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.
"Embed Learning in Context" is the second delivery principle of the DOL AI Literacy Framework (TEN 07-25). It calls for AI literacy delivered in terms of the worker's actual job, industry, or existing training program, through industry-specific examples, occupational tasks, employer-specific alignment, program integration, and cohort-appropriate pacing.
Last verified against the DOL source: August 20, 2026. What changed
Score your program on these five checkpointsWhat the framework says
Paraphrased: AI literacy is "more impactful when it is delivered in ways that are directly relevant to the worker's job, industry, or existing training experience." Embedding "helps reduce friction, increase uptake, and reinforce how AI fits into existing workflows," and supports retention by anchoring new concepts to scenarios workers already understand.
The five example delivery approaches:
- 2.1
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.
- 2.2
Occupational tasks and workflows
Teaching AI literacy through real job functions and activities that workers perform, helping them see how AI tools support their specific day-to-day tasks.
- 2.3
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.
- 2.4
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.
- 2.5
Cohort-specific considerations
Adjusting delivery style, pace, and references to match workers' experience, familiarity with technology, or career stage to maximize relevance.
Reproduced from the public framework text under 17 U.S.C. section 105. Read the source on dol.gov.
What it means in practice
Generic AI training fails the transfer test: a worker can ace a generic course and still not see where AI fits their Tuesday. The framework's fix is proximity, examples from the learner's industry, exercises built on the learner's actual job functions, and delivery inside programs the learner is already in (apprenticeship, CTE, credentialing). Note the employer-alignment marker: for organizations, contextualization includes teaching your AI policy and your approved tools inside the training, which is also how Area 5 (responsible use) stops being abstract.
How programs fail this provision
One-size-fits-all licensing: the same course sold to a hospital system and a school district, with the same examples (usually "write a marketing email"). The framework is explicit that relevance is a design requirement, not a nice-to-have. Second failure: context claimed via a swapped logo and intro paragraph while the exercises stay generic. Inspection question: name three exercises in the course that could only belong to our sector.
The five checkpoints this provision scores on
Instruction uses the tools, use cases, and terminology of the learners' specific industry or sector.
Framework marker: Industry-specific examples
AI literacy is taught through the real job functions and activities learners actually perform.
Framework marker: Occupational tasks and workflows
Content is aligned to the organization's own systems, internal AI tools, policies, and strategic goals.
Framework marker: Employer-specific alignment
AI literacy is delivered inside an existing program (apprenticeship, CTE curriculum, credentialing, reskilling) rather than as a detached add-on.
Framework marker: Training program integration
Delivery style, pace, and references are adjusted to the cohort's experience, technology familiarity, and career stage.
Framework marker: Cohort-specific considerations
How GAGE addresses it
In AI Literacy and Professional Conduct, this provision sits in AI in the Workplace and Team Leadership and Bonus: SMB AI Adoption Path and Bonus: AI for Educators. 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 require a different course per industry?
It requires industry-relevant examples, tasks, and terminology, which a modular program can deliver through contextualized paths rather than wholly separate courses.
Where does CTE fit?
"Training program integration" is an explicit delivery approach: the framework encourages AI literacy delivered inside existing CTE curricula and Registered Apprenticeships rather than as a bolt-on.
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 cte programs
TEN 07-25 for CTE: AI Literacy Inside Career and Technical Education
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