TEN 07-25 Alignment Checker
Every AI literacy program claims to cover the framework. This scores yours against it, provision by provision, and tells you which three gaps to close first.
Score your AI literacy program against all 60 checkpoints of the U.S. Department of Labor's AI Literacy Framework, five foundational content areas and seven delivery principles, in about 12 minutes. Free, no account, and your answers stay in your browser. Built from the public TEN 07-25 text.
The complete guide publishes all 5 content areas and 7 delivery principles in full, with what each one means in practice and how programs fail it. Read the guide first, or start scoring below and read the provisions as you go.
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Understand AI Principles
Learners are taught that AI generates responses by identifying statistical patterns in data, and that the same input can produce different outputs.
What DOL says
Pattern recognition and probabilistic outputs. AI systems generate responses by identifying statistical patterns in data, which can result in different outputs from the same input.
Learners can name common AI capabilities (generating text, analyzing data, recognizing images) and the input/output formats they work across (text, audio, visual).
What DOL says
Capabilities and modalities. Common AI capabilities include generating text, analyzing data, and recognizing images across different input and output formats such as text, audio, or visual content.
Learners can explain the difference between training (building the model on large datasets) and inference (the model generating outputs in real time).
What DOL says
Training and inference. Training builds the AI model using large datasets, while inference is how the model generates outputs in real-time workplace applications.
Learners are taught that AI can produce confident but incorrect outputs (hallucinations), and that verification and avoidance of overreliance are required.
What DOL says
Hallucinations and accuracy limits. AI can produce confident but incorrect outputs, making it critical to verify results and avoid overreliance.
Learners are taught that every AI system reflects human decisions about data, goals, and parameters, and where human judgment remains essential.
What DOL says
Human design and oversight. Every AI system reflects human decisions about data, goals, and parameters, requiring users to understand where human judgment is still essential.
Explore AI Uses
Learners practice using AI for productivity tasks: drafting documents, creating presentation outlines, or analyzing reports.
What DOL says
Productivity tools. Using AI to draft written documents, create draft presentation outlines, or analyze reports, allowing workers to move more efficiently through common tasks in a workflow.
Learners practice using AI for information support: answering questions, surfacing background material, or creating tailored learning content.
What DOL says
Information support. Leveraging AI to answer questions, surface relevant background information, or create learning content tailored to specific workplace needs.
Learners practice using AI for creative assistance: first drafts of copy, naming ideas, or graphic options that humans then refine.
What DOL says
Creative assistance. Generating initial drafts of marketing copy, naming ideas, graphic options, or other creative assets that workers can then refine and improve.
Learners see task-specific AI applications relevant to their field (e.g., code snippets, transcription, data-entry automation, schedule organization).
What DOL says
Task-specific applications. Applying AI to solve targeted problems such as writing code snippets, transcribing audio, automating data entry, or organizing complex schedules.
Learners are exposed to AI decision-support uses (recommendations, risk assessments, forecasts) framed as informing, not replacing, human decisions.
What DOL says
Decision-support systems. Using AI tools to generate recommendations, risk assessments, or forecasts that help inform and augment human decision-making.
Direct AI Effectively
Learners are taught to frame requests with background information, intended audience, tone, and goals.
What DOL says
Contextual framing. Providing background information, intended audience, tone, or specific goals helps shape the AI's response to better match the user's needs in different workplace scenarios.
Learners practice structured prompting: clear step-by-step instructions and specified output formats.
What DOL says
Prompting techniques. Structuring prompts clearly, using step-by-step instructions, and specifying formats or outputs allows workers to unlock more advanced or precise capabilities of the AI system.
Learners are taught when and how to supply relevant data, supporting materials, or examples to improve AI outputs.
What DOL says
Supplying relevant input data. Workers should understand when and how to include the most relevant data, supporting materials, or examples to improve the accuracy and usefulness of AI outputs.
Learners practice iterating, using follow-up prompts to clarify, refine, or reframe until outputs meet the standard.
What DOL says
Iterating on outputs. Effective users treat AI interactions as an ongoing process, using follow-up prompts to clarify, refine, or reframe results until they meet the desired standard or purpose.
Learners are shown how vague or misleading prompts degrade outcomes, and how to adjust wording to remove ambiguity.
What DOL says
Avoiding vague or misleading prompts. Workers should recognize how prompt clarity and word choice affect outcomes and adjust their approach accordingly to avoid ambiguity.
Evaluate AI Outputs
Learners practice cross-checking AI outputs against trusted sources to catch false claims, outdated references, or fabricated content.
What DOL says
Verifying factual accuracy. Workers must cross-check AI-generated outputs against trusted sources or known information to identify false claims, outdated references, or fabricated content.
Learners practice reviewing outputs for completeness and clarity, full coverage of the task, expressed in usable form for the intended audience.
What DOL says
Assessing completeness and clarity. Outputs should be reviewed to ensure they fully address the task or question and are expressed in a clear, actionable, or usable form for the intended audience.
Learners practice spotting missing steps, flawed logic, and faulty assumptions in AI outputs.
What DOL says
Spotting gaps or logical errors. Users should be able to identify missing steps, flawed logic, or faulty assumptions that may make the output unreliable or misleading.
Learners practice judging whether an output achieves the intended goal and is fit for purpose in the specific task or workflow.
What DOL says
Aligning with strategic intent. Outputs should be evaluated based on whether they achieve the desired goal, support the right message, and are fit for purpose in a specific task or workflow.
Learners are taught to layer their own expertise, context, and discretion over AI output before interpreting, using, or revising it.
What DOL says
Applying human judgment. Workers should understand how to layer in their own expertise, context, and discretion when deciding how to interpret, use, or revise AI-generated content.
Use AI Responsibly
Learners are taught what types of data must never be entered into AI tools, and how to prevent accidental disclosure of confidential information.
What DOL says
Protecting sensitive information. Workers should understand what types of data should not be entered into AI tools and how to prevent accidental disclosure of confidential information.
Learners are trained on the organization's AI-use policies, including tool- or context-specific rules.
What DOL says
Following workplace policies and rules. Users must be aware of and follow any organizational policies around AI use, including guidance related to specific tools or contexts.
Learners are taught to recognize misuse, plagiarism, impersonation, harm, and how to report issues.
What DOL says
Avoiding misuse or harm. Workers should be aware of how AI tools can be used inappropriately, whether for plagiarism, impersonation, or harm, and know how to report issues.
Learners are taught that risk varies by task, audience, and sector, and to apply greater scrutiny in higher-stakes settings.
What DOL says
Managing context-specific risks. Workers should understand how risk varies across different tasks, audiences, or sectors and apply greater scrutiny or caution in higher-stakes settings.
Learners are taught that they remain accountable for decisions and outputs produced with AI, and that AI responses are never final without review.
What DOL says
Maintaining accountability. Workers remain responsible for the decisions and outputs they produce with AI tools and should avoid treating AI responses as final or authoritative without review.
Enable Experiential Learning
Training embeds AI tools into learners' real day-to-day tasks (writing, research, scheduling), not hypothetical exercises.
What DOL says
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.
Training includes interactive prompt exercises, including deliberately poorly written prompts to show how phrasing and structure change outcomes.
What DOL says
Interactive prompt exercises. Providing practice with different types of prompts, including poorly written examples, helps workers see how phrasing, specificity, and structure affect outcomes.
Learners receive real-time feedback on their AI outputs during exercises.
What DOL says
Live feedback and iteration. Structuring exercises where users receive real-time feedback on AI outputs encourages experimentation and reinforces learning by doing.
Learners compare AI-generated work side-by-side with human-created work or their own previous output to build judgment.
What DOL says
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.
Activities are scaffolded from simple use cases to progressively more complex workflows.
What DOL says
Progressive difficulty levels. Designing training activities that begin with simple use cases and advance toward more complex workflows scaffolds learning and builds momentum.
Embed Learning in Context
Instruction uses the tools, use cases, and terminology of the learners' specific industry or sector.
What DOL says
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.
AI literacy is taught through the real job functions and activities learners actually perform.
What DOL says
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.
Content is aligned to the organization's own systems, internal AI tools, policies, and strategic goals.
What DOL says
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.
AI literacy is delivered inside an existing program (apprenticeship, CTE curriculum, credentialing, reskilling) rather than as a detached add-on.
What DOL says
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.
Delivery style, pace, and references are adjusted to the cohort's experience, technology familiarity, and career stage.
What DOL says
Cohort-specific considerations. Adjusting delivery style, pace, and references to match workers' experience, familiarity with technology, or career stage to maximize relevance.
Build Complementary Human Skills
AI exercises are paired with problem-solving work that keeps human judgment central to AI-supported decisions.
What DOL says
Critical thinking integration. Designing learning experiences that pair AI use with exercises in problem-solving, reinforcing human judgment as central to AI-supported decisions.
Learners use AI to brainstorm and generate variations, then apply their own creativity to select and refine.
What DOL says
Creative development exercises. Encouraging workers to use AI tools to brainstorm, generate variations, or remix ideas, then apply their own creativity to select, refine, or improve the results.
Learners revise AI-drafted content for tone, clarity, persuasiveness, and audience appropriateness.
What DOL says
Communication refinement. Using AI to draft content while teaching workers how to revise AI-generated material for tone, clarity, persuasiveness, or appropriateness for the audience.
Learners practice ambiguous scenarios where organizational, legal, or personal values must govern how AI output is used.
What DOL says
Values-based decision scenarios. Practicing navigation of ambiguous situations where humans must apply a combination of organizational, legal, or personal values to act on AI outputs.
Training explicitly shows how subject-matter expertise increases the value workers can extract from AI.
What DOL says
Domain expertise amplification. Emphasizing how the value of AI increases when workers bring subject-matter knowledge or workflow understanding to shape and assess results.
Address Prerequisites to AI Literacy
The program starts with a simple diagnostic of participants' digital familiarity and barriers.
What DOL says
Evaluate baseline readiness. Starting with simple diagnostics to evaluate whether participants have the digital familiarity needed to begin using AI tools effectively, and to identify any barriers.
Light-touch digital-literacy refreshers (device use, app navigation, browser tools) are available to participants who need them.
What DOL says
Integrate digital literacy skills. Offering light-touch refreshers or resources on digital literacy skills for participants who need to brush up on device use, app navigation, or browser tools.
Device or broadband gaps are addressed with practical options (public computer labs, mobile-first content, asynchronous formats).
What DOL says
Consider options for access support. Where device or broadband gaps exist, exploring practical solutions such as public computer labs, mobile-first content, or asynchronous formats.
Training materials work in low-bandwidth environments and on mobile devices where feasible.
What DOL says
Consider bandwidth flexibility. Considering training materials that are compatible with low-bandwidth environments and mobile devices where feasible.
Delivery accommodates a range of skill levels and learning speeds without assuming prior experience.
What DOL says
Acknowledge different starting points. Building delivery models that accommodate a range of skill levels and learning speeds without assuming prior experience.
Create Pathways for Continued Learning
Participants have a visible route from basic literacy to more advanced AI proficiency, including managing complex AI systems.
What DOL says
Advance to AI proficiency. Helping participants move from basic AI literacy usage to more advanced AI proficiency, including more directly managing complex AI systems.
Workers who want to build AI-powered solutions, including entrepreneurial paths, are supported.
What DOL says
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.
Training is structured in stackable layers (foundational literacy → data handling, tool configuration, prompt engineering).
What DOL says
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.
Continued learning is aligned to the tasks, tools, and responsibilities of specific roles or career stages.
What DOL says
Offer occupation-specific progressions. Aligning continued learning with the specific tasks, tools, and responsibilities associated with different job roles or career stages.
Next steps into AI-related careers (AI product specialist, prompt engineer, data analyst) are signposted.
What DOL says
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.
Prepare Enabling Roles
Instructors, coaches, or facilitators receive targeted AI literacy content and methods (train-the-trainer).
What DOL says
Train-the-trainer models. Equipping instructors, coaches, or facilitators with targeted AI literacy content and methods to deliver, reinforce, and contextualize learning for others.
Managers receive AI literacy focused on team oversight, change management, and integrating AI into daily operations.
What DOL says
Manager upskilling. Providing AI literacy focused on use cases relevant to team oversight, change management, and integrating AI tools into daily operations.
Career counselors and mentors are equipped to guide learners on AI's impact on job search, career growth, and skill needs.
What DOL says
Career navigation support. Tailoring AI literacy for career counselors or mentors so they can guide learners on how AI tools impact job search, career growth, and evolving skill needs.
Peer leaders are identified and trained as informal, accessible sources of support within teams.
What DOL says
Peer learning champions. Identifying and training peer leaders with the right framing to serve as accessible, informal sources of support and enthusiasm within teams.
HR/L&D leaders understand how to embed AI literacy across onboarding, upskilling, and internal mobility.
What DOL says
HR and L&D alignment. In a corporate setting, ensuring those leading key learning functions understand how to embed AI literacy across onboarding, upskilling, and internal mobility pathways.
Design for Agility
There is a standing mechanism for regularly refreshing tools, examples, and instructional content as AI capabilities change.
What DOL says
Continuous content updates. Building delivery systems that allow for regular refreshes of tools, examples, and instructional content to reflect current AI capabilities.
Learner feedback and real-world outcomes are systematically used to revise delivery.
What DOL says
Feedback-driven iteration. Using learner input and real-world outcomes to revise delivery methods and content based on what is working in practice.
Training is built in modular units that can be swapped, expanded, or reordered as needs and technologies change.
What DOL says
Modular content design. Structuring training in flexible units that can be swapped, expanded, or reordered as new needs or technologies emerge.
Scenarios and use cases are periodically revisited for alignment with current workplace AI applications.
What DOL says
Responsive use case selection. Revisiting and revising scenarios periodically to ensure alignment with the latest workplace applications of AI.
The program evaluates whether participants gain practical, transferable AI skills, and adapts based on the results.
What DOL says
Outcome-driven iteration. Evaluating whether participants are gaining practical, transferable AI skills and using those insights to adapt and refine delivery strategies.
Answer all 60 checkpoints to score. 60 to go, and your answers are saved in this browser as you work.
This self-assessment is an educational tool built by GAGE.Academy. It is not affiliated with, endorsed by, or certified by the U.S. Department of Labor. DOL does not certify or endorse training programs or assessment tools. Scores reflect your own self-reported answers against the public framework text. Your answers stay in this browser unless you ask for the report by email. Framework text verified August 20, 2026. Read the complete guide.
Questions about this tool
Is this an official DOL assessment?
No. The Department of Labor publishes no assessment and no certification under TEN 07-25, and it does not certify or endorse training programs or tools. This is an independent self-assessment built by GAGE from the public framework text, and every checkpoint traces to an example content area or delivery approach in that document.
Is the DOL AI Literacy Framework mandatory?
No. The framework is voluntary guidance issued February 13, 2026. It creates no legal requirement, certification, or enforcement mechanism. Its practical weight comes from funding alignment through TEGL 03-25, which encourages WIOA funds for AI skills development, and from its role as the federal design reference that procurement language is starting to borrow.
What happens to my answers?
They stay in your browser's local storage so you can close the tab and come back. Nothing is sent anywhere unless you ask for the report by email, in which case the email address and the scores are what travel. The share link carries scores in the URL itself, not on a server.
How is the score calculated?
Each provision scores the sum of its five answers out of 10, expressed as a percentage. The overall score is the mean of the twelve provision scores, so the seven delivery principles carry more weight than the five content areas, exactly as the framework is shaped. All twos is 100, all ones is 50, all zeros is 0.
Last verified against the DOL source: August 20, 2026. What changed