Understand AI Principles
A clear grasp of what AI is and how it works, not technical mastery, but the vocabulary and mental models needed to understand how today's AI tools operate. This foundation demystifies AI, supports more confident and accurate use, and enables workers to apply, prompt, and evaluate AI systems effectively across workplace scenarios.
"Understand AI Principles" is the first of five foundational content areas in the DOL AI Literacy Framework (TEN 07-25, February 13, 2026). It requires that workers learn how AI actually works, probabilistic pattern-matching, capabilities and modalities, training versus inference, hallucinations, and human design decisions, without requiring technical mastery.
Last verified against the DOL source: August 20, 2026. What changed
Score your program on these five checkpointsWhat the framework says
The framework's summary, paraphrased: a foundational component of AI literacy is a clear grasp of what artificial intelligence is and how it works. Workers do not need technical mastery; they need "the vocabulary and mental models needed to understand how today's AI tools operate." This foundation "helps demystify AI, supports more confident and accurate use, and enables workers to apply, prompt, and evaluate AI systems more effectively across a wide range of workplace scenarios."
The framework names five example content areas:
- 1.1
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.
- 1.2
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.
- 1.3
Training and inference
Training builds the AI model using large datasets, while inference is how the model generates outputs in real-time workplace applications.
- 1.4
Hallucinations and accuracy limits
AI can produce confident but incorrect outputs, making it critical to verify results and avoid overreliance.
- 1.5
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.
Reproduced from the public framework text under 17 U.S.C. section 105. Read the source on dol.gov.
What it means in practice
A worker who has absorbed this area behaves differently in three observable ways: they are not surprised when the same prompt yields a different draft on Tuesday (probabilistic outputs); they never paste an AI claim into a client document unverified (hallucinations); and they can explain to a colleague, in plain language, why the tool is confident and wrong at the same time (training vs. inference + human design). Notice what is absent: math, model architecture, code. The framework deliberately sets the bar at mental models, because mental models are what change behavior at scale.
How programs fail this provision
The most common failure is tool-tutorial training: "here is where the button is" sessions that produce operators who trust the tool precisely because they never learned what it is. Second most common: vendor webinars that demonstrate capabilities (Area 2) and skip limitations entirely. The inspection question for this provision: can a graduate explain why the AI was confidently wrong last week? If not, Area 1 was never taught, whatever the slide deck claimed.
The five checkpoints this provision scores on
Learners are taught that AI generates responses by identifying statistical patterns in data, and that the same input can produce different outputs.
Framework marker: Pattern recognition and probabilistic outputs
Learners can name common AI capabilities (generating text, analyzing data, recognizing images) and the input/output formats they work across (text, audio, visual).
Framework marker: Capabilities and modalities
Learners can explain the difference between training (building the model on large datasets) and inference (the model generating outputs in real time).
Framework marker: Training and inference
Learners are taught that AI can produce confident but incorrect outputs (hallucinations), and that verification and avoidance of overreliance are required.
Framework marker: Hallucinations and accuracy limits
Learners are taught that every AI system reflects human decisions about data, goals, and parameters, and where human judgment remains essential.
Framework marker: Human design and oversight
How GAGE addresses it
In AI Literacy and Professional Conduct, this provision sits in AI Fundamentals and Digital Foundations. 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 "Understand AI Principles" require coding or math?
No. The framework explicitly says technical mastery is not required, it asks for "the vocabulary and mental models needed to understand how today's AI tools operate."
Why does the framework lead with this area?
Because every later skill, prompting, evaluating, responsible use, depends on knowing the tool produces probabilistic, fallible, human-designed output. DOL presents it as the foundation that "demystifies" AI and enables confident, accurate use.
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 workers, job seekers, and students
TEN 07-25 for Workers: What the DOL AI Literacy Framework Means for Your Career
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