The seven delivery principles
How a program built to the DOL AI Literacy Framework is delivered. This is where most real programs fail inspection: the content exists, but it is not experiential, not contextualized, and not maintained.
The DOL AI Literacy Framework names seven delivery principles: 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. They govern how AI literacy is delivered, and a program can teach every content area and still miss them.
Last verified against the DOL source: August 20, 2026. What changed
Enable Experiential Learning
AI literacy is most effectively developed through direct, hands-on use, 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, makes training more engaging and memorable, and lets workers immediately apply what they're learning.
- Real-world task integration
- Interactive prompt exercises
- Live feedback and iteration
- Side-by-side human comparisons
- Progressive difficulty levels
Embed Learning in Context
AI literacy becomes more impactful when delivered in ways directly relevant to the worker's job, industry, or existing training experience. Embedding learning into familiar settings reduces friction, increases uptake, reinforces how AI fits into existing workflows, and supports retention by anchoring new concepts to real scenarios.
- Industry-specific examples
- Occupational tasks and workflows
- Employer-specific alignment
- Training program integration
- Cohort-specific considerations
Build Complementary Human Skills
AI tools are amplifiers of human input, not standalone capabilities with fixed value, their effectiveness depends on the skills, knowledge, and judgment of the people who design, manage, and interact with them. AI literacy efforts work best when they demonstrate AI's augmentation of critical thinking, creativity, communication, and domain expertise.
- Critical thinking integration
- Creative development exercises
- Communication refinement
- Values-based decision scenarios
- Domain expertise amplification
Address Prerequisites to AI Literacy
AI literacy efforts can only succeed if learners have the foundational tools and access needed to engage, digital literacy skills, device access, or broadband connectivity. Programs should proactively identify and address these barriers so participants have what they need to complete training and apply AI tools confidently in daily work.
- Evaluate baseline readiness
- Integrate digital literacy skills
- Consider options for access support
- Consider bandwidth flexibility
- Acknowledge different starting points
Create Pathways for Continued Learning
Foundational AI literacy is only the starting point. Programs should establish visible routes for participants to deepen their skills, pursue specialized training, or transition into AI-related occupations, ensuring AI literacy is not a one-time event but a sustained capability that grows alongside the technology.
- Advance to AI proficiency
- Encourage builder and entrepreneurship pathways
- Design stackable learning models
- Offer occupation-specific progressions
- Support pathways into AI-related careers
Prepare Enabling Roles
AI literacy efforts are more successful when the people supporting workers, managers, trainers, mentors, 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 that reflect their unique roles in enabling others.
- Train-the-trainer models
- Manager upskilling
- Career navigation support
- Peer learning champions
- HR and L&D alignment
Design for Agility
AI technologies evolve at a pace unlike previous workplace tools, new capabilities, platforms, and use cases emerge every few months. 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.
- Continuous content updates
- Feedback-driven iteration
- Modular content design
- Responsive use case selection
- Outcome-driven iteration
GAGE (Global Academy of Generative-AI Education) is a private education company, not affiliated with or endorsed by the U.S. Department of Labor. DOL does not certify or endorse training programs. Framework summaries are drawn from the public TEN 07-25 document. Read TEN 07-25 on dol.gov. Last verified: August 20, 2026.