What Two Learning Circles Taught Us About AI Readiness in Adult Education

July 20, 2026

In spring 2026, IDEAL Consortium Learning Circles tackled AI readiness from two angles: one focused on educators' foundational digital and AI skills, the other on how AI is reshaping workforce-relevant "durable skills" for their adult learners. Two groups, two starting points, one shared question: what does it take to prepare adult education programs, teachers and learners alike, for a world where digital skills are assumed and AI is already built into the tools and tasks people engage with every day? Meeting regularly through the spring of 2026, participants surfaced a handful of themes that anyone doing professional development, policy, or program design in adult education will likely recognize.

Existing frameworks help, but none quite fit

Both groups were asked to consider what skills frameworks might be leveraged or adapted to define the skills needed for "AI readiness." What came up again and again wasn't about any one framework, but a shared understanding of things a framework needs to do to be usable:

• It has to meet people where they are: flexible enough that someone just getting curious about AI and someone already deep into it can both find a starting point.

• It has to speak in familiar teaching language rather than AI jargon, connecting back to what good instruction already looks like instead of introducing a whole new vocabulary.

• It has to focus on the "what”,   concrete skills rather than staying abstract about delivery methods.

Measured against that list, the two most-cited references, the Department of Labor's (DOL) AI Literacy Framework and the Digital Promise framework, each cover part of it. DOL's framework is recognizable and practical enough to align with workforce partners, but by its own definition centers on generative AI specifically, leans more on how to deliver AI instruction than how the AI literacy content areas actually look in practice. Given its origin, many practitioners are also seeking more guidance regarding industry-specific application of generative AI. Digital Promise takes a broader view of AI and offers more comprehensive skill categories, but, like DOL, wasn't built with adult education's starting conditions in mind.

The field needs the AI conversation to get specific

Rather than trying to "teach AI" as a general subject, the practical starting point that surfaced again and again was to pick a specific thing you want educators or learners actually doing with AI, then work backward to the skill and considerations that use requires. One circle put this into practice directly: for a use case like revising or adapting existing content with AI, the group wrote a concrete "I can" statement — I can check licensing restrictions and know what I can and can't reuse when working with generative AI — tied to a specific skill and a specific ethical consideration (in this case, copyright). Starting from the use case and building outward, rather than starting from a framework and hoping it covers the use case, is what made the exercise concrete instead of abstract.

Policy is still mostly missing

The participants noted that AI policies are being rapidly developed in departments of education across the country, but little of that work specifically addresses AI use for older youth, adults, and older adults. Most states described having some version of "AI guidance, not policy," often borrowed from K-12 or general state-employee rules rather than written for adult learners. Two other priorities came up just as often, though they're less about the age of the learner and more about AI use in general: ethical and transparent use, and clear guidance on what "human in the loop" actually means — less a principle to state, more a decision an educator can point to when it counts.

The few concrete examples that already exist are about governing tools, not teaching skills. For example, Pennsylvania uses their own adapted version of the Workforce EdTech AI tool evaluation checklist to vet a tool before adopting it. One Connecticut agency that supports professional development took a different approach. Rather than maintaining a list of approved or prohibited tools, it provides broad guidance that is updated as new tools emerge, emphasizing AI use that enhances learning while maintaining instructional rigor and meeting learning goals.

AI skills need to count with employers

The workforce-focused circle pushed further on what AI-era digital skills are and why they need to matter to employers, not just teachers. A useful distinction came from Which Skills Matter Now?, a report by aiEDU and the Burning Glass Institute that the group read and discussed. The report sorts skills into two categories: 1) those that should stay anchored, developed independently, regardless of what AI tools exist and 2) those genuinely transformed by AI collaboration.

That framing fed into a bigger, practical idea: teaching learners specific vocabulary to name and describe skills they're already building, so those skills read as workforce-relevant instead of staying invisible. A learner working through a research project with AI has practiced real, describable skills like evaluating sources, revising a draft, checking a tool's output for accuracy, but rarely has the words to say so in an interview unless a program has explicitly taught that language. The framing matters as much as the skill itself: as one facilitator put it, employers respond to concrete technology skills more readily than to soft skills alone, because they can see the immediate workplace need. Teaching learners to describe what they did with AI in concrete, technical terms (e.g., "verified chatbot output against original sources" rather than generic "critical thinking") puts their skills in the category employers already value.

Kentucky put that same instinct into practice with an employer checklist that adult education programs can use to survey employers on the employability, academic, and digital/technology skills that especially matter for success at their company. A tool like this can facilitate deeper partnerships with employers and inform program design. 

Adult education has done this before. As one facilitator pointed out, the field already knows how to help learners make their technical skills concrete and credible to employers — the Northstar Digital Literacy Assessment's standards lists did exactly that when digital literacy was the new skill set learners need to be able to describe. AI doesn't demand a new playbook; it invites the field to run a proven one again.

What comes next

The questions raised in the Learning Circles won't be answered by any one state working alone,  they call for growing our collective expertise:

• How do you integrate both digital skills andAI literacy into PD?

• What does "human in the loop" actually mean in day-to-day practice — not just as a principle, but as a decision an educator can point to?

• How do you help learners describe their AI-related skills in terms employers value?

We plan to build on this year's conversations at the upcoming IDEAL Institute and expand membership so more states can contribute to — and draw on — a stronger community of practice. Not yet a member? Learn more about joining IDEAL for your state and be part of where this conversation goes next.

Learn more about the IDEAL Consortium at ideal.worlded.org.