Agilities — from agilities.org by the DeBruce Foundation; via Paul Fain
Help students build confidence and prepare for bright careers!
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Also from Paul Fain, see:


Unlocking Opportunity: Progress on Moving More Students Toward Good Jobs — from highered.aspeninstitute.org

Released in partnership with the Community College Research Center (CCRC), Unlocking Opportunity: Progress on Moving More Students Toward Good Jobs highlights early outcomes from the first 10 colleges in the Unlocking Opportunity network. The report demonstrates that community colleges can rapidly increase enrollment in high-value workforce and transfer pathways while reducing enrollment in or improving programs with weaker labor market and bachelor’s degree outcomes.
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The economy is shutting young adults out of career-entry jobs, analysis finds — from hrdive.com by Laurel Kalser
Artificial intelligence matters, but in a “narrow, early and age-specific way,” researchers at the Federal Reserve Bank of St. Louis said.

Dive Brief:

  • The decline in overall job openings, exacerbated by a rising demand for artificial intelligence-related skills, is causing employment opportunities for young adults aged 18 to 24 to deteriorate, according to researchers at the Federal Reserve Bank of St. Louis.
  • Between April 2023 — when the U.S. labor market was at its strongest — and December 2025, the employment rate among 18- to 24-year-olds fell by more than 2 percentage points, researchers William Rodgers, III, and Alice Kassens reported in a June 30 post. The decrease appeared primarily as higher unemployment, rather than as labor force exits, “indicating that younger workers were still searching for jobs but with fewer opportunities available,” the authors noted.
  • By contrast, there was no comparable slide for workers aged 25-64, whose employment outcomes remained largely stable, the researchers said.
 

Why recruiters can’t find workers and new grads can’t find jobs (it’s not AI) — from washingtonpost.com by Jon Marcus
Experts say a major labor shortage looms because of population shifts and a mismatch between new graduates’ skills and employers’ needs.

Recent college graduates complain they can’t find entry-level jobs because artificial intelligence is taking over.

Yet, tech recruiter Matt Walsh and other experts say the growth of AI and the struggle to find entry-level work mask a bigger problem: The United States is facing what’s projected to become the largest labor shortage in its history.

In sectors such as semiconductor production, the problem isn’t AI or too few jobs, said Walsh, CEO of the Phoenix-based search firm Blue Signal.

“It’s ridiculous,” he said. “There just aren’t enough people.”

Economists warn that the worsening labor problem, due in part to a skills shortage and population shifts, will be vast and reach beyond tech.

Among the trends that have been leading to this moment: a mismatch between the careers college graduates are pursuing and the jobs employers are struggling to fill. Far fewer students are majoring in health care fields than are needed to meet demand, for instance.

“We have pumped so many young people into business and finance” when what’s really in demand are graduates in other fields, Hetrick said. “It’s like a factory producing these workers like widgets, even though society is saying, ‘We really don’t need them.’ And the factory just keeps pumping them out.”

 

“Teachers ban it. Employers demand it.”

 


Also relevant/see:


The Shifting Career Ladder — from nafez.substack.com by Nafez Dakkak
AI is changing how work works and quietly removing the pathways through which young people learn to become experts.

AI is reshaping how people build skills, enter professions, and move along the career ladder and through the labour market.

In this conversation, I sit down with Matt Sigelmen founder of LightCast and now the President of Burning Glass Institute. Matt has dedicated his career to understanding the labor market and helping society improve the connections within in it.

Matt and I explore why people and opportunities are often only “a few skills apart,” why entry-level work may be losing its traditional role as the first rung of expertise, and why schools, universities, and employers now need to rethink the pathways that turn potential into mastery.

Educators need to be deeply aligned with what these changes are, and they need to shift the AI discourse from “how” questions to “what” questions. What do we need to teach? What do we need to keep in the curriculum?

 

The Law School Deans Driving AI Innovation in Legal Education — from natlawreview.com by Shivani Vedhere, AI & the Law Newsletter; via Colin S. Levy

Artificial intelligence is no longer a peripheral issue for legal education. It is quickly becoming one of the central questions facing law schools: how to prepare future lawyers for a profession in which AI will affect research, client counseling, litigation strategy, access to justice, and the business of law.

For decades, law schools treated legal technology as an elective or a niche interest for students already inclined toward innovation. That era is ending. Law firms are adopting AI tools at scale and even investing in developing their own tools. Clients are asking harder questions about efficiency, cost, and competence. Courts are sanctioning lawyers and litigants for AI-generated hallucinations, with the number of identified cases in the United States now exceeding 1,000. Students entering the profession will be expected to keep up with this rapidly changing landscape.

The most forward-looking law schools are responding accordingly. That transformation is being driven in large part by a group of innovative law school deans who are treating AI not as a passing trend, but as a structural change in legal education.

These initiatives signal a broader shift in legal academia where law schools are no longer merely debating whether AI belongs in the curriculum. The more pressing question is how deeply, how early, and how responsibly AI should be integrated into legal education.

 

Instructional Design Trends: What’s Shaping The Future Of Learning? — from elearningindustry.com by Christopher Pappas

Table of contents

1. Why Instructional Design Is Entering A New Era
2. The State Of Instructional Design Today
3. Top Instructional Design Trends Shaping 2026
4. The Future Of Instructional Design And Technology


Also from elearningindustry.com, see:

The Future Of Personalized Learning And The Leaders Being Trained To Deliver It — by Ryan Ayers

Table of contents

1. Personalized Learning For Future Leaders
2. Where Personalized Learning Is Heading
3. What Implementing Personalized Learning At Scale Actually Requires
4. The Educational Leaders Being Trained To Deliver This Future
5. Conclusion

 

Artificial Intelligence and the Future of Entry-Level Work: A Framework for Safeguarding and Reinventing Early Career Pathways — from the World Economic Forum (weforum.org) and PwC

Artificial intelligence (AI) is reshaping how organizations hire, develop and advance talent, and this is most visible at entry-level. Globally, more than one in three young workers are employed in occupations with medium to high exposure to AI-driven task change. How these roles evolve will have significant implications for organizational performance, workforce participation and economic mobility.

 

Will learning curated by employers replace degrees? — from universityworldnews.com by Louise Nicol

If universities do not future-proof their offer through deeper and more credible partnerships with employers and industry, what exactly prevents employers from educating and training people themselves?

This is why the future of higher education depends on far deeper and more operational partnerships with industry. Not symbolic advisory boards or occasional guest lectures but genuine co-design of curricula, shared ownership of applied projects and clear accountability for graduate capability.

Universities that integrate live industry problems, cross-faculty collaboration and work-based learning into the core of their programmes make themselves harder to replace. Those that acknowledge the existence of external learning platforms and deliberately build them into a broader educational journey strengthen rather than weaken their position.

The real risk for universities is not replacement but marginalisation. Employers will not abandon universities out of hostility or ideology. They will do so pragmatically if universities fail to add distinctive value beyond what employers can now deliver themselves.

 

The Current State of Play: AI in Higher Education and the Road Ahead — from er.educause.edu by Tanya Gamby, David Kil, Rachel Koblic, Paul LeBlanc, Mihnea Moldoveanu and George Siemens

The conventional explanation for this strategic vacuum points to the speed of technological change; it is moving too fast for institutions built for deliberation. That is true. . . and incomplete. The deeper issue is cultural. In fairness to higher education, many industries are struggling to keep up with the pace of AI advances. Higher education, however, moves even more slowly and is not built for the kind of transformational speed now underway. Getting institutional stakeholders to engage, rethink the work, and move faster may be the central challenge facing presidents and chancellors today, and that’s saying a lot in such volatile times.

From DSC:
I highlighted this paragraph because it hits upon the key item involved here — culture. “The deeper issue is cultural.” I think that’s a very true statement.

Part of the culture and setup of many institutions includes giving faculty members full rein of their classes and their departments. Faculty members have a great deal of leeway and power in how they do things. So trying to get X faculty members to get on board — including the Department Chairs — is not an easy task. 

Another part of culture involves being willing — or not — to change in the first place. Some institutions are like Google and are used to making changes and being more innovative. But those institutions are not the norm, at least in my experience. And this doesn’t even address another topic the article mentioned — the pace of these changes. As the authors point out, most institutions of traditional higher education are not equipped to deal with the current pace of change (nor are most of our other types of institutions and our corporations as well). 

I’m going to end this posting with another brief excerpt from the article:

Institutions rooted in human relationships, committed to truth-seeking, and oriented toward the full development of persons play a central role. AI cannot manufacture the experience of mattering to another human being. It cannot model intellectual courage or ethical discernment. It cannot build the kind of community in which students discover who they are and what they believe.

These are not small things. They are, in fact, the things most worth doing. At their best, colleges and universities are not only preparing better workers but shaping individuals and strengthening society.

 

From DSC:
I used to be able to bring up Firefly on the web and use it “free” of charge — I didn’t have to go purchase tokens or credits. (I was actually paying for the Adobe Creative Cloud Pro suite of tools…so it wasn’t really free.)

But the other day I was trying to figure out what the latest pricing is at Adobe with that suite of tools and the use of credits for AI-based features. They say Adobe Creative Cloud Pro users get 4000 credits a month. Well, I have that suite and I’m still getting prompted to purchase credits. Firefly for individuals runs from $9.99 (2,000 credits/month) to $139.91 per month (50,000 credits per month). Not inexpensive, right? Below are other items along these lines.


The Era of Affordable AI Is Over. What Comes Next? — from builtin.com by Ameya Kanitkar
AI providers are shifting to usage-based billing for their services. AI fluency is more important now than ever to make the most of your tools to avoid unnecessary spending.

Summary: The era of cheap, flat-rate AI is ending as providers shift to usage-based billing. Every prompt now carries a direct cost, turning casual use into major budget risks, as seen when Uber depleted its 2026 AI budget in four months. Leaders must now track real-time value and token efficiency.

For a brief window, companies had access to the most transformative technology in a generation at the cost of a streaming subscription. Tools like ChatGPT put AI within reach of anyone with a browser and time for experimentation, while GitHub Copilot came in at just $10 a month, with token costs remaining relatively low. In the beginning, experimentation felt cost-effective, easy and relatively low-risk. 

But that era is ending, and the bill is coming due faster than a lot of enterprise leaders anticipated. 


The Fable of AI in Education — from downes.ca by Stephen Downes
Marc Watkins, Rhetorica, Jun 17, 2026

Tokenomics will be a hot topic of discussion on university campuses because, as Marc Watkins notes in this article, there is no realistic path forward to providing all students with access to advanced AI.


From this posting on LinkedIn.com from Dr. Nick Jackson:

And now there is a third layer emerging. Institutions are waking up to a systems-level question they are likely not remotely prepared for. Who pays for AI? How are budgets managed when there are unclear token consumption pricing models? How is AI procured? Who decides what tools get used and by whom and who gets access and at what level?

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If AI Eats the Entry-Level Job, Where Do Young People Learn to Work? (Ryan Craig, Achieve Partners) — from humanistxyz.substack.com by Allison Dulin Salisbury; via Ryan Craig
“The public should not be subsidizing colleges whose students lack relevant, paid, in-field work experience.”

That is the trap at the center of this conversation: everyone wants to hire someone with three years of experience, and almost no one wants to provide those three years.

And Ryan’s policy prescription is unusually concrete: pay employers to hire and train apprentices, following the countries that have scaled apprenticeship far faster than the U.S.; require colleges receiving federal student aid to provide relevant, paid, in-field work experience; and build a market of intermediaries that can make the whole thing operational.

Ryan’s view is that higher education remains critically important. But college without meaningful work experience may become a much worse bet, especially for students who cannot afford to guess wrong.

 

The Evolving L&D Roles in 2026 Exploring who you might become next — from liftedlnd.substack.com by Lifted L&D

1. The Learning Experience Architect
This is really the evolution of the instructional designer. The difference is that the focus is no longer on building individual courses. Instead, the focus shifts towards designing capability ecosystems.

In modern learning platforms, learning is dynamic and increasingly personalised. AI engines infer skill levels, recommend resources, generate practice scenarios and adapt content based on how people engage. The role of the Learning Experience Architect is to orchestrate that environment so it genuinely supports capability development.

Across all of these emerging roles, three themes keep appearing.

The first is data fluency. …
The second is systems thinking. …
The third is human judgement.


Also relevant/see:


 
 
 


Rethinking Learning Design in Elementary Schools — from edcircuit.com
Why K–5 leaders must redesign—not just adopt—technology to restore attention, deepen thinking, and align AI with how children actually learn

Rethinking learning design in elementary schools is critical as screen time and AI reshape attention, thinking, and student engagement.

Designing for Thinking, Not Just Doing
At its core, learning design must shift from task completion to thinking development.

This requires creating environments where students:

  • Spend time processing ideas
  • Work through confusion without immediate answers
  • Build persistence through challenge

It also requires clarity around the role of technology.

Technology should:

  • Extend thinking
  • Provide meaningful feedback
  • Support exploration

It should not:

  • Replace effort
  • Short-circuit reasoning
  • Eliminate productive struggle

The goal is not to reduce technology use.

It is to ensure that students remain the ones doing the thinking.


Should We Integrate AI into Our Teaching?: Evidence-Based Guidelines for Deciding When AI Belongs — from Faculty Focus by Norman Eng, EdD

Four Questions for Deciding Whether to Use AI

Question 1: Will this AI tool help students use, recall, and demonstrate understanding of core disciplinary content?
Question 2: Will this AI tool require students to apply their learning to a new context?
Question 3: Will this AI tool support—not replace—independent, evidence-based reasoning?
Question 4: Will this AI integration preserve meaningful human interaction?


 
© 2025 | Daniel Christian