A Lifespan Approach to AI Literacy

AI literacy may be the defining skill-building opportunity of our time. Not because AI is everywhere (though it is), but because getting this right, reaching people across ages, contexts, and life stages, means we can build a future where AI works for everyone. The question is not whether to prioritize AI literacy. It is how to build an approach expansive enough to meet people where they are.

The Department of Labor (DOL) recently released its Artificial Intelligence Literacy Framework with a companion course that enables people to build AI literacy in short learning bursts sent via text messages. These two resources add more dimension to how we define and prioritize AI skills. For educators, these federal initiatives bridge the gap between the AI literacy needs they observe in their work and the guidance from policymakers. AI literacy is officially a key component of workforce development.

Given that workforce development spans all ages, a question emerges: What does a lifespan approach to AI literacy actually look like? As AI becomes as fundamental as reading and writing, we must develop an approach that goes beyond the use of specific tools. We view contextualization, embedding AI literacy into and presenting it through the lens of the specific places where learners live, learn, and work, as key to the kind of broad approach that can address different needs.

Young People: The Future Directors of AI

AI literacy didn’t start with ChatGPT. Initiatives like AI4K12 have been influencing K-12 since 2018. Today, contextualized guidance from organizations like UNESCO, Digital Promise, and aiEDU is abundant. In practice, a critical challenge remains: moving AI out of the STEM silo.

To ensure AI literacy isn’t just an “add-on,” instruction must be integrated across all disciplines. This means learning about AI in history and art class, in addition to computer science class. The learning needs extend to teacher professional development (PD), too. Teachers’ understanding and use of AI varies widely. In our experience, PD presents durable instructional strategies that leverage AI in connection with content area knowledge, teaching skills, and a tool-neutral presentation of the technology, which helps meet a wide range of needs. As DOL’s “Effective Delivery Principles” highlight, we must prepare enabling roles, including teachers, counselors, coaches, and administrators, to be active “directors” of AI who can critically evaluate and ethically design human-centered learning experiences augmented by AI.

The Adult Workforce: Reskilling for Resilience

For the workforce, guidance on AI literacy tends to be more fragmented. While the World Economic Forum warns that AI will both create and displace jobs, the path to “reskilling” is sometimes opaque, leaving individuals, single employers, or expensive degree programs to create pathways that aren’t always connected. Adult education programs, public libraries, and literacy nonprofits have historically stepped in to fill the gap, especially with digital literacy, and oftentimes do so with limited funding.

To turn AI literacy into a true opportunity-generator, we must address three core challenges:

• Defining Digital Literacy: We cannot teach AI literacy without a clear definition of the digital literacy that comes before it. In a recent focus group, one educator remarked: “While we’re still learning to walk with digital literacy, we’re being asked to run with AI literacy.” According to reports from the National Skills Coalition, HR departments struggle to translate the digital skills of a particular role in job descriptions, often falling back on specific software names rather than transferable competencies. Addressing digital literacy as a prerequisite to AI literacy requires that employers, educators, and policymakers speak a shared language about digital skills.
• Moving Beyond Task-Based Training: Workplace training often focuses on immediate job tasks. To advance in their careers, and even to make parallel transitions, workers need a deeper conceptual understanding of AI principles. This is where adult education programs excel. They are better equipped to view and address the learner as a whole, considering immediate demands and connecting AI skills to the real-world motivations of a learner’s life. Partnerships between employers and adult education programs are a natural bridge, supporting learners’ goals for today and tomorrow.
• The Foundational “On-Ramp”: Perhaps most importantly, we must acknowledge that reading, writing, and math are the true prerequisites for AI. If a worker struggles with foundational literacy, they will struggle to “Direct AI” or “Evaluate outputs.” Adult literacy education is the essential infrastructure for AI literacy. Supporting adults who are developing foundational literacy to concurrently build AI awareness is not a stretch goal. It is a design requirement.

Older Adults: The Power of Experience

Research from the Urban Institute shows that while older workers face age unfairness and digital barriers, they possess a “wealth of experience” that AI cannot replicate. The real barrier for older adults is often access and confidence, not capability. In fact, the skills that make older workers effective, contextual judgment, domain expertise, and critical thinking, are exactly the “complementary human skills” that the Department of Labor encourages developing in its AI literacy framework. A lifespan approach recognizes that older adults aren’t just recipients of AI training; they are the evaluators who bring seasoned human perspective to AI use.

Designing for the Whole Learner

A holistic, community-driven approach to AI is vital because the stages of life are interconnected. When a grandparent and grandchild sit together to explore an AI tool, when a parent asks their teenager to explain how a chatbot works, or when a family navigates a school’s AI policy together, learning flows in every direction. Family and intergenerational contexts are not peripheral to AI literacy. They are some of its most powerful settings.

The DOL framework helps us make these connections by centering the learner in the context of work. But its principles can, and must, extend further, into the home to support intergenerational growth, into the voting booth as civic engagement, and beyond. Today’s literacies are like a tapestry, and AI literacy must be strategically woven in so that learners develop what researchers call “lifelong and lifewide” capacities: the ability to communicate, collaborate, and critically evaluate across platforms and over time.

The promise of AI literacy will not be realized through a single framework, a single course, or a single stage of life. It will be realized when an adult learner building foundational reading skills also begins to understand that AI was trained on human language and carries human bias. When a young person designing AI-powered tools has the ethical grounding to ask who it is for. When an older worker’s decades of judgment are recognized not as a limitation, but as exactly what responsible AI use requires. As defined by Long and Magerko (2020), AI literacy is about the power to communicate, collaborate, and critically evaluate. By focusing on the whole person and their journey across platforms and over time, we ensure that the future of learning and work includes everyone.

Explore the AI Literacy Landscape

For more information on the frameworks and research mentioned in this post, explore the resources below:

National & Workforce Frameworks

• U.S. Department of Labor AI Literacy Framework (2026): The primary guide for integrating AI into the American workforce system.
• Jobs for the Future (JFF) – Use Cases for GenAI: A practical toolkit for workforce development practitioners.
• AI Literacy in the Workforce Brief: A guide to foundational AI competencies across different industries.

K-12 & Youth Education

• UNESCO AI Competency Frameworks: Global standards for both students and teachers to foster responsible AI citizenship.
• OECD / TeachAI Framework: A joint initiative providing durable AI competencies for primary and secondary education.
• Day of AI: Free, hands-on curriculum developed by MIT RAISE for K-12 learners.

Older Adults & Lifelong Learning

• Urban Institute: AI and Older Workers: Research on the unique challenges and “human-skill” opportunities for workers age 50+.
• AARP / OATS AI Guide for Older Adults: A practical guide focused on using AI to simplify health, finance, and daily life.