U.S. – The U.S. Department of Labor’s Artificial Intelligence Literacy Framework

On 13 February 2026, the U.S. Department of Labor issued a voluntary framework on AI Literacy. This framework serves as a resource for program design and encourage AI literacy training across the public workforce and education systems. The Department believes a cooperation between the public workforce and education systems are essential for future talent development and the reindustrialisation of America. This blogpost will cover key initiatives from the framework.

The U.S. Department of Labor (hereafter ‘Department’) introduced a voluntary framework that enables companies and education platforms (such as schools and vendors) to effectively integrate AI literacy into educational and workforce contexts. This should help accelerate AI skill development for American students, teachers, job seekers, and workers.

The framework's main idea is that, in an increasingly AI-driven economy, baseline AI literacy skills will be necessary for every worker to succeed, regardless of their industry or occupation. The U.S. emphasizes the empowerment of citizens to effectively use AI, whereas the European Union considers the current AI literacy obligation a tool to ensure AI is safely deployed across the European Union.

The framework aims to support a wide variety of AI literacy efforts and emphasizes that programs should have the flexibility to choose an approach that will lead to the best outcome for AI skill development in their context. Similar to the European AI Act (Article 4), there is no one-size-fits-all approach to AI literacy. In other words, consider the framework a starting point to be finetuned.

The framework provides a working definition for AI literacy, a detailed set of and examples for each of the foundational content areas and delivery principles for AI education and skill development training.

AI literacy is defined 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'. Please note that the ability to evaluate is not explicitly included in the EU AI literacy obligation. However, one could argue that it is implicitly covered by Article 4 EU AI Act.

The definition focuses mainly on generative AI, the Department acknowledges that AI covers a wide variety of technologies. But because generative AI is the most transformative and widespread in applications and when people nowadays refer to 'AI', they are often referring specifically to generative AI, the framework focuses on preparing users to use and assess the use of generative AI. This is as well another approach that Americans take. In the EU, we primarily use the AI literacy as a tool to help providers and deployers comply with the EU AI Act. For example, proper human oversight can only be achieved when personnel is trained on AI, and bias can only be detected if there is at least awareness, which can only be present with AI literacy.

Concerning ‘literacy’ in this context, this refers to a foundational level of knowledge and skill that all workers and students should have. AI literacy serves in other words as a baseline for engaging with AI tools in any job, while also acknowledging there is no one-size-fits-all approach and that specific roles could require more advanced capabilities beyond this foundational level.

Section 1: Foundational Content Areas of AI Literacy

(1) Understand AI Principles

Goal: Users must develop a clear grasp of what AI is and how it works. This foundation helps to demystify AI and empower users to apply and evaluate AI systems more effectively.

Example: Output of generative AI can be hallucinated and there are accuracy limits, which makes it crucial to avoid overreliance by verifying results.

(2) Explore AI Uses

Goal: Users must recognize when and how to apply AI effectively, and where human input remains essential.

Example: AI can be seen as a decision-support system, AI can be used to generate recommendations, risk assessments or forecasts that help inform and augment human decision-making.

(3) Direct AI Effectively

Goal: Users must understand how to interact with AI systems in order to gain useful and relevant results.

Example: AI interactions must be seen as an ongoing process, using follow-up prompts to finetune results.

(4) Evaluate AI Outputs

Goal: Users must be able to evaluate whether an output is accurate, complete, and appropriate for the task. This evaluation skill ensures that the human-in-the-loop is in control of the process and AI is solely a support tool and not the final authority.

Example: Users must learn the reflex to cross-check AI-generated output against trusted sources or known information to identify false claims, outdated references, or hallucinated content. Users must as well be in the position to spot missing steps, flawed logic and faulty assumptions that make output unreliable or misleading.

(5) Use AI Responsibly

Goal: Users need to know how to use AI appropriately by understanding the boundaries of appropriate use.

Example: Users remain accountable for the decisions and deliverables they produce with AI-powered tools and should avoid treating AI responses as authoritative without human review. In other words the human-in-the-loop is the final decision maker.

Section 2: Delivery Principles of AI Literacy

(1) Enable Experiential Learning

Goal: The authors underline that AI literacy is learned by doing, through direct, hands-on use. Through trial and error users will learn how to work productively with AI. This will also make trainings more engaging and more memorable.

Example: Offering exercises where users receive feedback on AI outputs encourages experimentation and reinforces learning by doing.

(2) Embed Learning in Context

Goal: If AI literacy is applied to a certain context the impact of it will be more powerful.

Example: By teaching AI literacy through real job functions and activities, this will help users to see how those tools can support their specific day-to-day tasks.

(3) Build Complementary Human Skills

Goal: AI tools are amplifiers of human input, and the quality of the decisions based on the output depends heavily on the skills, knowledge, and judgement of the people who make use of it. The idea is that when users understand how to complement AI's capabilities with their own insights and instincts, they unlock far greater potential than either could deliver alone.

Example: It must be highlighted that the value of AI increases when users bring in their own subject-matter knowledge or workflow understanding to finetune results.

(4) Address Prerequisites to AI Literacy

Goal: Programs should ensure that participants have no barriers (such as no digital literacy skills) to complete the trainings on AI literacy. This will result in more people being reached and deliver better outcomes.

Example: Acknowledge different starting points and design different training programs to address this.

(5) Create Pathways for Continued Learning

Goal: It is vital to know that foundational AI literacy is only the starting point. As AI tools evolve and become more integrated into the workplace, users need to deepen their skills or pursue specialized trainings continuously. AI literacy is in other words an ongoing process.

Example: Create stackable learning modules, training programs should range from very basic to more advanced skills.

(6) Prepare Enabling Roles

Goal: Equip people who support workers, such as managers, with the right knowledge and tools to guide others effectively.

Example: Train-the-trainer models: Equip people who support others with targeted AI literacy content and methods to deliver, reinforce, and contextualize learning for others.

(7) Design for Agility

Goal: Training programs cannot be treated as a fixed curriculum and must be revised often to remain relevant over time.

Example: Build programs that allow regular refreshes of content that reflects current AI capabilities.

Want to read the full framework (and all the other examples)? Click here.

The U.S. AI Literacy Framework from the Department of Labor takes a holistic approach. It underlines the importance of integrating AI literacy programs in schools and continuing to develop AI skills throughout one’s career. The main goal of the framework is to empower and inspire stakeholders to take advantages of the possibilities that AI offers, particularly generative AI. The framework highlights the idea that AI benefits organisations when used responsibly, with human input remaining crucial to unlock the full potential of AI tools. This approach differs from that of the EU AI Act (which is a law and not just a framework) and which focuses primarily on ensuring the safety of AI deployment.

Shannen Verlee