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.
"Enable Experiential Learning" is the first of seven delivery principles in the DOL AI Literacy Framework (TEN 07-25). It holds that AI literacy develops through direct, hands-on use in real tasks, with interactive prompt exercises, live feedback, side-by-side human comparisons, and progressive difficulty, rather than abstract instruction.
Last verified against the DOL source: August 20, 2026. What changed
Score your program on these five checkpointsWhat the framework says
Paraphrased: workers build confidence "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 by helping users see how their inputs shape outputs, refine their instincts through trial and error, and build a mental model for how to work productively with AI."
The five example delivery approaches:
- 1.1
Real-world task integration
Embedding AI tools into day-to-day tasks such as writing, research, or scheduling allows workers to gain familiarity in authentic scenarios.
- 1.2
Interactive prompt exercises
Providing practice with different types of prompts, including poorly written examples, helps workers see how phrasing, specificity, and structure affect outcomes.
- 1.3
Live feedback and iteration
Structuring exercises where users receive real-time feedback on AI outputs encourages experimentation and reinforces learning by doing.
- 1.4
Side-by-side human comparisons
Asking participants to compare AI-generated work to human-created work or their own previous outputs builds judgment and discernment.
- 1.5
Progressive difficulty levels
Designing training activities that begin with simple use cases and advance toward more complex workflows scaffolds learning and builds momentum.
Reproduced from the public framework text under 17 U.S.C. section 105. Read the source on dol.gov.
What it means in practice
This principle is the framework's verdict on the lecture-and-slides model: it doesn't work for AI literacy. The five approaches form a design checklist, authentic tasks, deliberate bad examples, immediate feedback, comparative judgment, scaffolding. The "poorly written examples" marker deserves attention: it is the only delivery approach in the framework that prescribes failure as curriculum. Learners should experience bad prompting producing bad output, because that felt experience is what builds the causal model.
How programs fail this provision
Video-course failure: 40 recorded lectures, a quiz, zero hands-on reps, a format that teaches about AI while the framework demands learners use AI. Second failure: sandbox-only practice on toy tasks disconnected from learners' real work (also a Principle 2 failure). Inspection question: what percentage of seat time is the learner operating an AI tool on a real task? Under this principle, that number is the program.
The five checkpoints this provision scores on
Training embeds AI tools into learners' real day-to-day tasks (writing, research, scheduling), not hypothetical exercises.
Framework marker: Real-world task integration
Training includes interactive prompt exercises, including deliberately poorly written prompts to show how phrasing and structure change outcomes.
Framework marker: Interactive prompt exercises
Learners receive real-time feedback on their AI outputs during exercises.
Framework marker: Live feedback and iteration
Learners compare AI-generated work side-by-side with human-created work or their own previous output to build judgment.
Framework marker: Side-by-side human comparisons
Activities are scaffolded from simple use cases to progressively more complex workflows.
Framework marker: Progressive difficulty levels
How GAGE addresses it
In AI Literacy and Professional Conduct, this provision sits in Practical AI Workflow Design and Prompt Engineering and Assessment and Continuous Learning. 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 experiential learning require live instructors?
No. The framework's markers (real tasks, interactive exercises, live feedback, comparisons, progressive difficulty) can all be delivered asynchronously with a well-built platform, what matters is hands-on reps with feedback, not a room.
Why do "poorly written examples" matter?
The framework includes them deliberately: watching weak prompts fail teaches how phrasing and structure drive outcomes, faster than any lecture on prompt theory.
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 education & training providers
TEN 07-25 for Training Providers: Designing AI Literacy Programs to the DOL Framework
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