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: rotationsclinicsco-opsinternshipsshort 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.


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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 — 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.

 

The U.S. debt tops a record-shattering $40 trillion. Yes, with a T. — from npr.org by Scott Horsley

The federal debt topped $40 trillion Wednesday, a new high-water mark for red ink that shows no sign of receding.

The news, reported by the Treasury Department in its daily financial update, comes just five months after the debt surpassed $39 trillion. The government continues to pile up debt at a rapid clip, as spending outstrips revenue by more than $2 trillion a year.

“Our current fiscal trajectory is plainly unsustainable, and that’s the best-case scenario,” said Margaret Spellings, president of the Bipartisan Policy Center. “Even in the rosiest scenarios, we’re speeding toward a cliff and refusing to turn the wheel.”


Also related, see:

Gross National Debt Reaches $40 Trillion — from crfb.org

And other warnings signs are flashing too, with debt held by the public recently exceeding the size of our economy, the deficit-to-GDP ratio running twice as high as where it should be, and interest costs exceeding our national defense budget.

 
 

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.


 

As fewer young people choose college, this district wants to ensure they have career options — from hechingerreport.org by Ariel Gilreath
In Niagara Falls, New York, a town with shrinking job options, students are encouraged to consider their careers in ninth grade

At Niagara Falls High School, students are encouraged to consider their careers upon arrival in ninth grade. By 11th grade, every student at the school must pick from one of five pathways, ranging from business and finance to design and engineering. The shift toward career education comes as about one-third of seniors at Niagara Falls High are rejecting college and instead choosing to enter the workforce after graduation, compared with roughly a quarter a decade ago, said Superintendent Mark Laurrie, who retired in June. In a town that’s struggled with population declines, where now-shuttered manufacturing plants once dominated the local labor market, there’s an urgency to prepare high school students for careers.

“It’s important to give them hope for the future with a career pathway — a career, not a job — to have something to aspire to and, hopefully, keep them in the city and in the state,” Laurrie said.

 

 
 
 

Staffing Agencies as Apprenticeship Accelerators — from apprenticeshipsforamerica.org by Harry Leech; via Ryan Craig
Could America’s staffing industry be a catalyst for growing apprenticeship? 

Apprenticeships for America (AFA) sees the development of a mature intermediary system as crucial to scaling apprenticeships in America. There are many shapes and forms of intermediaries: spanning public sector and private, for profit and not-for-profit. One promising and relatively unexplored model is staffing agencies.

This Field Briefing reviews the landscape of opportunity for harnessing America’s $200 billion staffing industry to grow apprenticeship. It explores the fit between apprenticeship intermediary functions and capacities of staffing firms. And it offers examples of how staffing agencies todays are supporting apprenticeship activies of their customers. Ultimately, it will take policy simplification and stable funding to fully benefit from the integration of the staffing industry and apprenticeship. This report sets out how.

Download the full report here: Staffing Agencies and Apprenticeship Report

 

Digital Accessibility Lawsuits in 2026: Five Trends Companies Should Know — from blog.usablenet.com

Here are five findings companies should understand, along with practical steps for reducing risk.

1. Digital accessibility lawsuits are on pace to reach 6,000
2. Where a company sells matters more than where it is headquartered
3. E-commerce remains the primary target
…and more

 

How Explainer Video Production Turns Ideas Into Clear Stories — from bitrebels.com by Lauren Williamson

Ideas rarely arrive in a neat order. They show up as notes, claims, diagrams, feature lists, and customer questions. Explainer video production turns that raw material into a story people can process quickly. It clarifies the problem, frames the change, and shows why the answer matters. When the script, visuals, voice, and pacing work together, viewers spend less energy decoding the message and more attention on absorbing it.

Explainer video production works because it gives ideas a usable order. It turns scattered information into a story with purpose, pace, and visual evidence. The strongest results come from careful message decisions before animation starts. When script, design, voice, and timing support one central point, viewers can follow with less effort. That clarity helps organizations teach, persuade, and make it easier to remember.

 

Nikii Shaver on legal AI strategy, agentic governance, and trusted judgement — from The Geek in Review Podcast

What does legal AI value look like once speed stops serving as the headline metric? In this episode of The Geek in Review, Greg Lambert and Marlene Gebauer speak with Nikki Shaver, co-founder and CEO of  Legal Technology Jub and a member of the inaugural Financial Times Law 50. Shaver argues that law firms need to move beyond time saved toward efficacy: stronger output, stronger client outcomes, and more effective legal advice.

The conversation examines why the billable hour is far from finished yet no longer serves as the sole measure of legal value. Shaver compares hourly timekeeping to a taxi meter: useful for internal visibility, yet insufficient as the price signal for work transformed by AI. Workflow mapping, client discussions, and pricing discipline become central where an AI-enabled process compresses weeks of effort into hours.

Corporate legal departments are adopting AI at a faster pace, bringing new pressure to outside counsel. Some in-house teams see AI as a route to keep more work inside, while others see room for firms to take on work that previously sat outside budget limits. Shaver frames the strategic question around delivering more for clients, especially in practice areas where a firm holds differentiated expertise.

AI has not produced the promised empty calendar. Instead, lawyers report fuller schedules, longer documents, and a growing verification tax. Shaver flags the rise of 40-page forms, bloated redlines, and outputs that look polished yet lack sound reasoning. The episode makes a practical case for concise drafting, human review, and critical reasoning before any AI-generated material reaches a client or counterparty.

Agentic AI raises the stakes. Legal Technology Hub’s AI Agents in Law Map tracks hundreds of solutions, yet governance has not kept pace with new autonomy, connectors, and downstream system access. Shaver urges firms to establish traceability, unique identifiers, risk-based human oversight, enforceable policies, and a clear view of where data travels.

For firms aiming past baseline adoption, Shaver draws a line between routine personal use and strategic transformation. Daily use builds fluency, but competitive advantage grows from proprietary workflows, data foundations, client-facing collaboration spaces, and focused investment in the practices where a firm already excels. Her crystal-ball view is blunt: trusted judgment will become a scarce premium asset, AI-native firms will rise, and traditional firms will launch AI-native subsidiaries of their own.


Nonprofit, legal automation company design new AI tool to protect public benefits — from abajournal.com by Amanda Robert

An artificial intelligence tool from national nonprofit Frontline Justice and legal automation company Josef that aims to improve access to justice is rolling out across three states.

Frontline Q, an AI assistant that can help families navigate the complex Supplemental Nutrition Assistance Program, is now available in Arizona, Texas and Alaska. Using a combination of federal, state and local regulations and with oversight from legal aid lawyers, it offers answers to questions about the program’s eligibility and appeal rules.


CLM is a Zombie, ALSPs are In Trouble and Outside Counsel Budgets Cut in Half: Episode 54, Wordsmith.ai CEO Ross McNairn — from legallydisrupted.com by Zach Abramowitz
The understated founder of one of the hottest legal AI startups on the market makes bold predictions


Though not necessarily related to legaltech, these items caught my eye as well:

The Los Angeles Police Department (LAPD) is reportedly ending its deal with Flock Safety, a surveillance company that helps law enforcement track vehicles using thousands of its license plate cameras placed across the United States.

A senior LAPD official told news outlets, first reported by ABC7 and the Los Angeles Times, that the police department would allow its three-year contract with Flock to expire when it ends on Saturday. The department cited “serious concerns” around civil liberties and privacy. Flock’s cameras are operated by the Atlanta, Georgia-based company and not the LAPD.

To survive this nightmarish job market, candidates are now “spraying and praying,” as one career coach described it — or paying resume services to blast out thousands of CVs per day to game the system and land a gig. However, according to experts in the tech industry who spoke with SFGATE, this is only creating a vicious cycle of inefficiency that hurts both workers and companies.

 

Amazon, gig companies see spike in workers on SNAP and Medicaid, study shows — from washingtonpost.com by Lauren Kaori Gurley and Rachel Lerman
Amazon workers on federal aid nearly tripled between 2020 and 2025, while ride-hailing and food delivery drivers shot up on lists compiled by the U.S. Government Accountability Office.

While the number of working adults who depend on food stamps and Medicaid has ticked up since the start of the pandemic, the number of recipients who work at Amazon and on gig economy platforms has exploded, according to a new report from the U.S. Government Accountability Office.

The number of Amazon workers relying on the federal programs for the poor nearly tripled between February 2020 and September 2025, according to the report published Wednesday. And for the first time, ride-hailing and app-based food delivery companies — Uber, Lyft, DoorDash, Grubhub and Instacart — collectively ranked among the top three employers with workers receiving aid.


From DSC:
I have long disliked what the gig economy was/is doing to workers. A handful of entrepreneurs who started these companies have done very well for themselves, while the rest of the people who are doing most of the work hardly make anything. No medical insurance, no vision or dental insurance, and no retirement contributions/benefits. Amazon works its people to the bone (I saw this firsthand with our son and his deteriorating physical health while working for them), and look what it gets them? Bezos Inc. make out great — others…not so much. 


Ride-hailing and food-delivery companies, meanwhile, shot past Walmart to collectively rank as the top employer of food stamp users and among the top three for Medicaid users, according to The Post’s analysis. 

 

Steward Stories: How Heart of Oregon Corps Turns Service into Careers — from gettingsmart.com by Karen Pittman and Merita Irby

Our “Steward Stories” series captures lessons learned from interviews with leaders of mature, purpose-built ecosystem intermediaries. An ecosystem steward is a boundary-spanning leader who goes beyond traditional out-of-school time (OST) system building to weave together the diverse people, places, and possibilities that shape a young person’s daily life. Rather than managing a single isolated network, stewards collaborate across K-12 schools, youth development programs, and workforce systems to build vibrant, equitable learning ecosystems. They drive systemic change from practice to policy by creating purpose-built intermediaries, developing scalable cross-system training tools, and championing “Future Features” of learning. These core priorities include promoting learner agency, institutionalizing “unwalled” schools that connect community resources to formal education, broadening the definition of educators to include informal mentors, and normalizing pathways for students to receive school credit or credentials for out-of-school learning.

 The starting points, paths, and targets set by these stewards are as varied as the conditions and opportunities present within their communities. But they share similarities in vision and approach. Learn More Here.

Learning ecosystems may be found anywhere, but it takes careful stewardship to help them thrive.
— Shift, Remake Learning

 
© 2025 | Daniel Christian