DOL alignment
Human-First AI Literacy + Human × AI Fluency.
Your foundation for the DOL framework—and what comes next.
Together, Human-First AI Literacy and Human × AI Fluency meet or exceed all five content areas and all seven delivery principles in the U.S. Department of Labor’s AI Literacy Framework, in our documented self-assessment.
Met or exceeded
Met or exceeded
Human-First AI Literacy + Human × AI Fluency.
HumanSkills’ self-assessment of curriculum and delivery design. The DOL framework is voluntary guidance; this is not federal certification or DOL endorsement.
The two programs in AI Foundations
Human-First AI Literacy and Human × AI Fluency make up AI Foundations. Together, they meet the DOL framework and give people a path to keep developing.
Human-First AI Literacy builds understanding and safe, intentional use. Human × AI Fluency develops repeatable quality, verification, and responsibility for real work. Together, they address every part of the framework.
Understand what AI can do, where it fails, and what you bring. Use it for practical tasks while keeping your thinking, voice, and judgment active.
Choose worthwhile work. Get dependable results. Check what matters and explain why the work is reliable.
AI Agility and the programs that follow build on this foundation. Their curriculum is additional development, beyond the two-program assessment below.
01 / The content standard
Five content areas, addressed by Human-First AI Literacy and Human × AI Fluency. Evidence for every one.
Choose a content area to see the curriculum evidence and the reason for its rating.
Understand how AI generates responses, where it fails, and where human judgment belongs.
| Evidence theme | Where it appears in AI Foundations |
|---|---|
| Pattern-based generation and variable outputs | Literacy1.1 What AI Actually Is · 1.3 How AI Learns and Where It Fails Fluency1.1 From Lucky to Repeatable |
| Capabilities and input formats | Literacy3.1 Directing AI for Getting Things Done · 3.2 Directing AI to Actually Learn · 3.3 Directing AI for Writing with You · 3.4 Directing AI for Creating Compelling Presentations Fluency3.5 More Than You'd Ever Type: Voice, Files, and Screenshots |
| How AI learns and produces responses | Literacy1.1 What AI Actually Is · 1.3 How AI Learns and Where It Fails |
| Hallucinations and accuracy limits | Literacy1.3 How AI Learns and Where It Fails Fluency4.1 Building a Verification Instinct |
| Human contribution and oversight | Literacy2.4 What You Bring to AI Fluency1.2 Automatable, Augmentable, Irreducibly Human |
Learners connect a plain-language understanding of AI with practical decisions about its capabilities, limits, and human oversight.
Explore useful applications across everyday tasks and the learner’s own recurring work.
| Evidence theme | Where it appears in AI Foundations |
|---|---|
| Everyday tasks, learning, and communication | Literacy3.1 Directing AI for Getting Things Done · 3.2 Directing AI to Actually Learn · 3.3 Directing AI for Writing with You · 3.4 Directing AI for Creating Compelling Presentations |
| Choosing suitable work | Fluency1.1 From Lucky to Repeatable · 1.2 Automatable, Augmentable, Irreducibly Human |
| Applications in the learner’s context | Fluency3.1 Making Sense of What You Didn't Write · 3.3 Rehearsing High-Stakes Communication · 3.4 Briefings, Meetings, and Follow-Through · 3.6 Turning Material Into Work Products |
| Thinking and new uses for familiar work | Fluency3.2 Thinking It Through With AI · 3.5 More Than You'd Ever Type: Voice, Files, and Screenshots |
Learners explore several forms of AI support, select useful applications, and keep the purpose and decisions in human hands.
Give AI clear direction, improve the exchange, and test whether an approach produces reliable work.
| Evidence theme | Where it appears in AI Foundations |
|---|---|
| Clear requests and prompting | Literacy2.1 How to Design Basic AI Prompts Fluency2.1 Asking AI to Improve the Ask |
| Context, constraints, and examples | Literacy2.2 Telling AI What It Needs to Know Fluency2.2 Writing It Once: Structure and Examples |
| Iterative improvement | Literacy2.3 The Art of Conversation With AI Fluency2.3 Checking You Got What You Asked For |
| Purposeful dialogue and richer input | Fluency3.2 Thinking It Through With AI · 3.3 Rehearsing High-Stakes Communication · 3.5 More Than You'd Ever Type: Voice, Files, and Screenshots |
| Repeatability tested in practice | Fluency1.1 From Lucky to Repeatable · 2.2 Writing It Once: Structure and Examples · 4.3 Demonstration: Reliable Work on Real Work |
Why we rate this exceeds: learners move from writing clear prompts to testing a repeatable approach on their own work and explaining what made the result reliable.
Check whether AI did the task, verify what matters, and account for the quality of the finished work.
| Evidence theme | Where it appears in AI Foundations |
|---|---|
| Accuracy and source-grounded understanding | Literacy1.3 How AI Learns and Where It Fails Fluency3.1 Making Sense of What You Didn't Write · 4.1 Building a Verification Instinct |
| Completeness, clarity, and instruction adherence | Fluency2.3 Checking You Got What You Asked For |
| Bias, context, and human judgment | Literacy1.3 How AI Learns and Where It Fails · 2.4 What You Bring to AI Fluency1.3 Using AI on Work That Matters |
| Reader fit and demonstrated accountability | Fluency3.6 Turning Material Into Work Products · 4.3 Demonstration: Reliable Work on Real Work |
Why we rate this exceeds: the assessment design culminates in a real work product the learner explains under questioning, supported by checks against the request, the source, and the stakes.
Protect information, use AI within clear boundaries, and retain the skills and accountability the work requires.
| Evidence theme | Where it appears in AI Foundations |
|---|---|
| Privacy and data boundaries | Literacy1.4 Staying Safe and Private with AI Fluency1.3 Using AI on Work That Matters |
| Disclosure, ownership, and organizational expectations | Fluency1.3 Using AI on Work That Matters Literacy3.3 Directing AI for Writing with You |
| Human decisions and responsibility | Literacy2.4 What You Bring to AI Fluency1.2 Automatable, Augmentable, Irreducibly Human · 4.1 Building a Verification Instinct · 4.3 Demonstration: Reliable Work on Real Work |
| Preserving human capability and attention | Literacy3.2 Directing AI to Actually Learn Fluency4.2 Protecting the Skills That Give You Leverage |
Why we rate this exceeds: responsible use includes deliberate practice of human capability, clear limits on delegation, and a requirement to account for the work produced with AI.
Responsible & Ethical AI develops these practices further as a separate companion program. It is not required for the AI Foundations rating.
Literacy = Human-First AI Literacy · Fluency = Human × AI Fluency. Evidence themes organize our crosswalk; they are not additional DOL requirements.
02 / The delivery standard
Seven delivery principles. A learning experience designed to make capability visible in practice.
The DOL framework calls for contextual, experiential learning, complementary human skills, and continued development. Here is how Human-First AI Literacy, Human × AI Fluency, and their delivery model put those principles to work.
Apply the learning to real tasks, then explain what made the work reliable.
Learners apply AI to practical tasks throughout Human-First AI Literacy, then bring their own recurring work into Human × AI Fluency. The Fluency demonstration asks them to produce a work product and explain what made it repeatable, what they verified and why, and what remained their contribution.
Why we rate this exceeds: a real-work demonstration makes the learner’s reasoning and contribution part of the assessment design.
Curriculum: Literacy 2.1–3.4 · Fluency 1.1, 2.1–3.6, and 4.3 Demonstration: Reliable Work on Real Work.
Let the learner’s goals and real work shape the practice.
Practice begins with the learner’s goals and develops through their own documents, recurring tasks, unresolved problems, meetings, and work products. Virgil, the Agentic Learning Guide, prompts reflection and supports practice in that context.
Learners work with situations and materials that matter to them, so the practice has a purpose beyond completing a lesson.
Curriculum: Literacy welcome and onboarding · Fluency 1.1, 3.1–3.6, and 4.3.
Develop the thinking, communication, and judgment behind effective AI use.
Human skills are named alongside technical skills in the Fluency curriculum and practiced in the work itself: understanding a source, thinking through an unresolved problem, preparing for a difficult conversation, serving a reader, calibrating trust, and standing behind a result.
Why we rate this exceeds: named human capabilities connect to dedicated practice and a demonstration that examines the learner’s contribution.
Curriculum: Literacy 2.4 and 3.2–3.4 · Fluency 3.1–3.3, 3.6, and 4.1–4.3, with human skills mapped throughout.
Provide an accessible starting point and identify readiness needs.
Human-First AI Literacy starts with plain-language explanations. Both Human-First AI Literacy and Human × AI Fluency include an LMS tour and onboarding. No technical prerequisites or paid AI subscription are required. Programs are accessed through a browser, with learning in the learner’s language. Readiness intake checks access, preferences, and preparedness.
For institutional delivery, partners should plan device, connectivity, and digital literacy support alongside the course.
Curriculum: welcome, LMS tour, and onboarding in both programs. Delivery model: HumanSkills’ published access and prerequisite information.
Give people a clear route from first use to directing connected systems.
Human-First AI Literacy leads into Human × AI Fluency. After that assessed foundation, Responsible & Ethical AI deepens responsible judgment before the path continues through AI Agility, AI Workflows + Agents, Agentic Workflow Design, and Human × AI Orchestration.
Human-First AI Literacy and Human × AI Fluency establish a starting point for continued development as the learner’s work and responsibilities grow.
Curriculum and pathway: the supplied HumanSkills curriculum ladder, from Literacy and Fluency through Agility, workflows, agentic workflow design, and orchestration.
Build shared capability among leaders and the people they support.
HumanSkills’ private team cohorts bring leaders and their teams into the same organizational context, building shared language for AI use, review, and responsible practice. Leaders work with the practices they are helping their teams develop.
This principle is addressed through the team delivery model, supported by Human × AI Fluency’s attention to organizational expectations, communication, and accountable work.
Delivery model: HumanSkills’ private cohort approach. Supporting curriculum: Fluency 1.3, 3.3–3.4, and 4.3.
Teach transferable methods and keep the learning current.
Human-First AI Literacy and Human × AI Fluency teach methods that transfer across AI tools: clear direction, source-aware reading, verification, and human judgment. Both programs collect learner feedback. HumanSkills updates its programs as models change and learners identify what can improve.
Why we rate this exceeds: continuing revision is paired with transferable methods, so adaptation is part of the learning design.
Curriculum: tool-independent methods and feedback activities in both programs. Provider record: 21 program revisions since 2025, reported in August 2026; this is a program-wide figure.
This September 2026 self-assessment maps Human-First AI Literacy and Human × AI Fluency to all five DOL content areas and seven delivery principles. Curriculum evidence comes from the HumanSkills curriculum master; delivery ratings also use HumanSkills’ published delivery model and revision process. “Exceeds” identifies additional depth in practice or assessment design. The ratings describe program coverage and delivery design, not independent certification or proof of an individual learner’s proficiency.
Read DOL TEN 07-25 ↗ Issued February 13, 2026 · Content refreshed September 2026
03 / The reason to go further
Literacy builds understanding. Fluency makes it dependable practice. Together, they give people a foundation to build on.
AI Agility carries that practice into reusable collaboration systems. Workflows and agents connect the work. Orchestration brings people, AI, and systems together under human direction.
Decide what matters, when AI is useful, and how much of the work stays yours. Keep the authority to question, change course, and say no.
See responsible usePractice judgment, critical thinking, and verification. Protect the skills that let you explain and defend the work you produce.
See human skills in practiceBuild context, standards, and collaboration methods others can reuse. Carry forward what works and improve it with experience.
See reusable collaborationRecognize new possibilities as AI changes. Put the capacity you create into better decisions and work worth pursuing.
See how the program adaptsThis is the purpose of Directing Intelligence: turning AI possibility into better work and lasting value through human intent, judgment, and accountability.
Research behind the approach
Research informs how we turn the framework into learning.
These studies examine different settings and methods. Together, they support attention to collaboration, metacognition, human judgment, and the learning people retain.
A survey of 292 AI users found that collaborative AI literacy and metacognition were associated with greater self-reported benefits. The study connects AI use with the ways people direct, evaluate, and adapt a collaboration.
Sidra & Mason, 2026; first published online 2025. International Journal of Human–Computer Interaction.Atchley and colleagues argue for AI learning that deliberately develops metacognition: planning an approach, evaluating AI-generated work, and reflecting on what the learner understands.
Atchley et al., 2024. Cognitive Research: Principles and Implications, 9, 20.In a field experiment with 791 P&G professionals, individuals using AI produced work comparable in quality to two-person teams without AI. Human judgment also retained value in selecting ideas.
Dell’Acqua et al., 2026. The Cybernetic Teammate. Organization Science. The earlier 2025 working paper reported 776 complete survey records.Student interviews in a design course identified faster iteration and creative benefits, alongside risks of shallow learning and weaker reflection. These findings support paying attention to how learners use AI and what they continue to practice themselves.
Sandhaus et al., 2025; initial preprint 2024. Co-Designing with Algorithms. DIS ’25 manuscript.Our response: build human capability alongside AI capability, so people can produce better work, explain their decisions, and keep developing their own judgment.
04 / Your development path
From Human-First AI Literacy to Human × AI Orchestration.
A connected path from understanding AI, to producing reliable work, to designing and directing systems of people and AI. Each stage adds a new kind of capability.
Understand AI, its limitations, and your role in using it. Practice safe, purposeful use in everyday tasks.
Explore Human-First AI LiteracyTurn recurring work into repeatable quality. Verify at the right level and account for what makes the result reliable.
Explore Human × AI FluencyDeepen judgment about bias, privacy, verification, and accountability. Make responsible practice part of how you choose, direct, and evaluate work with AI.
Explore Responsible & Ethical AIA good AI conversation can help with one task. A Human × AI collaboration system gives you a way to do good work again.
You define the outcome, provide the right context and set the standards for good work. Then you direct the collaboration, evaluate the result and refine the approach. Keep the instructions and criteria that work, so the next task starts from experience.
That’s AI Agility. Knowing how to choose, adapt and improve your approach as the work, and AI, continually change.
Explore AI AgilityTurn recurring work into workflows and bounded agents. Set the brief, scope, permissions, and checks for work you will not watch happen.
Explore AI Workflows + AgentsDesign how people and agents work together across a process, including handoffs, data boundaries, operational readiness, and failure recovery.
Explore Agentic Workflow DesignCoordinate multiple workflows, people, and AI systems through shared standards, governed connections, responsibility, and value measurement.
Explore Human × AI OrchestrationThe numbers correspond to levels 0–5 in the eight-level Directing Intelligence competency model. The + marks Responsible & Ethical AI as a companion program. Course completion is a step in development; demonstrated capability comes from what a learner can do.
The Challenge includes Human-First AI Literacy, Human × AI Fluency, and the AI Agility core. Its additional practice develops reusable collaboration and professional judgment.
*Provider-reported figures in the August 2026 alignment record. AI Agility core time excludes Human-First AI Literacy and Human × AI Fluency.
Challenge completers receive 12 months in the Directing Intelligence Community, with continuing applied content. Community membership is part of the Challenge offering.
Explore the AI Agility Challenge