Alignment with the U.S. DOL “Make America AI-Ready” Initiative

Alignment with the U.S. DOL “Make America AI-Ready” Initiative#

This book is designed to support AI literacy in the spirit of the U.S. Department of Labor’s Make America AI-Ready initiative. This chapter maps the book’s content to the initiative’s official AI literacy framework, situates it among recognized AI literacy programs, and provides language useful for faculty and workforce-development grant proposals.

About this alignment

The framework below is summarized from the U.S. Department of Labor’s “AI Ready” page (https://beta.dol.gov/ai-ready) as of June 2026. Program descriptions are summarized from the providers’ own pages. Details of government initiatives and third-party courses change; verify the current specifics before citing them in a proposal.

What is Make America AI-Ready?#

Make America AI-Ready is a free AI literacy course offered by the U.S. Department of Labor, delivered over one week as short daily text-message lessons (about ten minutes a day, at no cost, accessible from any phone). According to the Department, the initiative is “designed to ensure every American worker has the chance to learn the foundational skills to benefit from AI” (attributed to U.S. Secretary of Labor Lori Chavez-DeRemer on the DOL page). It is positioned as a starting point that points learners toward further skill-building and AI-related careers.

The initiative is organized around a 5-pillar AI literacy framework.

The five pillars and how this book covers them#

DOL pillar

What it means

Where this book develops it

Understand AI Principles

Core concepts, capabilities, and limitations of AI, the foundation for effective use.

AI and Tools Reference; Module 1 Chapter 1: Introduction to Generative AI and Chapter 2: Foundation Models and Large Language Models.

Explore AI Uses

Exploring AI tools and use cases, and how AI complements human expertise.

Use-case sections throughout Module 1, and the application patterns in Module 3 (Module 3: Building Applications with Foundation Models).

Direct AI Effectively

Providing the right context and writing clear prompts that produce effective outputs.

Module 1 Chapter 3: Prompt Engineering and Chapter 4: Advanced Prompting Techniques.

Evaluate AI Outputs

Assessing AI results for accuracy and relevance, and iterating on outputs.

Module 2 Chapter 1: Evaluating LLMs; the veracity discussion in Chapter 3: Dimensions of Responsible AI.

Use AI Responsibly

Using AI ethically and securely, protecting information, ensuring accountability.

AI Literacy and Responsible Use; Module 2 Chapter 2: Foundations of Responsible AI and Chapter 4: Improving Security and Safety.

Every pillar of the DOL framework maps onto material in this book, and the book goes further by adding hands-on Amazon Bedrock labs for each concept. In short, the DOL course is an on-ramp; this book is the deeper course a learner can take next.

The broader AI literacy landscape#

AI literacy courses equip learners to understand, evaluate, and responsibly use AI tools in daily and professional life. They typically focus on practical prompting, ethical awareness, identifying AI-generated bias, and recognizing limitations such as hallucinations, all themes this book treats in depth. Recognized programs include:

Program

Audience

Focus

IBM SkillsBuild, AI Literacy

Students and lifelong learners

A free program covering real-world use cases and hands-on business problem-solving.

AI Literacy for the Modern Workplace (Udemy)

Non-technical professionals

How LLMs work, risk mitigation, and effective prompting.

Digital Education Council, AI Literacy for All

Executives and institutions

Foundational knowledge to scale AI learning across disciplines.

LinkedIn Learning, Building AI Literacy

Professionals and teams

Curated learning paths to integrate AI into a standard skill set.

This book complements those offerings: where many AI literacy courses are tool-agnostic and conceptual, this book pairs the concepts with a specific, industry-standard implementation stack (Amazon Bedrock and LangChain) and runnable labs, making it suitable as a credit-bearing or workforce course rather than only an awareness module.

Courses aimed at working professionals#

A second tier of programs targets professionals who want practical, no-code AI skills, generative AI, prompt engineering, and responsible use, without a technical background:

Program

Focus

Google AI Essentials

A practical course on using generative AI in daily workflows, prompt generation, and task automation, no technical background required.

Google Prompting Essentials (Coursera)

A short, beginner-friendly specialization on building a library of reusable prompts and refining AI tone and output settings for productivity.

IBM, Generative AI: Prompt Engineering Basics (Coursera)

Practical techniques for tailoring AI responses and managing instructions for business use cases.

Anthropic, AI Fluency: Framework & Foundations

A structured framework for human-AI collaboration and understanding the limitations of current tools.

AI Workflow & Prompt Engineering Certification (SSGI)

A deeper, self-paced certification on designing AI processes that augment rather than replace human work.

These professional courses emphasize moving beyond basic chat to structured, scalable AI integration, building reusable prompt libraries and setting inference controls (temperature and context), which this book teaches concretely in Chapter 3: Prompt Engineering and the AI and Tools Reference.

LinkedIn Learning modules worth noting#

LinkedIn Learning’s Building AI Literacy path (about nine hours) bundles several practical modules that map directly onto this book’s prompting chapters:

  • Prompt Engineering: How to Talk to the AIs and Advanced Prompt Engineering Techniques, foundational and advanced prompting, paralleling Chapter 3: Prompt Engineering and Chapter 4: Advanced Prompting Techniques.

  • Iterate and refine your prompts, setting constraints, defining specific outputs, and pushing the AI through three to five refinement cycles to remove generic, low-effort language, the iterative loop this book stresses, combined with the effort levels idea from AI and Tools Reference.

  • Prompt Engineering and AI Agents with ChatGPT, archiving reusable chats, using custom instructions to personalize an AI library, and converting prompts into automated workflows, the same habits formalized in A Practical AI Workflow: Making AI Do the Heavy Lifting.

Note

These third-party course titles and durations are summarized from the providers’ listings and are mentioned for orientation, not endorsement; confirm current details on the platform.

A ready-to-adapt course description#

For faculty or training leads building a syllabus, the following description and learning outcomes summarize a no-prerequisite professional AI literacy course that this book can support end to end.

Sample course description

This asynchronous, self-paced course equips professionals across industries with a clear, practical understanding of artificial intelligence. No technical background is required. Through real-world examples and hands-on exploration of user-friendly AI tools, participants learn how AI is transforming the workplace and how to use it responsibly and effectively. Topics include the fundamentals of AI and machine learning, automation and augmentation in business, generative AI applications, data ethics, algorithmic bias, and prompt generation. By the end, professionals can engage confidently with AI technologies, make informed decisions about their use, and contribute to responsible AI integration in their organizations.

Learning outcomes. On completion, a learner will be able to:

  • Understand key AI concepts and how they apply in professional settings.

  • Identify AI tools relevant to their industry and evaluate their value.

  • Recognize the risks, limitations, and ethical considerations of AI use.

  • Explore the impact of AI on workforce trends, decision-making, and productivity.

  • Build foundational literacy to support informed collaboration with technical teams.

Each outcome is supported by this book: the first by the Primer and Module 1; the second by the tools reference and Module 3 use cases; the third by Module 2; the fourth by the use-case and “In the news” sections throughout; and the fifth by the hands-on labs that bridge non-technical learners toward technical collaboration.

For grant and proposal writing

When proposing AI literacy work, you can position this open-source textbook as a ready-made, standards-aligned curriculum:

  • Framework alignment. It covers all five pillars of the DOL Make America AI-Ready AI literacy framework (Understand, Explore, Direct, Evaluate, Use Responsibly), with an explicit mapping (the table above).

  • Open and reusable. It is released under CC-BY-SA-4.0 (with MIT-0 sample code), so institutions can adopt, adapt, and redistribute it at no cost, supporting broad workforce access.

  • Hands-on and assessable. It includes runnable labs and worked examples, enabling competency-based assessment rather than awareness-only outcomes.

  • Responsible-AI grounded. A full module on evaluation, responsible-AI dimensions, and security and safety addresses ethics, bias, and accountability requirements common in workforce grants.

  • Accessible on-ramp. It explicitly connects to the free, phone-based DOL course as an entry point, supporting learners with limited prior exposure or technology access.

State factual claims (dates, dollar amounts, enrollment figures, specific program features) only after confirming them against current primary sources; this book deliberately avoids inventing such specifics.

Mapping to ABET student outcomes#

For programs seeking or maintaining ABET accreditation, this book’s activities support all six Computing Accreditation Commission (CAC) student outcomes. The outcome statements below are summarized; confirm the exact current wording against the ABET Criteria for Accrediting Computing Programs.

No.

ABET CAC student outcome (summarized)

Where this book supports it

1

Analyze a complex computing problem and apply principles of computing to identify solutions.

Prompt-engineering and advanced-prompting chapters; RAG and agent design.

2

Design, implement, and evaluate a computing-based solution to meet requirements.

The Module 1-3 labs (Bedrock apps, chatbots, RAG, agents) and worked examples.

3

Communicate effectively in a variety of professional contexts.

“In the news,” worked examples, and the writing/summarization use cases.

4

Recognize professional responsibilities and make informed judgments based on legal and ethical principles.

Module 2 (responsible AI, evaluation, security and safety) and the AI Literacy primer (PII, FERPA, HIPAA).

5

Function effectively as a member or leader of a team.

Team-oriented lab and project use (e.g., shared prompt libraries, evaluation work teams in Chapter 1: Evaluating LLMs).

6

Apply computer science theory and software development fundamentals to produce computing-based solutions.

Foundation-model and transformer theory (Chapter 2: Foundation Models and Large Language Models), tokens and embeddings, and the runnable labs.

Mapping to Bloom’s taxonomy#

The book is structured so learners move up the six levels of the revised Bloom’s taxonomy, from recall to creation. Each chapter’s “Key takeaways,” worked examples, and labs target progressively higher levels.

Bloom level

Cognitive activity

How the book targets it

Remember

Recall facts and terms.

Definitions, the AI and tools reference, and “Key takeaways” summaries.

Understand

Explain ideas and concepts.

The theory sections and “How these connect” callouts throughout.

Apply

Use knowledge in new situations.

Worked examples and the hands-on Bedrock labs.

Analyze

Draw connections and compare.

Comparison tables (fine-tuning vs. RAG, Q&A vs. conversation) and the evaluation chapter.

Evaluate

Justify a decision or judge quality.

Model-selection guidance, responsible-AI risk assessment, and LLM evaluation.

Create

Produce original work.

Capstone labs: building chatbots, RAG systems, agents, and multimodal applications.

For course and grant proposals

Together, the ABET and Bloom mappings let you show that the curriculum is both accreditation-aligned (ABET CAC outcomes 1-6) and pedagogically scaffolded (Bloom’s six levels), alongside the DOL AI-literacy framework alignment above. Verify ABET outcome wording and any accreditation specifics against current ABET documents before submission.

Key takeaways#

  • The DOL Make America AI-Ready initiative defines a 5-pillar AI literacy framework: Understand AI Principles, Explore AI Uses, Direct AI Effectively, Evaluate AI Outputs, and Use AI Responsibly.

  • Every pillar maps onto this book, which extends the framework with Amazon Bedrock labs and worked examples.

  • The book complements established AI literacy programs (IBM SkillsBuild, Udemy, the Digital Education Council, and LinkedIn Learning) by adding a concrete, hands-on implementation stack.

  • Its open license and standards alignment make it well suited to cite in faculty and workforce-development grant proposals.