On February 13, 2026, the U.S. Department of Labor’s Employment and Training Administration (ETA) issued Training and Employment Notice (TEN) No. 07 25, transmitting the Department’s Artificial Intelligence (AI) Literacy Framework as a resource for program design and encouraging expanded AI literacy training.

The Department of Labor (DOL) describes the framework as voluntary guidance (not a new rule) and 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; the framework is organized into five Foundational Content Areas and seven Delivery Principles.

This update applies to private-sector employers and private education or training providers that use the Department of Labor’s Artificial Intelligence Literacy Framework for workforce training and took effect on February 13, 2026.

What Employers Need to Do

• Identify where AI is already showing up in the business operations (e.g., drafting reports, analyzing data, customer responses) and pinpoint tasks where AI can augment work product and efficiency.

• Define role-based AI literacy expectations (baseline literacy for most roles; deeper proficiency for select roles) and align training to how AI is actually used in those jobs.

• Publish clear internal guidance on appropriate AI use, including:
  • What information cannot be entered into AI tools (confidential/sensitive data)
  • When human review is required, and
  • Who is accountable for AI-assisted outputs

• Build Training Using the Framework’s “What” and “How”:
  • Cover the five content areas (principles, uses, directing, evaluating, responsible use), and
  • Deliver training using the seven delivery principles (hands-on, job-contextual, human-skill complementary, prerequisites, pathways, enabling roles, agility).

• Keep Training Agile: Make content modular and refresh use cases regularly as tools evolve and workplace adoption patterns change.

Overview

• What This Is: The DOL’s AI Literacy Framework (via TEN 07 25) is intended to guide AI literacy program design and support expanded AI education and training.

• Legal Effect: The framework is voluntary guidance and does not create new legal obligations for employers.

• Definition: The DOL defines AI literacy as competencies that enable individuals to use and evaluate AI responsibly, with a primary focus on generative AI.

Why This Matters

• AI literacy is positioned as a baseline workforce capability: The DOL states that in an AI-driven economy, workers will need baseline AI literacy skills regardless of industry or occupation, and the framework is intended to guide scalable training approaches.

• It signals “what good looks like” for employer training: Although voluntary, the framework provides a practical roadmap for what to teach and how to teach it as AI tools become part of everyday work.

• It emphasizes human accountability and evaluation: The DOL highlights responsible use and evaluation of outputs as core competencies, reinforcing that AI should support work—not replace human judgment.

Key Risks for Employers

• Sensitive Data Exposure: The framework explicitly treats privacy/security and protection of critical information as part of responsible AI use; weak guardrails increase confidentiality and data-handling risk.

• Overreliance on AI Outputs: The DOL identifies “evaluate AI outputs” as a foundational competency (verify accuracy, relevance, and fit-for-purpose); failing to train this increases error propagation and business decision risk.

• Inconsistent Adoption and Manager-driven Drift: The DOL’s delivery principles emphasize enabling roles (including managers/trainers) and contextual training; without this, teams adopt AI unevenly and may create inconsistent practices.

• Training That is Too Generic to be Usable: The DOL stresses embedding learning in context and experiential learning; purely generic training may not translate to actual job tasks, reducing effectiveness and increasing misuse.

• Access and Prerequisite Gaps Affecting Participation: The framework flags prerequisites such as digital literacy and access to devices and connectivity; ignoring these gaps can limit workforce participation and create uneven capability across teams.

Additional information

• The DOL published TEN 07 25 with the complete framework and a framework graphic summarizing the five content areas and seven delivery principles.

• The DOL notes the framework is intended to evolve over time based on stakeholder input, advances in AI capabilities, and labor market changes.

The Framework: 5 Content Areas and 7 Delivery Principles (Summary)

Section 1: Five Foundational Content Areas (the “what”)

(1) Understand AI Principles: Build a baseline understanding of how AI works (core concepts, capabilities, and limits) so workers can use AI confidently and appropriately without needing technical mastery.

(2) Explore AI Uses: Gain exposure to practical workplace use cases to recognize where AI can support tasks and where human expertise and judgment remain essential.

(3) Direct AI Effectively: Learn how to interact with AI effectively by providing clear instructions, sufficient context, and iterative refinements to produce useful outputs.

(4) Evaluate AI Outputs: Critically assess AI-generated results for accuracy, completeness, logic, and fit-for-purpose rather than treating AI as the final authority.

(5) Using AI Responsibly: Use AI in ethical and secure ways by protecting sensitive information, following policies, and maintaining accountability for AI-assisted work outcomes.

Section 2: Seven Delivery Principles (the “How”)

• Enable Experiential Learning: Teach AI literacy through hands-on practice using realistic tasks so learners can apply skills, compare outputs, and improve through feedback and iteration.

• Embed Learning in Context: Make training job- and industry-relevant by integrating AI learning into real workflows or existing programs so it is immediately actionable.

• Build Complementary Human Skills: Emphasize that AI augments human capabilities by reinforcing judgment, communication, creativity, problem-solving, and domain expertise alongside AI use.

• Address Prerequisites to AI Literacy: Reduce barriers to participation and success by accounting for prerequisites such as digital literacy and access to devices and connectivity.

• Create Pathways for Continued Learning: Treat AI literacy as a starting point and provide structured routes to more advanced or role-specific skills and AI-related career pathways.

Source Reference

• DOL – Press Release – AI literacy framework providing foundational content areas, delivery principles to guide nationwide efforts (February 13, 2026)
• DOL – Attachment I – The Department of Labor’s Artificial Intelligence Literacy Framework

Resources

• DOL TEN 07-25 – Training and Employment Notice No. 07-25
• DOL TEN 03-25 – Training and Employment Guidance Letter No. 03-25

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