workplace

Want to Close the Skills Gap? Bring Learning Closer to Work — from campustechnology.com by Elise Awwad

Key Takeaways

The skills gap is a workforce access problem as much as a talent problem: Employers broadly plan to upskill workers, yet many employees still lack sufficient time or access to AI training.
AI fluency compounds where workers already have access: Employees who use AI regularly build confidence and capability, while those whose jobs keep them farther from the technology risk falling further behind.
Upskilling works better when learning is integrated with work: Employers and higher education should create pathways that combine technical and durable skills with on-the-job application, rather than treating reskilling as an individual responsibility.

The skills employers say they cannot find are increasingly the skills they expect employees and candidates to arrive with, fully formed, at their own expense.
…
Upskilling or reskilling is often framed as an individual responsibility: a matter of initiative, of staying current. But if employers need these skills, they have a stake in making sure workers have a realistic way to develop them.

[Gifted article] The unexpected toll of searching endlessly for a new job [Carpenter]

On 10/08/2026, in business, career development, Daniel S. Christian, Emotion, engagement / engaging students, expectations, hearts -- matters of the heart, society, sociology, student-related, United States, vendors, workplace, by Daniel Christian

The unexpected toll of searching endlessly for a new job — from washingtonpost.com by Julia Carpenter [this is a gifted article]
“Ghost job” postings, AI interviewers and a lack of in-person connection can take a toll on your mental health.

A prolonged job search doesn’t just sap job seekers of time, money and energy — for many, the hunt takes a devastating toll on their mental health.

Although unemployment rates in the United States have been historically low, the Bureau of Labor Statistics reports that more than a quarter of jobless people qualify as “long-term unemployed,” meaning they have been hunting for 27 weeks or more.
…

“A prolonged job search isn’t simply a logistical challenge,” she said. “It erodes a person’s confidence over time and their sense of agency, along with their financial security, obviously.”

Studies show unemployment is linked to the development of mental disorders, including anxiety and depression. Long-term job seekers describe heightened stress and exhaustion, which can make the everyday work of a job hunt feel even more burdensome, said Mindy Shoss, professor of industrial and organizational psychology at the University of Central Florida.

From DSC:
I retired at the end of 2021, so I haven’t been in any kind of job search for years now. But I went through a time of job searching back in 2017 that lasted 9 months. I had some part-time work during that time, but nothing like the long work weeks that I was used to for years. It was a highly emotional time for me. Putting in the work to apply for a job took me at least a day per job application. Over time, not hearing anything back was discouraging. It’s hard not to take things personally, even though I knew these were business decisions. (But I also strongly believed that I faced age discrimination.) Putting oneself out there in a job search can be highly grueling and discouraging.

As any good job searching firm will tell you, networking is key. Students — know that up front. Go find out about networking, and if you aren’t doing it now, start doing it as soon as you can. Start to:

Share relevant and useful information via a blog, out on LinkedIn, or via other types of social media
Develop contacts and help them out whenever possible
Connect people to each other

Don’t just initiate something with your network when you need something. Feed your network constantly. (Though I will say, my network on LinkedIn was very little help to me. It turned out that the people I knew locally came through for me the most.)

October is National Disability Employment Awareness Month [nationaldisabilityinstitute.org]

On 10/05/2026, in accessibility / UDL, Bias | Diversity Equity & Inclusion (DE&I), career development, collaboration, Daniel S. Christian, innovation, legislatures / government / legal, neurodivergent/special needs, society, workplace, by Daniel Christian

October is National Disability Employment Awareness Month — from nationaldisabilityinstitute.org

Every October, we pause to honor something that should be obvious all year long: people with disabilities are part of the American workforce, and they always have been.

This is National Disability Employment Awareness Month, or NDEAM. The 2026 theme, set by the U.S. Department of Labor’s Office of Disability Employment Policy, is “Celebrating Value and Talent.” It’s the same theme as last year, carried forward as the country marks its 250th birthday.

We love that choice. It doesn’t ask anyone to prove their worth. It starts from the truth that the value and talent are already here.

A month with a long history
NDEAM began in 1945 as a single week. Congress expanded it to a full month in 1988, and today it’s observed by employers, schools, community groups and advocates across the country.

Eighty years in, the work is far from done.

Also relevant/see:

National Disability Institute (NDI) has launched a new podcast series. — from nationaldisabilityinstitute.org

“Moving the Needle” explores the ideas, innovations and partnerships that shape NDI’s work and are defining what comes next.

Episode 1 chronicles the gaps that existed prior to NDI’s formation, the organization’s humble beginnings and how far NDI has moved the needle toward building a better financial future for people with disabilities.

From vinyl records and paper checks to QR codes and smartphone apps, the first episode takes listeners on a then-and-now journey through NDI’s history, told through personal and professional reflections from members of its leadership team.

From DSC:
The topic of disability hits close to our home. One of our daughters was born with Mosaic Down Syndrome. She is different, but wonderfully so. Literally, from day one, the LORD said that He was going to teach me through her, and He has absolutely done so. I won’t get into all the ways that that has occurred, but valuing differences is not a cliché for me. For one thing, I don’t use the word “normal” much anymore…as there is no “normal.” I might use the word typical, but not normal.

Our daughter flavors the world in wonderful ways. For example:

Even as a little girl, she would go up and give hugs to people — surprising them in wonderfully positive ways.
She thrived in preschool all the way up through 5th grade, but then struggled mightily in 6th grade and 7th grade; she then went into homeschooling in 8th-12th grade. She also went to a Christian High School and a Career Technical Center.
Within the last several years, she’s really hit her stride in school. Her note-taking and skills have jumped to a completely new level. She learns, but she learns at her own pace.
She loves to dance and to draw things. She loves to write fantastical stories.
She has successfully worked two jobs already.
She’s in a special program for college and is learning a lot — but not without challenges.
She has an imagination that is off the charts. She can see movies in her head and hear songs without issue. We’re hoping that this special program will give her access to creative writing classes, art, criminology, and other topics that she’s interested in.

The LORD has made good on His word — again. I’ve learned a lot through her. I’ve changed because of her — no doubt about it. But I’ve also learned the incredible challenges that people with disabilities face. If their unique physical and/or cognitive situations weren’t enough, the way policies are created puts them at huge disabilities that the rest of society would balk at in an instant (and our Senators and Representatives would not stand for it if this were them or someone in their families). This involves budgetary items, big time. But also, people with disabilities are often pigeonholed into certain jobs/industries. This needs to stop.

So I’m posting the above items to give more visibility to this subculture within our society, and the challenges that they face. Let’s help straighten this situation out.

Making real work visible: Credentialing Community Justice Workers with Frontline Justice [eddesignlab.org]

On 09/29/2026, in 21st century, 24x7x365 access, Access to Justice (A2J), change, communities of practice, credentialing, law schools, Legal operations, legal reform, legal technologies, legislatures / government / legal, skills, society, vision/possibilities, workplace, by Daniel Christian

Making real work visible: Credentialing Community Justice Workers with Frontline Justice — from eddesignlab.org (also educationdesignlab.org)

Frontline Justice is working toward a simple, ambitious vision: By 2035, anyone, no matter their background or location, can access the legal help they need for everyday civil issues. To move toward that future, the work is organized around two goals that run in parallel: (1) rapidly growing a new workforce of justice workers now, and (2) laying out long-term vocational paths so this role becomes a lasting and respected part of the justice ecosystem.

The AI Tools that L&D Teams Are Building (Rather than Buying) — from drphilippahardman.substack.com by Dr. Philippa Hardman
Aka, how build skills are changing how L&D thinks & works

More and more of my advisory work is no longer about helping organisations choose a vendor; it’s about helping their teams build their own AI tools, and build them well. Over the last couple of years I’ve watched several hundred L&D practitioners do it, and in this week’s post I share what they built, why, and what it tells us about what L&D actually needs from AI.

A Tool of One’s Own
So, what do these tools look like in practice? I did some analysis and the tools that L&D teams are building first fall into three categories:

1. Tools that support the work before and after the course
2. Tools that make the learning experience more dynamic
3. Tools that do L&D tasks to the team’s own methods, processes and standards

Priority 1: Getting a View and Setting a Vision Across Systems — from michaelbhorn.substack.com by Michael B. Horn, Angela Jackson, Mallory Dwinal-Palisch, and Danny Curtis

The shift required to prepare learners for a labor market defined by rapid skill change and more complex transitions cannot be accomplished by any one system. Nor is it enough for K–12, higher education, and workforce development to improve independently. They must work together—aligning programs, sharing resources, creating smoother transitions, and building models that combine academic learning with real workplace experience. Career readiness is not simply a goal these systems share. It is a shared outcome that they produce together as parts of a single talent pipeline.

For that reason, the first priority for states is not a single programmatic reform. It is creating a coherent, cross-system strategy. K–12, higher education, and workforce agencies— and the providers they oversee—need a common understanding of the current system, the future state they are trying to build, the changes required to get there, and the progress they are making toward those changes.
.

Your Next Job Interview Could Be With a Bot. Here’s How to Prepare. [Rumage]

On 09/23/2026, in Artificial Intelligence/Agents/LLMs/and Related, business, career development, change, communications, corporate / business world, productivity / tips and tricks, student-related, workplace, by Daniel Christian

Your Next Job Interview Could Be With a Bot. Here’s How to Prepare. — from builtin.com by Jeff Rumage
More employers are using AI to screen candidates before they ever speak with a recruiter. Here’s what to expect from an AI interview and how to put your best foot forward.

Summary: AI-led interviews are becoming a common early-stage hiring screen as employers use conversational AI to evaluate applicants at scale. Candidates can prepare by practicing aloud, keeping answers concise, using mock AI interview platforms and avoiding scripted responses.

New study: 15 community college presidents on what it will take to lead higher ed’s next chapter [eddesignlab.org]

On 09/19/2026, in career development, change, changing business models, community colleges, competency-based learning/education, experimentation, faculty/staff, future, future of higher education, game-changing environment, generational differences, higher education, skills, student-related, teaching & learning, United States, workplace, by Daniel Christian

New study: 15 community college presidents on what it will take to lead higher ed’s next chapter — from eddesignlab.org

Community colleges are at a pivotal moment.

As learners, employers, and regional economies change, these institutions have an opportunity to lead the next evolution of higher education — if they can move beyond structures built for a different era.

In the Lab’s new study, Insights from 15 Community College Presidents on the Future of Learning in a New Economy, leaders share their perspectives on the opportunities and barriers facing the sector, as well as the institutional changes needed to help more learners access economic mobility.

Their insights validate Education Design Lab’s Future of Learning framework, which asks five essential design questions:

How might we make skills and competencies visible?
How might we create stackable pathways that credential skills along the way?
How might we make learning universally flexible through multiple modalities?
How might we guarantee applied learning opportunities for every learner?
How might we ensure every learner has access to adequate support services?

Escaping The Backrooms Of Workforce Development — by Ryan Craig

Which makes sense. Because far too many find themselves in a liminal space, neither in school nor a career. Needing to make a living in an era of digital transformation and AI, they’re having a horrific time finding their way into and around work. So it’s not just education and training wonks who think workforce is the defining issue of our time. The success of Backrooms demonstrates Gen Z thinks so, too.

It’s happening everywhere. Young people around the world are angry at how hard it’s become to launch careers. This summer an entire protest movement arose in India – the world’s largest Gen Z population – around entrance exams for medical school, one of the few remaining sure bets. In the UK, youth unemployment is over 16%; it’s 15% across the EU and over 25% in Sweden. Back home, nearly 400,000 young Americans stopped looking for work in June alone. So it’s urgent that we identify what actually works to help young people get good first jobs.

The answer lies between apprenticeship perfection and train-and-pray scale. We desperately need a scalable training-first model that’s as proximate as possible to employment opportunities, employers, and actual employment. That can only mean work-based learning: real work experience completed during or connected with a training, certificate, or degree program. Work-based learning can take many forms: rotations, clinics, co-ops, internships, short projects. It can be integrated into coursework or independent. But what all forms require are bona fide employers, which regulate scale. Such programs can enroll only as many as can fit within the constraint of available work-based learning opportunities.

Also see:

Is Your College A Zombie? — from forbes.com by Ann Kirschner; via Ryan Craig

I want to be careful here, because this is not an argument that universities are doing nothing. They are doing an enormous amount. New programs, AI task forces, employability frameworks, microcredentials, and shifts in how they enable lifelong learning.

Activity is not the problem; what has not changed is the machinery that decides what counts. Too many institutions move in herds rather than forging an independent path. When one college launches a new program, say, data science and AI, fifty will follow without asking whether their version serves their students or simply checks a competitive box.

Meanwhile, 88% of American professionals believe colleges and universities should be the ones providing AI training, and nearly half say their employers have offered no AI resources whatsoever.¹

AI Adoption in the Workplace Accelerates, but Trust Gap Remains [Parker]

On 08/25/2026, in 21st century, 24x7x365 access, Artificial Intelligence/Agents/LLMs/and Related, business, career development, change, emerging technologies, leadership, pace of change, policy, strategy, workplace, by Daniel Christian

AI Adoption in the Workplace Accelerates, but Trust Gap Remains — from campustechnology.com by Sean Parker

Key Takeaways

AI adoption is accelerating across the workplace, with 62% of U.S. workers now using generative AI for professional purposes.
Employee concerns about AI’s impact on jobs remain high, even among workers actively using technology.
Companies are adopting AI faster than they are creating clear guidelines, raising questions around trust, leadership and workplace readiness.

A new Pulse of the Workforce Special Topic Report published by Idealis and CivicScience found that while AI adoption is accelerating across the U.S. workforce, confidence is not growing at the same pace. The report points to a central tension: AI is becoming common at work before many organizations have built the policies, training, and leadership practices employees need to use it with confidence.

We Keep Designing the Future from the Past — from LinkedIn.com by Monty Alger

“Using AI to create the practice opportunities online students need” [Miller Perkins] + other items re: AI in our learning ecosystems

On 08/19/2026, in 21st century, 24x7x365 access, adjunct faculty, adult learning, Artificial Intelligence/Agents/LLMs/and Related, assessment, business, career development, colleges, communications, communities of practice, community colleges, content development, aggregation, repositories, corporate / business world, corporate universities / corporate training, digital learning, education, education technology, emerging technologies, Emotion, engineering, experimentation, faculty/staff, game-changing environment, higher education, homeschooling/homeschoolers, human-computer interaction (HCI), ideas, innovation, instructional design, intelligent systems, intelligent tutoring, IT in HE, K-12 related, leadership, learning, learning agents, learning ecosystem, Learning Experience Design, learning spaces, liberal arts, mathematics, online learning, personalized/customized learning, productivity / tips and tricks, professional development, student-related, teachers, teaching & learning, technologies for your home, tools, training / L&D, UK, United States, universities, vendors, workplace, youth, by Daniel Christian

Using AI to create the practice opportunities online students need— from timeshighereducation.com by Kathy Miller Perkins
For disciplines that depend on interpersonal skills such as communication, leadership, negotiation and mediation, AI can provide elusive experiential learning, particularly in asynchronous environments

What if AI’s greatest educational value lies not in generating answers but in generating experiences?

The result surprised me. Students did not simply interact with the AI, they meaningfully engaged with it. Many reported that the experience felt valuable precisely because the simulated conversation partner pushed back. Unlike classmates who sometimes hesitate to challenge one another, the AI consistently maintained its position. Students had to work harder, listen more carefully and apply the communication strategies they had spent weeks learning. And, importantly, they practised.

Here’s how to show students what responsible AI use looks like — from timeshighereducation.com by Andrew Firr and Alex Fenton
Well-designed assessments can highlight generative AI’s limitations, where it can provide support and what responsible practice looks like. Andrew Firr and Alex Fenton offer strategies

We have been exploring ways to offer this in our Level 7 module for MSc engineering management students, many of whom come from the Global South. Assessment is built around a practical design problem: students examine how a real campus process operates, where delays occur, evidence they observe and how a feasible redesign might improve the system – in a 3,000-word essay.

They can use AI for planning and providing clarity, but not for generating the evidence on which the analysis rests. Invented observations, measurements, screenshots or unverifiable citations are prohibited. The assessment design reinforces this through a case study, diagram, fact-checking exercise and a short AI use statement.

Requiring an AI use statement encourages students to reflect on their processes. We ask which tool they used, how they used it, how they checked the output and which parts of the work depended on their own evidence and judgement. It reminds them that evidence must be theirs and that citations must be manually verified.

Slow math: Kids may learn more when AI makes them review mistakes — from hechingerreport.org by Jill Barshay and Kristin Fasiang
A randomized experiment involved more than 6,000 Tennessee middle school students learning fractions

In an experiment involving more than 6,000 middle schoolers in Tennessee, students learned slightly more math when an AI tutor walked them through their mistakes and then required them to demonstrate the same skill correctly three times in a row before moving on.
…
The winning combination wasn’t the addition of AI tutoring alone, but AI tutoring plus repetition, with the idea that students needed to stick with the skill to demonstrate some level of mastery. The students who practiced math with this AI-enhanced “mastery learning” approach scored about 3 percentage points higher than students receiving conventional computerized instruction. The advantage was small.

I Built a Team of AI Bots That Write Feedback Better than Me. Here’s How. — from drphilippahardman.substack.com by Dr Philippa Hardman
Aka, how to make learners love AI-assisted feedback, rather than loathe it.

This wasn’t hidden from the cohort. For reasons I’ll come to below, I always tell my cohorts at the very start that an AI assistant will be helping me draft their feedback. The aim wasn’t to remove me from the process, but to find out whether AI could help me to deliver feedback at a volume and quality that I simply couldn’t maintain without AI.

Spoiler: the experiment worked — but not for the reason I expected. In this post, I’ll share exactly what I built, tell you how to build a version for your course and reveal the three conditions that decide whether learners trust AI-assisted feedback or quietly stop reading it.

TL;DR: the spec for my feedback assistant is long not because the model needs persuading, but because it’s where my judgement lives. Every rule in it is a decision AI would otherwise make by itself, with limited expertise. A spec doesn’t make the model smarter — it makes expertise and judgement legible enough for the model to follow.

Of the five stages of AI grief, some managers are stuck in denial— from timeshighereducation.com by Shadi Mohamed
Acceptance is not surrender. It is about getting beyond a policing obsession and rethinking what assessment aims to measure, says Shadi Mohamed

In that moment of institutional blindness, it struck me: universities are not merely struggling with a technological adoption curve. They are grieving the demise of the modern university’s economic and institutional logic.

To understand the scale of this threat posed by AI, consider what the internet did to journalism. Newspapers thrived by bundling together classified advertising with quick news hits, sports scores and opinion columns, using that reliable revenue to fund the expensive social necessity of investigative reporting. The rise of internet advertising did not kill journalism outright but it shattered the bundle, leaving the expensive core product without its historical financial engine.

Universities operate on a similar logic, bundling content delivery, assessment, credentialling, research and professional formation. But generative AI is now commodifying the most visible parts of that package. When AI can deliver personalised explanations instantly and generate the essays and reports we use to measure student capability, the traditional degree loses its role in the labour market as a proxy for understanding. If the bundle breaks, the economic model that funds our deeper purposes is in peril.

New grads have to compete with AI for entry-level roles, hiring managers say [Ewen]

On 08/10/2026, in Artificial Intelligence/Agents/LLMs/and Related, career development, change, changing business models, credentialing, skills, society, workplace, youth, by Daniel Christian

New grads have to compete with AI for entry-level roles, hiring managers say — from hrdive.com by Lara Ewen
Nearly half of organizations now ask a senior worker plus AI to do the work of several entry-level grads, per a new report.

Dive Brief:

Hiring managers in the U.S. are betting on artificial intelligence over new graduates, with 48% saying they would rather invest in AI tools than hire and train a recent college graduate, according to a Friday report from ResumeTemplates.com.
The job market does still have space for 2026 graduates, and 65% of hiring managers said they planned to hire the same number or more this year compared to last year, per the report. However, 23% said they expected to hire fewer 2026 grads this year or none at all, and 12% didn’t know how many they would hire.
Most hiring concerns centered around workplace skills rather than credentials. Nearly 70% of hiring managers said they had “at least one character concern about recent grads,” including 33% who cited “a lack of work ethic.” Another 76% said recent grads required assistance understanding basic documents such as memos, contracts and budgets.

Learning Ecosystems

October is National Disability Employment Awareness Month [nationaldisabilityinstitute.org]

Making real work visible: Credentialing Community Justice Workers with Frontline Justice [eddesignlab.org]

Categories

Tags