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.
"Create Pathways for Continued Learning" is the fifth delivery principle of the DOL AI Literacy Framework (TEN 07-25). It positions foundational AI literacy as "only the starting point" and requires programs to build visible routes toward advanced proficiency, specialized training, stackable credentials, occupation-specific progressions, and AI-related careers.
Last verified against the DOL source: August 20, 2026. What changed
Score your program on these five checkpointsWhat the framework says
Paraphrased: as AI tools evolve and integrate into work, workers "will need clear opportunities to deepen their skills, pursue specialized training, or transition into AI-related occupations." Programs should make AI literacy "not a one-time event but a sustained capability that grows alongside the technology."
The five example delivery approaches:
- 5.1
Advance to AI proficiency
Helping participants move from basic AI literacy usage to more advanced AI proficiency, including more directly managing complex AI systems.
- 5.2
Encourage builder and entrepreneurship pathways
Supporting workers who want to go beyond using AI tools to building their own AI-powered solutions, including through entrepreneurship.
- 5.3
Design stackable learning models
Structuring training in layers that build from foundational literacy to deeper skills in areas like data handling, AI tool configuration, or prompt engineering.
- 5.4
Offer occupation-specific progressions
Aligning continued learning with the specific tasks, tools, and responsibilities associated with different job roles or career stages.
- 5.5
Support 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.
Reproduced from the public framework text under 17 U.S.C. section 105. Read the source on dol.gov.
What it means in practice
The framework draws a bright line between literacy (the baseline for everyone) and proficiency (role-specific depth), and this principle is the bridge. For program designers, "stackable" is the operative word: each layer must credential something real and point at the next layer. For employers, this principle answers the "then what?" question that kills training ROI, a literacy program with no visible next step trains workers who promptly take their new skills to an employer who has one. For districts and colleges, the occupation-specific progression marker maps directly onto CTE pathways design.
How programs fail this provision
Dead-end courses: a one-off AI awareness class with no next rung, no credential logic, and no connection to role-specific depth. The framework's language, "visible routes", is the test: the pathway must be perceptible to the learner at the moment they finish the foundation, not buried in a catalog. Second failure: pathways that exist only toward vendor lock-in rather than toward occupations. Inspection question: what does a graduate do next, and is that step named inside the course they just finished?
The five checkpoints this provision scores on
Participants have a visible route from basic literacy to more advanced AI proficiency, including managing complex AI systems.
Framework marker: Advance to AI proficiency
Workers who want to build AI-powered solutions, including entrepreneurial paths, are supported.
Framework marker: Encourage builder and entrepreneurship pathways
Training is structured in stackable layers (foundational literacy → data handling, tool configuration, prompt engineering).
Framework marker: Design stackable learning models
Continued learning is aligned to the tasks, tools, and responsibilities of specific roles or career stages.
Framework marker: Offer occupation-specific progressions
Next steps into AI-related careers (AI product specialist, prompt engineer, data analyst) are signposted.
Framework marker: Support pathways into AI-related careers
How GAGE addresses it
In AI Literacy and Professional Conduct, this provision sits in Assessment and Continuous Learning and Advanced AI Literacy. Every topic in the program ends in a mastery assessment, and the teach-back gate asks the learner to explain the material back in their own words before it counts as understood, which is the difference between a program that covers a checkpoint and one that can evidence it.
Module titles come from the program registry, so this cannot drift away from what the program contains. GAGE is a private company: the program is built to the DOL framework, and DOL neither certifies nor endorses it or any other program.
Questions about this provision
Does the framework define "AI proficiency"?
It distinguishes literacy (the foundational baseline) from proficiency (deeper, role-specific capability, up to managing and building AI systems) and leaves proficiency definitions to employers and stakeholders per role and context.
What AI careers does the framework name?
AI product specialists, prompt engineers, and data analysts are the named examples, alongside builder and entrepreneurship pathways.
Does your program actually meet this provision?
Answer the five checkpoints above and the other 55, and get a dated report that scores your program provision by provision. Free, and your answers stay in your browser.
What this means for community colleges
TEN 07-25 for Community Colleges: The DOL Framework as Your AI Literacy Curriculum Spec
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.
Last verified against the DOL source: August 20, 2026. What changed