in their own contexts. At the same time DOL believes that initiatives incorporating the foundational content areas and delivery principles outlined in this framework may be especially effective in equipping the American workforce to deliver on the Administration s reindustrialization agenda. In line with this framework s principle of agility DOL is putting this framework forward as a starting point while also acknowledging that this framework will evolve over time based on stakeholder input advances in AI capabilities and changes in labor market dynamics. Defining AI Literacy DOL 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. The Department recognizes that AI covers many different technologies from older rule-based systems to machine learning models and computer vision tools. And while these systems do not all operate the same way or use the same methods the most transformative and widespread I-2 applications of AI are generative AI tools. For that reason when many people refer to AI they are often referring specifically to generative AI. This framework reflects that reality by placing primary emphasis on preparing workers to understand and use generative AI tools. The Department also recognizes the multiple meanings of literacy in this context. It should be understood to mean a foundational level of knowledge and skill that all workers and students should have as AI becomes embedded across the economy. AI literacy serves as the baseline for engaging with AI tools in any job while acknowledging that many roles will require more advanced capabilities beyond this foundational level such as managing and building AI systems. Some discussions of AI literacy use terms like AI proficiency or AI fluency . In some cases these terms are being used to imply different levels of mastery while in other cases the terms are being used as alternative language of the same concept. In most workplaces indicating a need for AI literacy is not enough on its own employers and other stakeholders may need to define the specific AI skills and depth of knowledge or levels of proficiency appropriate for each role and context. Despite these variations DOL believes that starting with a clear foundation for understanding AI literacy in the context of the workforce and education systems is essential to advance the America First worker agenda. Audience Considerations This framework is designed to support a wide range of users working to strengthen AI literacy across the American workforce. While the foundational content areas and delivery principles are broadly applicable the way they are applied will vary depending on the role setting and goal of each stakeholder. The following considerations offer additional clarity on how different groups might use this framework and outline practical flexible ways to move this work forward. Workers Workers can use this framework to understand what AI literacy means for their career and how to build these skills proactively. While some workers may be concerned about whether AI will impact their job security foundational AI literacy can equip workers to work effectively alongside AI tools unlocking new productivity and opportunities for career advancement. This includes current workers adapting to AI-enabled changes in their jobs job seekers navigating an evolving labor market and students preparing to enter an economy increasingly shaped by AI. Workers can start by identifying routine tasks in their current work such as drafting emails summarizing reports or organizing data and experimenting with an AI tool to complete those tasks. This helps workers compare the AI output with how the worker would normally approach their work noticing 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 out training opportunities that build on foundational AI literacy toward role-specific proficiency. These steps position workers to remain competitive in an evolving job market and take on expanded responsibilities as AI becomes integrated into their field. I-3 Employers Employers can use this framework to build AI literacy across their workforce preparing employees to work effectively and responsibly with AI tools. As AI tools become embedded in daily operations employers need workers who can use these technologies responsibly and effectively. This includes employers onboarding new hires upskilling current employees to work alongside AI tools and ensuring managers can effectively guide AI adoption across teams. Industry associations can also use this framework to create shared approaches or support member organizations in building AI-ready workforces. Employers can start 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 these tools. Employers should identify specific tasks where AI can augment employee capabilities and determine what level of AI literacy different roles require. Employers can 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. These steps help employers maintain competitive advantage improve operational efficiency and position their workforce to leverage AI tools as the technology continues to evolve. Education and Training Providers Education and training providers can use this framework to integrate AI literacy into their programs equipping learners with the 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. This framework provides a common foundation that helps providers design effective AI literacy instruction across different programs. Community colleges online learning platforms Registered Apprenticeship programs and other providers can use the framework 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. Providers can add examples tied to specific industries or job roles incorporate exercises that build judgment about AI outputs and create clear pathways for learners to progress 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. These steps help providers deliver instruction that enhances graduate employability meets industry demand and positions programs as responsive to emerging workforce needs. State and Local Agencies State and local agencies can use this framework to advance AI literacy across the public workforce and education systems preparing students and job seekers for evolving labor market I-4 needs while addressing employer demand for AI-ready workers. This framework helps agencies integrate AI literacy into Workforce Innovation and Opportunity Act WIOA services and other programs while adapting to regional labor market needs. This includes state and local workforce development boards serving workers and job seekers through training and career services helping employers hire talent prepared for AI-enabled workplaces and supporting training providers and sector partnerships. Agencies can start by assessing how workers and employers in their region are adopting AI tools and identifying opportunities to integrate AI literacy content into existing workshops orientations or career navigation services. Agencies can work with local training providers and employers to align AI literacy content with regional industry needs ensure staff are equipped to discuss AI tools with customers and identify how AI literacy can strengthen re-employment upskilling and talent matching efforts. Working with local employers and sector partnerships can help agencies understand which AI skills are most in-demand and where to focus training investments. These steps help agencies improve employment outcomes strengthen employer partnerships and demonstrate responsiveness to emerging labor market needs. What Comes Next In line with this framework s principle of agility DOL is releasing this initial version of our framework to establish a clear starting point while also committing to evolve it over time to ensure continued relevance. The pace of AI advancement demands this approach it is important for all workforce and education stakeholders to examine whether existing processes across the system are capable of keeping pace with the speed at which AI is transforming the economy. This framework provides near-term guidance for stakeholders while signaling that the framework will be regularly updated based on technological advances labor market changes and implementation feedback. This version of the framework reflects extensive input from employers training providers state and local agencies and other workforce and education stakeholders. Moving forward DOL will continue engaging with this broad network to refine the framework and guide related AI literacy efforts. In particular DOL welcomes input on how the framework can be expanded to include industry-specific guidance that supports even stronger integration into incumbent worker training Registered Apprenticeships career and technical education education and training programs across the K-20 landscape and other workforce-related programs. DOL will also explore ways to identify and share promising AI literacy models tools and training approaches that help translate this framework into action. DOL will continue to welcome input from across the workforce and education systems on what AI literacy efforts are working where barriers remain and what types of future guidance would be most valuable. Given the large number of companies that signed the White House pledge to provide free AI education resources DOL intends to collaborate with these and other partners to ensure these efforts reach American workers as effectively as possible. I-5 Detailed View of the Department of Labor s AI Literacy Framework Section 1 Foundational Content Areas of AI Literacy 1. Understand AI Principles A foundational component of AI literacy is developing a clear grasp of what artificial intelligence is and how it works. For workers this does not require technical mastery but it does require the vocabulary and mental models needed to understand how today s AI tools operate. This foundation helps demystify AI supports more confident and accurate use and enables workers to apply prompt and evaluate AI systems more effectively across a wide range of workplace scenarios. Examples of content areas include 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. 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. Training and inference Training builds the AI model using large datasets while inference is how the model generates outputs in real-time workplace applications. Hallucinations and accuracy limits AI can produce confident but incorrect outputs making it critical to verify results and avoid overreliance. 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. 2. Explore AI Uses A core element of AI literacy is understanding how AI is being used across real-world workplace settings. Workers benefit from exposure to practical applications that illustrate 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 helping workers recognize when and how to apply AI effectively and where human input remains essential. Examples of content areas include Productivity tools Using AI to draft written documents create draft presentation outlines or analyze reports allowing workers to move more efficiently through common tasks in a workflow. Information support Leveraging AI to answer questions surface relevant background information or create learning content tailored to specific workplace needs. Creative assistance Generating initial drafts of marketing copy naming ideas graphic options or other creative assets that workers can then refine and improve. I-6 Task-specific applications Applying AI to solve targeted problems such as writing code snippets transcribing audio automating data entry or organizing complex schedules. Decision-support systems Using AI tools to generate recommendations risk assessments or forecasts that help inform and augment human decision-making. 3. Direct AI Effectively A core element of AI literacy is 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 but it does require a mental model for how to frame prompts share information and iterate strategically to improve the quality of responses. Examples of content areas include Contextual framing Providing background information intended audience tone or specific goals helps shape the AI s response to better match the user s needs in different workplace scenarios. Prompting techniques Structuring prompts clearly using step-by-step instructions and specifying formats or outputs allows workers to unlock more advanced or precise capabilities of the AI system. Supplying relevant input data workers should understand when and how to include the most relevant data supporting materials or examples to improve accuracy and usefulness of AI outputs. Iterating on outputs Effective users treat AI interactions 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 Workers should recognize how prompt clarity and word choice affect outcomes and adjust their approach accordingly to avoid ambiguity. 4. Evaluate AI Outputs An essential part of AI literacy is 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 but not a final authority. I-7 Examples of content areas include Verifying factual accuracy Workers must cross-check AI-generated outputs against trusted sources or known information to identify false claims outdated references or fabricated content. Assessing completeness and clarity Outputs should be reviewed 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 Users should be able to identify missing steps flawed logic or faulty assumptions that may make the output unreliable or misleading. Aligning with strategic intent Outputs should be evaluated 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 Workers should understand how to layer in their own expertise context and discretion when deciding how to interpret use or revise AI- generated content. 5. Use AI Responsibly Responsible use of AI is a core component of AI literacy. As AI tools become more embedded in daily workflows workers must understand the boundaries of appropriate use both to safeguard information and to ensure outputs are applied ethically and effectively. This 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. Examples of content areas include Protecting sensitive information Workers should understand 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 Users must be aware of and follow any organizational policies around AI use including guidance related to specific tools or contexts. Avoiding misuse or harm Workers should be aware of how AI tools can be used inappropriately whether for plagiarism impersonation or harm and know how to report issues. Managing context-specific risks Workers should understand how risk varies across different tasks audiences or sectors and apply greater scrutiny or caution in higher- stakes settings. Maintaining accountability 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. I-8 Section 2 Delivery Principles of AI Literacy 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 re learning in ways that feel relevant and valuable to their day-to-day responsibilities. Examples of delivery approaches include Real-world task integration Embedding AI tools into day-to-day tasks such as writing research or scheduling allows workers to gain familiarity in authentic scenarios. Interactive prompt exercises Providing practice with different types of prompts including poorly written examples helps workers see how phrasing specificity and structure affect outcomes. Live feedback and iteration Structuring exercises where users receive real-time feedback on AI outputs encourages experimentation and helps reinforce learning by doing. Side-by-side human comparisons Asking participants to compare AI-generated work to human-created work or to their own previous outputs builds judgment and discernment. Progressive difficulty levels Designing training activities that begin with simple use cases and advance toward more complex workflows helps scaffold learning and build momentum. 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. Examples of delivery approaches include Industry-specific examples Aligning instruction with the tools use cases and terminology most relevant to a given sector such as healthcare manufacturing transportation or retail. Occupational tasks and workflows Teaching AI literacy through real job functions and activities that workers perform helping them see how AI tools can support their specific day-to-day tasks. Employer-specific alignment Embedding content within the systems culture and goals of a particular employer including their internal AI tools policies and broader strategic objectives. I-9 Training program integration Delivering AI literacy as part of existing Registered Apprenticeships CTE curricula short-term credentialing programs or reskilling efforts to reinforce task relevance. Cohort-specific considerations Adjusting delivery style pace and references to match workers experience familiarity with technology or career stage to maximize relevance. 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. Examples of delivery approaches include Critical thinking integration Design learning experiences that pair AI use with exercises in problem-solving reinforcing human judgment as central to AI-supported decisions. Creative development exercises Encourage workers to use AI tools to brainstorm generate variations or remix ideas then apply their own creativity to select refine or improve the results. Communication refinement Use 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 Practice navigating ambiguous situations where humans must apply a combination of organizational legal or personal values to act on AI outputs. Domain expertise amplification Emphasize how the value of AI increases when workers bring in subject-matter knowledge or workflow understanding to shape and assess results. 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 these prerequisites as integral to program design AI literacy efforts can reach more people and deliver better outcomes. Examples of delivery approaches include I-10 Evaluate baseline readiness Start with simple diagnostics to evaluate whether participants have the digital familiarity needed to begin using AI tools effectively and identify any barriers. Integrate digital literacy skills Offer light-touch refreshers or resources on digital literacy skills for participants who need to brush up on device use app navigation or browser tools. Consider options for access support Where device or broadband gaps exist explore practical solutions such as public computer labs mobile-first content or asynchronous formats. Consider bandwidth flexibility Consider training materials that may be more compatible with low-bandwidth environments and mobile devices where feasible. Acknowledge different starting points Build delivery models that accommodate a range of skill levels and learning speeds without assuming prior experience. 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. Examples of delivery approaches include Advance to AI proficiency Help participants move from basic AI literacy usage to more advanced AI proficiency including more directly managing complex AI systems. Encourage builder and entrepreneurship pathways Support workers who want to go beyond using AI tools to building their own AI-powered solutions including through entrepreneurship. Design stackable learning models Structure training in layers that build from foundational literacy to deeper skills in areas like data handling AI tool configuration or prompt engineering. Offer occupation-specific progressions Align continued learning with the specific tasks tools and responsibilities associated with different job roles or career stages. Support pathways into AI-related careers Highlight next steps for workers interested in transitioning toward AI-centric occupations such as AI product specialists prompt engineers or data analysts. I-11 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. Examples of delivery approaches include Train-the-trainer models Equip instructors coaches or facilitators with targeted AI literacy content and methods to deliver reinforce and contextualize learning for others. Manager upskilling Provide AI literacy focused on use cases relevant to team oversight change management and integrating AI tools into daily operations. Career navigation support Tailor 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 Identify and train peer leaders with the right framing to serve as accessible informal sources of support and enthusiasm within teams. HR and L D alignment In a corporate setting ensure those leading key learning functions understand how to embed AI literacy across onboarding upskilling and internal mobility pathways. 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 that 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 that programs remain relevant over time and that workers leave with skills that match the tools they will actually encounter on the job. Examples of delivery approaches include Continuous content updates Build delivery systems that allow for regular refreshes of tools examples and instructional content to reflect current AI capabilities. Feedback-driven iteration Use learner input and real-world outcomes to revise delivery methods and content based on what s working in practice. Modular content design Structure training in flexible units that can be swapped expanded or reordered as new needs or technologies emerge. Responsive use case selection Revisit and revise scenarios periodically to ensure alignment with the latest workplace applications of AI. Outcome-driven iteration Evaluate whether participants are gaining practical transferable AI skills and use those insights to adapt and refine delivery strategies