GenAI practice blossoms through the open exchange of insights — from timeshighereducation.com by Samuel Doherty, who is the education and innovation coordinator at the University of Newcastle in Australia How a structured GenAI professional development series, built around practice, peer voices and multiple entry points, fosters open exchange among colleagues, universities and industry
Connect internal practice to sector-wide thinking Whatever is happening within any single institution is only part of the picture. Effective GenAI practice grows through open exchange of insights among colleagues, universities, professional bodies and industry, and a development programme that is entirely inward-looking risks missing both useful knowledge and important shifts in expectation.
Our AI sector voices sessions aim to bring external contributors into the programme: researchers, practitioners and sector representatives working at the intersection of GenAI and higher education. The aim is to situate institutional practice within the wider conversation and to signal to staff that the institution is genuinely engaged with that conversation, not just managing it internally.
In the Australian context, the Tertiary Education Quality and Standards Agency (Teqsa) people pillarpositions staff as drivers, enablers, users and innovators of GenAI practice, and identifies a lack of information or understanding as one of the primary barriers to ethical and effective engagement. That framing is useful regardless of regulatory context: institutions that treat their people as active participants in shaping practice, rather than recipients of policy, are likely to develop more durable capability.
Regular, lightweight communications, a weekly community of practice update and a monthly all-staff digest can maintain momentum between sessions without adding significantly to anyone’s workload.
Institutions can use AI to make skills, pathways, and job outcomes visible to students and employers in ways traditional transcripts cannot.
Academic affairs, workforce development, career services, and employers need a shared definition of readiness and competency before tools can deliver meaningful value.
The second is portable competency records. Learning and employment records — AI-enabled documentation of what a student knows and can do, expressed in language employers recognize — are the infrastructure that makes credentials legible across the education-to-employment continuum. When a student can show an employer not just “completed Supply Chain Management 101” but “demonstrated proficiency in inventory optimization, route planning, and logistics software at the industry-recognized level,” the credential stops being abstract. It becomes evidence. Building these records requires investment in tools, yes — but more importantly, it requires faculty, workforce development staff, and employer partners to agree on what competency actually looks like before the technology is ever purchased.
For higher education to remain relevant, curricula must evolve. Here are some overarching recommendations for directions in higher education to bridge the skills gaps between universities and workplaces:
AI ethics and safety: Prepare students to navigate issues of fairness, bias, privacy, and societal impact.
Tackling complex questions: Emphasize open-ended challenges that blend structured and unstructured skills and reduce reliance on standardized tests and repetitive drills.
Critical thinking: Develop new assessments for judgment, creativity, and metacognition—essential to supervise AI outputs.
Human-AI synergy: Embed AI fluency across all disciplines, encouraging students to find the niches where human value is maximized.
Industry connection: Maintain close industry partnerships and collaborations including open innovation opportunities and collective intelligence approaches (Bharwaney & Sleeva, 2024).
Experiential learning and communities of practice are central to this vision. Internships, simulations, and cross-disciplinary projects can help students practice human-AI collaboration, resilience, and decision-making in environments that mirror the workplace’s ambiguity and complexity.
Universities that condemn the use of AI by students risk isolating themselves from the realities of today’s workplace, where interns and new hires are expected to be or quickly become adept at using AI for routine tasks and complex projects.
After release, formerly incarcerated people must navigate a maze of government systems, workforce programs, and parole requirements. They are rarely prepared to do this, and as a result, nearly half (45%) report no earnings within the first year of their release, according to research from the Brookings Institution.
The Big Idea: Reducing those barriers has become an increasing focus for a number of philanthropies and colleges. It’s also a growing labor market imperative.
There’s also an incentive for many states to help this population of potential workers land jobs. According to the U.S. Chamber of Commerce, formerly incarcerated individuals who are unable to maintain employment experience a recidivism rate of 52% over three years, while those who are employed for one year post-release experienced a recidivism rate of just 16%.
From programmes to learning ecosystems These pressures point towards a broader redefinition of higher education. Rather than viewing education as a one-time experience culminating in a degree, universities increasingly need to see themselves as partners in professional development across an entire career.
This means moving from a model centred on programmes to one focused on learning ecosystems that allow individuals to enter, leave and re-engage with higher education as their needs evolve.
Business schools may be particularly well placed to lead this shift because of their close engagement with employers and their long tradition of educating professionals at different stages of their careers.
But success will depend on more than introducing new modules or certificates. Universities must confront a fundamental question. Are the systems, structures and cultures that define higher education capable of supporting genuinely flexible learning?
The sector has already embraced the language of lifelong learning – the next step is ensuring that universities themselves are built to deliver it.
From DSC: Long-time readers of this blog have seen this graphic of mine posted over the last 12+ years: .
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Also relevant/see:
What if the undergraduate journey were a four-year internship? — from timeshighereducation.com by Michelle Seref Treating work placements and co-curricular programmes as optional or supplementary misses deeper questions about whether traditional degrees prepare students for careers. Michelle Seref explains
Attending workshops or polishing a résumé in their final semester does not make students career-ready. They need to practise how to work – how to collaborate, navigate ambiguity, manage projects and apply knowledge in context – throughout their academic experience. The reality is that career readiness is not a co-curricular programme; it is an essential part of an integrated curriculum.
To be clear, employers do not expect classrooms to become training centres. What they are asking for – implicitly and explicitly – is graduates who can function in complex environments from day one. That means graduates who can work in teams, communicate professionally with stakeholders, adapt when plans change, apply theory to real constraints and learn continuously on the job.
These capabilities do not develop through passive learning. But experiential learning is often misunderstood as a single, high-impact activity: an internship, a capstone project or study abroad. In reality, its power comes from repetition and progression. One experience introduces exposure. A sequence of experiences builds competence.
We are proposing a paradigm shift: repositioning the undergraduate journey as a four-year professional internship rather than a continuation of the K-12 classroom environment.
. From DSC: The problem with this innovative idea is that faculty often are not out in the “real world.” The best chance higher ed has to deliver on this idea is via the adjunct faculty members out there. Often, they are the ones practicing what they are teaching. They are constantly pulse-checking — and actively involved with — their industries and have more up-to-date, practical knowledge.
But this is a problem for traditional institutions of higher education, which have treated their adjunct faculty members poorly through the years. Adjunct faculty members hardly make minimum wage, have no benefits, no retirement plans, etc. — plus they have little to no say in faculty senates.
We are the leading ____ in the Midwest/Southwest/Northwest/etc. (says who? Prove it.)
Our patients’ care is important to us (no, it’s not…you only care if your customers’ accounts are paid in full. If patients’ care were actually important, you would fix what’s broken.)
Your call is important to us (no, it’s actually not. If it were actually important to you, you would have more customer service reps working so that the wait times were either non-existent or much shorter. The truth is that you would rather cut costs/headcount and have your customers wait. Be truthful about it. Stop the B.S.)
A vast number of American corporations don’t actually care about their customers — their concern focuses solely on obtaining their customers’ money.
One of the ways this plays out is that they hide behind the labyrinths that are designed into the call pathways in their Voice Response Units (VRUs). VRUs have been abused. Corporations hide behind them. It’s hard to actually reach a person or hold a person accountable for something.
And now, with executives getting rid of entry-level jobs in customer service, they seek to cut costs further as they implement AI-based systems…which rarely give us what we’re looking for.
But even in written communications, times seem to be changing…and not for the better. I had a customer service rep write me a letter recently (regarding an incident with our daughter’s experience at a blood lab). But in the letter, she didn’t even provide her last name or a direct phone # in her correspondence. This would NEVER have happened in business letters back in the day — her last name would have been present, for sure — and likely a direct phone #. This isn’t her fault. It’s her leadership’s fault. BTW, the issue was passed along to the lab’s leadership…and she closed her ticket out. But there was no mention of an actual fix or resolution. Nice hand washing job, don’t you think?
Another case in point. This time, involving Apple. (BTW, I’ve been a long-time Apple fan…until the last several years. They have lost some of their focus on customer service.) I wanted to ask a question about a purchase that showed up on our Visa bill from apple.com/bill. Do you think I could find an 800# to talk with someone at Apple? Nope. You can try to find things via their online-based support systems, but often their documentation doesn’t match up with one’s devices. I couldn’t even use their chat feature — their systems told me that their chat feature wasn’t available (and it was 11:30am EST).
I’m sure if you thought about it, you could come up with your own recent examples of poor customer service experiences — or examples of companies that you did business with who didn’t deliver what they said they would deliver.
The issue runs deeper than we think. It actually has to do with whether people actually care about each other or not. And here in America, actually caring about others seems to be in short supply.
The reason that I claim I am not well-suited to thrive as an instructor in the age of AI is because both AI Enthusiasts and AI Resisters put a lot of thought and energy into completely redesigning their classes in response to AI. This is the one takeaway that I don’t think the Exhausted Majority has fully accepted yet—to excel as a teacher in this AI era, you need to totally revise how you teach and how you assess what students learn in your classes.
I can say this much—whatever solution our industry comes up with, it’s likely to emerge from teaching and learning centers. Contrary to what Paul Schofield wrote in the Chronicle of Higher Education, pedagogy experts are the best hope we have to equip today’s faculty with the tools required to succeed in this uncertain educational environment. As I always tell my students, “I was trained for 7 years to become a researcher and 2 days to become a teacher.” The idea that only disciplinary experts know how to teach and have nothing to learn from so-called “nonscholars” is so laughable that one has to wonder whether an AI agent jokingly wrote that sad opinion piece to troll the whole academe.
Last week, one of the top law schools in the United States, the University of California, Berkeley School of Law, released its final policy on artificial intelligence, effective summer 2026. In the span of a breezy 1.5 pages, the school outlined the challenge AI poses to legal education and how it plans to address this problem. Despite these intentions, this AI policy is, in my estimation, the worst AI policy in higher education I have seen.
From AI Tutors to AI Study Mates— from drphilippahardman.substack.com by Dr Philippa Hardman New research reveals how AI can enable real learning — not just productivity gains
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The point isn’t that AI is inherently bad for learning — it’s that the meta-analyses showing that LLMs improve assignment and performance scores are measuring the wrong thing. They’re measuring performance with the AI present, not learning that persists once it’s gone.
. From DSC: Notice that when an AI-based learning system can remember what you’ve worked on and how you are doing — where you are struggling or doing well — it can have a positive impact on your longer-term learning. That, to me, is where long-term based learner profiles come in.
Later in the article, Dr. Hardman points out that “if we want to deliver AI tooling which supports substantive learning, we need to intentionally create a new category of AI tool for ‘learning at work’ which prioritises learning and development over productivity.” While I agree with that, I do wonder if businesses will care, so long as the work gets done and gets done well. But this calls into mind the word “experience” — something that traditionally has been hard fought to get in the corporate world. But the corporate realm often doesn’t like to pay for experience (beyond key AI-based jobs) when they perceive it’s getting too expensive. Ask all those 50 and over who had or have a target on their backs.
We welcome back Sabastian Niles, President and Chief Legal Officer at Salesforce, to discuss his recent “Open Letter to Law Firms.” As the legal industry hits a critical inflection point, Sabastian argues that the era of “AI theater” and small-scale pilots is over.
The conversation dives deep into the Innovator’s Dilemma facing law firms, the shift toward agentic AI, and how firms must reimagine their business models to remain competitive. Sabastian highlights that legal professionals are uniquely positioned to lead the charge in trusted AI transformation, provided they embrace transparency, data integration, and shared efficiency gains with their clients.
Three Realities for the Modern Legal Firm To lead in this landscape, there are three realities every firm leader must understand:
Competition is intensifying: …
Client expectations will reshape the market: Clients are no longer asking whether firms use AI. Rather, they’re expecting to see the benefits of that transformation passed directly to them. They expect more for less but are not simply seeking lower costs – they want more insight, more speed, and more value for every dollar of their budget. And law firms, which operate at the center of data, ethics, and risk, have outsized influence over the structure and deployment of trusted AI across all industries. Some clients, like Salesforce, are even creating agentic tools to improve the law firm’s experience when working with clients. …
Unified client intelligence is at the heart of legal strategy: …
Are AI First Firms a Threat To Biglaw? — from legallydisrupted.com by Zach Abramowitz and Logan Brown Episode 49 features AI first law firm founder Logan Brown
Is Big Law about to become the Yellow Pages? Hey, I didn’t say it, but ex-Cooley lawyer turned AI first law firm Logan Brown did. The question is do I agree?
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Instead of the traditional billable hour, they charge flat fees like $100 for a contract review or $50 to ask a lawyer a quick question via chat. She’s already got over 40 attorneys on the platform. And in a departure from the traditional partnership track, she actually chose to raise venture capital so she could scale the firm like a tech company and tackle the access-to-justice gap.
From DSC: LOVE to hear anything and everything regarding efforts to address the access-to-justice gap here in the United States!!! Along these lines, also see:
“Legal services are out of reach for many people and small businesses, and the gap is widening,” Anthropic said in its announcement. “We’re working with the Free Law Project, Justice Technology Association and other legal aid and public service organizations to help make legal services more affordable and available.”
That makes this the first time that a leading AI company is explicitly naming access to justice as a foundational pillar, JTA says, with Anthropic positioning the initiative as “investing in the premise that AI should expand access to justice — making legal services more affordable and available.”
What AI hallucinations in law actually are
In a legal context, AI hallucinations are one of two things. They’re either citations to cases or statutes that don’t exist, or citations to real authorities for propositions those authorities don’t actually support.
The first kind is the one making headlines. A lawyer or pro se litigant uses a general-purpose chatbot like ChatGPT, Claude, Gemini, Copilot, or Grok to help draft a brief. The model, predicting the statistically likely next word, decides a citation belongs in a particular spot, and produces one. The reporter might be real. The volume number might fall within the right range. The Bluebook formatting is often better than what most associates produce. The case itself just doesn’t exist.
The second kind is older than AI. Lawyers have always occasionally cited a case for a proposition that the case doesn’t stand for. AI has made this kind of error easier to commit and easier to catch.
A dangerous mind — from by Jordan Furlong Generative AI is a tireless genius with no boundaries. Use it carelessly, and it can usurp your voice, overwrite your ideas, and steal your originality. Make sure you safeguard your capacity to think.
Don’t let the genius do the hard work for you. The more incisive and unique your own thinking — the more you battle and struggle and eventually succeed in getting your ideas and insights out — the more you can benefit from the AI’s complementary improvements. The great irony of Gen AI is that it actually makes your own cognitive processes your most valuable asset.
So safeguard your mind. Defend your right to think as only you can. And if you don’t want AI to replace you, then don’t send it a written invitation.
The pilot phase is over. After two years of experimentation for legal departments, 2026 will be the year AI moves from “interesting tool” to “operational infrastructure,” whether they’re ready or not. We surveyed predictions from Gartner, Forrester, McKinsey, and other leading legal tech analysts to identify where expert consensus is forming. The implications for AI governance, outside counsel relationships, and regulatory compliance are significant.
LinkedIn Grad’s Guide 2026: Starting your career in the AI era — from linkedin.com by Gianna Prudente To help you head off in the right direction, we’ve identified where those starting their careers are finding opportunity, based on data from millions of LinkedIn member profiles.
While all of this is happening, colleges are still catching up. Many students are graduating without having spent much time learning how AI actually fits into day-to-day work — even as employers seek out those exact skills.
“Colleges are moving into an era of, we’ll let the faculty decide, which leads to a very uneven experience for students because some faculty are really into AI and other faculty are not,” says Jeff Selingo, a higher education strategist. “Employers are the same; they don’t really know how to act around early careers.”
Taken together, new grads are entering a uniquely challenging environment: fewer traditional entry points, slower turnover and a workplace that’s evolving faster than the systems preparing people for it.
For a few moments, all was quiet except the classroom’s ticking radiators. Then, a teary-eyed confession: one of the ostensible authors said she only used AI because she was scared of looking stupid, of being criticized for bad writing. She said she loved writing stories and hated having used AI. But she couldn’t stop herself, recounting a sequence similar to an addict’s descent: at first she fed her story into AI for a grammar check, it suggested line edits and she accepted, then it asked if she wanted structural edits, then it offered to rewrite the entire piece.
The other would-be author admitted he had never written a short story before and he had an idea but didn’t know where to start. I asked him why he didn’t reach out to me for help. He shrugged.
One of the other students raised her hand, saying she didn’t understand why it was bad for AI to write stories as long as the stories are based on their ideas. More students spoke: one wanted to know how using AI was any different from using a human editor. Another wanted me to answer why, at a university that launched one of the world’s first AI research programs in 1959, were we even having this debate? Isn’t AI meant to make everyone’s life easier? Less stressful? Isn’t the point of AI to free humans from the tedium of rote tasks?
The conversation that followed their confessions was one of the most productive teaching moments of my eight years at MIT. Writing, I told them, isn’t supposed to be easy, and of course it can be tedious but that doesn’t make it rote. Writing isn’t just the production of sentences – it’s the training of endurance by way of sustained attention. It’s a way of learning what one thinks by attempting to say it.
This $10K AI School Promises to Future-Proof Your Career — from builtin.com by Matthew Urwin Khan Academy, TED and ETS are starting a new program to equip students and professionals with the skills to thrive in an increasingly AI-driven economy. Here’s what you need to know.
Summary: The Khan TED Institute is a higher-education program that will teach students and workers how to use AI through interactive learning. The program’s AI-centric curriculum is an unproven approach, though, casting doubt on whether it will actually improve learning outcomes and career prospects.
The TalentLMS 2026 Annual L&D Benchmark Report — from talentlms.com From year-over-year training benchmarks to learner–leader gaps, see the data that defines the new era of learning. To turn insight into action, the report lays out 10 evidence-backed interventions to hardwire development. Plus, lift the lid on Learning Debt: What it is and how to spot it.
Executive summary
The skills economy is being rewritten in real time. AI is reshaping what people need to know, do, and deliver, faster than organizational structures can adapt. The result is a workplace caught between acceleration and inertia. Companies are racing to reskill for an AI-driven future while relying on structures built for yesterday’s world.
This TalentLMS 2026 L&D Benchmark Report captures that inflection point. Based on data collected through 2025, and compared with earlier findings from 2022 to 2024, it explores how learning is evolving and what’s holding it back.
Our research integrates two vantage points: HR leaders overseeing learning initiatives and employees receiving formal training. Together, they offer a dual perspective on how learning is managed and how it’s experienced.
The analysis also draws on insights from external research and leading L&D practitioners, anchoring the report in both evidence and practice.
Combined, the findings point to a structural fault line: Learning is expanding in scope but contracting in space. Organizations are multiplying programs, tools, and ambitions, yet the conditions for learning — time, focus, and cognitive bandwidth — keep shrinking.
The data from this report underscores this critical conflict: According to half of the surveyed employees and learning leaders, high workloads leave little room for training, even when it’s needed.
Employees work inside a permanent sprint, where attention is fragmented and reflection is sidelined. The space for learning is collapsing under the weight of doing. Sixty-five percent of employees say performance expectations have risen this year, yet lack of time remains the biggest barrier to learning.
The numbers confirm what employees and learning leaders both feel: Technology can advance overnight. But people and cultures can’t.
NEW YORK, April 13, 2026 /PRNewswire/ — FutureFit AI, a global leader in AI-powered workforce development technology, today announced an investment from Achieve Partners, led by investor and author Ryan Craig, to accelerate its mission of helping more people navigate to better jobs faster and cheaper at scale.
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“For too long, the U.S. workforce system has relied on disparate and disconnected systems to try to bridge the gap between the skills workers bring to the table, and the jobs available in a fast-changing labor market. In the age of AI, the need for a better approach has only become more urgent,” said Ryan Craig, co-founder and managing director of Achieve and author of Apprentice Nation, A New U, and College Disrupted. “FutureFit AI is solving that problem by helping workforce organizations create clearer paths to career opportunity for workers and solve pressing talent gaps that hinder economic growth. Their work around the country has already demonstrated the ability to help more people get good jobs faster.”
“A mission that began with a simple question of ‘What if everyone had a GPS for their career’has turned into years of working closely with government and industry leaders to respond to – and solve for – the impacts of digital transformation and AI on jobs and people,” added Ekhtiari. “Our partnership with Achieve will accelerate our work to build and scale the missing workforce transition infrastructure that our country and the world so badly need at this moment.”
AI for Your Next Career Move— from wondertools.substack.com by Jeremy Caplan Free tools to explore, research, and interview better
AI tools can serve as patient assistants when you’re looking for a job. Use them to organize your search. Or to challenge your assumptions about potential jobs. They can also help you present your strengths more persuasively. When you’re changing fields, or trying to move up, AI can help you stand out.
1. Visualize Your Career Options
Try: Google’sCareer Dreamer
What it is: A free tool for exploring jobs adjacent to yours. See a map of professional fields related to your interests.
How to use it: Start by typing in a current or previous role. Or name a job that interests you. Use up to five words. You can also name a specific organization or industry, if you have one in mind.
Career Dreamer asks what work activities interest you, then maps related career paths. Pick one at a time to explore.
You can then browse actual job openings. Refine the search based on location, company size, or other factors you care about.
If you’re sweating about what field to enter, here are a few things you can do now. Buy a cheap, thin notebook. Keep it on you. Every week, make a practice of writing down which actions you enjoy and which ones you hate, whom you like being around and whom you can’t stand. Keep running lists of what you’re good at and what ideas move you. Notice yourself.
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Look to your friends instead. Think about what roles you take on with them: math tutor, party planner, psychologist, workout coach. These answers often reveal truths that our résumés do not. In social relationships, we aren’t bound by suffocating expectations about our future. Our friends have needs, and by noticing how we respond to them, we can learn who we are.
There is a wiser way to seize the future, which is to think about need. What is your own assessment of what society will need most during your working years, the next four or five decades? What kind of care; what kind of products; what kind of information?
The people I see thriving at work are the ones who chased some bigger need — not imposed by hollow conventional wisdom, but articulated through independent observation. Craft gives their work authority. Need gives it propulsion.
TRAVERSE CITY, MI – The long line out the door and down the street of the little Oakwood Proper Burgers shop was a head-turner Saturday as the restaurant invited people to its 1,000 Burger Challenge event.
But the swift sales being rung up inside weren’t benefitting their own business. Instead, they were a heartfelt helping hand to a competitor across town.
The team behind Oakwood Proper – as well as several other restaurant friends from the area – joined together to raise money for “Chef Tim” Bergstrom, the man behind his namesake Bergstrom’s Burgers. He’s been undergoing cancer treatment for some time now, and medical bills are mounting. .
Which Jobs Are Most at Risk From AI? New Anthropic Data Offers Clues.— from builtin.com by Matthew Urwin Anthropic set out in its latest study to predict how artificial intelligence could impact the labor market. Instead, its findings raise more questions than answers for tech workers as the U.S. government refuses to regulate the AI industry.
Summary:
In its latest labor market study, Anthropic found that artificial intelligence poses the greatest threat to software jobs, women and younger professionals. As the Trump administration takes a hands-off approach to AI, tech workers may be left to grapple with these findings on their own.
We introduce a new measure of AI displacement risk, observed exposure, that combines theoretical LLM capability and real-world usage data, weighting automated (rather than augmentative) and work-related uses more heavily
AI is far from reaching its theoretical capability: actual coverage remains a fraction of what’s feasible
Occupations with higher observed exposure are projected by the BLS to grow less through 2034
Workers in the most exposed professions are more likely to be older, female, more educated, and higher-paid
We find no systematic increase in unemployment for highly exposed workers since late 2022, though we find suggestive evidence that hiring of younger workers has slowed in exposed occupations
