Artificial Intelligence/Agents/LLMs/and Related

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.

FutureFit AI Announces Strategic Investment to Help Governments and Industries Navigate AI’s Impact on People & Jobs

On 04/27/2026, in 21st century, 24x7x365 access, Artificial Intelligence/Agents/LLMs/and Related, career development, corporate / business world, legislatures / government / legal, United States, vendors, workplace, by Daniel Christian

FutureFit AI Announces Strategic Investment to Help Governments and Industries Navigate AI’s Impact on People & Jobs — from prnewswire.com; via Ryan Craig

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 Judges? AAA CEO Bridget McCormack & Learned Hand CEO Shlomo Klapper #LegalAI #legaltech [Abramowitz]

On 04/27/2026, in 21st century, 24x7x365 access, Access to Justice (A2J), Artificial Intelligence/Agents/LLMs/and Related, human-computer interaction (HCI), innovation, intelligent systems, law schools, Legal operations, legal reform, legal technologies, legislatures / government / legal, Michigan, platforms, society, tools, United States, vendors, by Daniel Christian

Recording at LegalWeek in New York, Zach sits down with Shlomo Klapper (founder of Learned Hand) and Bridget McCormack, former Chief Justice of the Michigan Supreme Court and now CEO of the American Arbitration Association, to challenge one of the biggest double standards in legal AI: “AI for me, but not for thee.” Lawyers are now widely using AI like #Harvey and #Legora — and now more than ever #claude — but the moment it touches judges or arbitrators, support drops off.

That hesitation comes as courts are under real strain, with judges handling thousands of cases a year and only minutes to decide each one, and no realistic way to keep up. Shlomo describes Learned Hand’s “AI law clerk,” built to support judicial research, analysis, and drafting, while Bridget brings the perspective of someone who has both made decisions on the bench and has pioneered the American Arbitration Association’s AI Arbitrator, a first of its kind. The conversation moves beyond AI as an assistant and into a harder shift: AI as part of decision-making itself, and whether the system can continue to function without it.

Also see:

Are Judges the Next To Adopt AI? Is That a Good Thing? — from legallydisrupted.com by Zach Abramowitz
Episode 46 of Legally Disrupted Has the Two Best Experts on the Topic

This brings us to an admitted, glaring double standard between lawyers and judges. Lawyers are totally fine with lawyers using AI, but those same lawyers become apoplectic at the thought of judges or arbitrators using AI. It is very much “AI for me, but not for thee.” A survey last year from White & Case and Queen Mary University of London School of Law showed that nearly 90% of lawyers were deeply supportive of AI for their own research and analytics, but that support drops to just 23% when it comes to a judge or arbitrator using it to make a decision.
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Yet, despite that hullabaloo, there is a massive need for alternative forms of intelligence in our courts. Right now, the system is drowning. We have state court trial judges disposing of 2,500 cases a year, meaning they have barely half an hour to spend on a single case. We are simply not going to lawyer our way out of this 50-year backlog. If we just use humans, we have a massive demand for intelligence but a severely limited supply. AI could step in to give these judges the capacity they desperately need for the courts to actually function.

An Attack on Sam Altman Sends a Terrifying Message [Gifted opinion article by Aaron Zamost]

On 04/27/2026, in 21st century, 24x7x365 access, Artificial Intelligence/Agents/LLMs/and Related, corporate / business world, emerging technologies, ethics, intelligent systems, morals/values, platforms, society, United States, vendors, by Daniel Christian

An Attack on Sam Altman Sends a Terrifying Message — from the nytimes.com; this is a gifted opinion article by Aaron Zamost

Lawless political violence landed on Silicon Valley’s doorstep this month when an attacker hurled a Molotov cocktail at the San Francisco compound of Sam Altman, OpenAI’s chief executive. The incident was a disturbing sign that simmering public anger about A.I. is spilling out of polling data and social media posts and into the real world.

The attack shook many tech employees, who in quiet conversations about safety wondered whether this was a watershed moment for the industry. I believe it should be — the whole thing is disturbing and jarring, but I’m hopeful it will change how some tech leaders deal with the societal consequences of their success.
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If these companies sold food, cars, medicine or any other consumer goods, their products would almost certainly be recalled while federal regulators investigated the allegations.
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You would think an industry creating this kind of outrage would reflect or recalibrate. Business experts teach us that companies facing customer backlash should acknowledge the failure, change their approach and earn back public trust. But the titans of tech no longer seem interested in convincing the public.

The foundation of Silicon Valley’s appeal has always been the implicit promise that great technology serves you, and that the people behind it understand your problems and want to solve them. That promise is starting to feel broken. Fixing it requires something much of Silicon Valley has forgotten how to do: listen and learn.

A Molotov cocktail is the absolute wrong way to send a message to tech. Its leaders need to hear it anyway.

What does journalism education look like when AI is starting to do entry-level work? [Caplan]

On 04/25/2026, in 21st century, 24x7x365 access, Artificial Intelligence/Agents/LLMs/and Related, change, changing business models, colleges, community colleges, ethics, faculty/staff, higher education, ideas, journalism, morals/values, staying relevant, surviving, United States, universities, by Daniel Christian

Jeremy Caplan’s post on LinkedIn:

Or see Jeremy’s post on substack — i.e., from wondertools.substack.com.

The Role of Faculty in the University of the Future [Gamby, Kil, Koblic, LeBlanc, Moldoveanu, & Siemens]

On 04/25/2026, in Artificial Intelligence/Agents/LLMs/and Related, colleges, communications, community colleges, creativity, Daniel S. Christian, education technology, emerging technologies, faculty/staff, higher education, IT in HE, learning ecosystem, Learning Experience Design, professional development, teaching & learning, United States, universities, vision/possibilities, by Daniel Christian

The Role of Faculty in the University of the Future — from er.educause.edu by Tanya Gamby, David Kil, Rachel Koblic, Paul LeBlanc, Mihnea Moldoveanu, and George Siemens
In the age of AI, the true future of higher education lies not in replacing faculty but in freeing them to do what only humans can—build meaningful relationships, cultivate wisdom, and guide students through the ethical and intellectual challenges machines cannot navigate.

Today, the work of knowledge transfer is often done better, faster, with more precision, and more patiently by AI. These systems can provide nonjudgmental, individualized learning opportunities twenty-four hours a day, seven days a week. Think of AI as a “genius teaching assistant” who assumes much of the work of basic knowledge transfer, unlocking learning when students get stuck and providing real-time assessment. Such a genius TA would offer faculty dashboards that update student progress, flag those who are struggling, and recommend targeted interventions. These tasks free faculty to focus on building genuine relationships with students, using the classroom to foster human skills, and curating community. This may be the great gift of AI to education. But it requires a profound reimagining of faculty roles—perhaps the single biggest hurdle to reimagining higher education, and equally its greatest opportunity.

A concerned faculty member might hear all this and conclude they are becoming obsolete. The opposite is true. The evolution of faculty roles demands more—not less—of what makes a great teacher.

This means intervening in high-impact moments when the genius TA has not unlocked learning; curating class time to lift students from knowing material to applying it in contexts that require critical thinking, judgment, and discernment; and cultivating the human skills that will be most prized in the age of AI: effective communication, constructive dialogue, empathy, creativity, and professional disposition. Most importantly, it means building genuine relationships with students—that make them feel like they matter—the kind that fuels transformation.

From DSC:
A quick comment on one of the sentences in the article, which asserts:

Centers for teaching and learning, which have long supported faculty development at many institutions, will be among the busiest places on campus in the years ahead.

I would change the word will be to should:

Centers for teaching and learning, which have long supported faculty development at many institutions, should be among the busiest places on campus in the years ahead.

For that statement to be true, centers for teaching and learning need to be well-versed in the tools and pedagogies involved, plus in learning science. Those centers need to have credibility for faculty members to value their services. And that’s just it, isn’t it? The faculty members need to see those centers for teaching and learning as having something that they lack…that they need assistance with. Otherwise, if such centers are just viewed as superfluous, nothing much will change.

Also, my experience has been that if those centers for teaching and learning are in an IT group/department, they should be moved to the academic side of the house instead. Many faculty members don’t value people from IT enough to make changes in how they teach — no matter how qualified those people are. They view those people as “IT” only.

You might also be interested in the other articles in that series:

The “Cognitive Offloading” Paradox [Hardman]

On 04/19/2026, in Artificial Intelligence/Agents/LLMs/and Related, Asia, colleges, community colleges, digital learning, education technology, emerging technologies, Europe, higher education, human-computer interaction (HCI), learning, learning ecosystem, Learning Experience Design, learning theories, student-related, teaching & learning, UK, United States, by Daniel Christian

The “Cognitive Offloading” Paradox — from drphilippahardman.substack.com by Dr. Philippa Hardman
New research shows that offloading learning tasks to AI can improve – rather than erode – human thinking and learning

The Rise of the “Offloading Paradox”
In March 2026, the International Journal of Educational Technology in Higher Education published a study that went beyond the question “does offloading hurt?” and asked a harder one: when students form genuine partnerships with AI — treating it as an intellectual collaborator rather than a passive tool — what actually happens to the way they think and learn? Specifically, do two cognitive responses — critical evaluation of AI outputs (what the researchers call cognitive vigilance) and strategic delegation to AI (cognitive offloading) — compete with each other, or can they coexist?

Based on previous research, Wang and Zhang hypothesised that cognitive offloading would hurt transformative learning. They expected the familiar story: delegation reduces cognitive struggle, struggle is where learning happens, therefore delegation undermines learning.

The study — 912 students across China, Europe, and the United States, using a three-wave time-lagged survey design that measured partnership orientation first, cognitive strategies two weeks later, and learning outcomes two weeks after that — found something more interesting than a simple reversal.

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.

Matthew links to:

Labor market impacts of AI: A new measure and early evidence — from anthropic.com

Key findings

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

Also see:

Dr. Ian Kelleher’s introduction to his paper here on LinkedIn

What the Future of Learning Looks Like in the Era of AI [U of M’s Center for Academic Innovation]

On 04/15/2026, in 21st century, 24x7x365 access, apprenticeships, Artificial Intelligence/Agents/LLMs/and Related, collaboration, colleges, communities of practice, community colleges, corporate / business world, credentialing, emerging technologies, experimentation, faculty/staff, future of higher education, game-changing environment, higher education, ideas, innovation, instructional design, intelligent systems, internships, learning agents, learning ecosystem, Learning Experience Design, Learning from the Living [Class] Room, lifelong learning, Michigan, real-world assignments, student-related, teaching & learning, United States, universities, workplace, by Daniel Christian

What the Future of Learning Looks Like in the Era of AI — from the Center for Academic Innovation at the University of Michigan, by Sean Corp

AI & the Future of Learning Summit brings industry, education leaders together to discuss higher education’s opportunity to lead, what students need, and what partnerships are possible

As artificial intelligence rapidly reshapes the nature of work and learning, speakers at the University of Michigan’s AI & the Future of Learning Summit delivered a clear message: higher education must take a leading role in defining what comes next.

One CEO of a leading educational technology company put it like this: “The only bad thing would be universities standing still.”

Universities must embrace their roles as providers of continuous, lifelong learning that evolves alongside technological change.

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This shift is already affecting early-career pathways. Employers are placing greater emphasis on experience, while traditional entry-level roles are becoming less accessible. There is often a gap between what a credential represents and the expectations of employers.

That gap is particularly evident in access to internships. Chris Parrish, co-founder and president of Podium, noted that millions of students compete for a limited number of internships each year, making it increasingly difficult to gain the experience employers demand.

“If you miss out on an internship, you’re twice as likely to be unemployed,” Parrish said.

You Can’t Future-Proof Your Career From AI, But You Can Do This [Tran]

On 04/07/2026, in 21st century, Artificial Intelligence/Agents/LLMs/and Related, career development, change, experimentation, ideas, organizational change, pace of change, productivity / tips and tricks, skills, workplace, by Daniel Christian

You Can’t Future-Proof Your Career From AI, But You Can Do This — from builtin.com by Liz Tran
Agility has become the most important skill to cultivate in today’s job market. Here’s how to get started.

Summary: Job seekers facing future panic should prioritize agility over information consumption. Build it by focusing on 30-day action experiments, reframing resumes around durable skills like problem-solving and embracing uncertainty through stretch applications and real-world feedback.
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The antidote is what I call AQ — the agility quotient — which is your capacity to face change, disappointment and uncertainty without losing your footing. Unlike IQ, which measures what you know, AQ measures how fast you adapt when the rules change. Right now, it’s the most important career asset you have. Here’s how to build it.

What Is Agility Quotient (AQ)?
AQ is a measure of an individual’s capacity to adapt quickly when rules, industries or circumstances change. Unlike IQ, which focuses on existing knowledge, AQ emphasizes the ability to face uncertainty and disappointment without losing one’s footing, prioritizing action and iteration over exhaustive planning.

How does this new AI-based situation impact judges, lawyers, and law firms? [Christian] + other items re: legaltech

On 04/07/2026, in 21st century, 24x7x365 access, Artificial Intelligence/Agents/LLMs/and Related, law schools, Legal operations, legal reform, legal technologies, legislatures / government / legal, podcasting, by Daniel Christian

DC: How does this impact judges, lawyers, and law firms? What should law schools be doing?

“The accessibility of sophisticated AI tools has destroyed traditional signals of human legitimacy online. We need a new verification system immediately.”https://t.co/zwUTIrz6GR

— Daniel S. Christian (@dchristian5) April 7, 2026

Summary: Accessible AI has killed traditional signals of legitimacy.
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Experiments show $20 consumer tools can easily bypass verification. The solution is shifting toward contextual proof that verifies human uniqueness without exposing identity.

After Hours 1: The legal profession’s new value proposition — from jordanfurlong.substack.com by Jordan Furlong
The days of selling legal tasks by the hour are ending. Lawyers’ future value lies in safeguarding clients’ legal journeys by overcoming the most challenging obstacles on the way. Part 1 of 2.

As a result, legal work is dividing into two spheres, the first larger than the second: what Gen AI can satisfactorily address, and what it can’t.

Sphere 1: Legal Production. This is all the specialized intellectual work involved in generating legal solutions: researching, issue-spotting, summarizing, synthesizing, drafting, revising, reasoning, and analyzing. This is the bulk of lawyers’ traditional activity and billed hours. In future, it will be done faster, cheaper, and increasingly better with machines — either by clients themselves, or embedded in systems and platforms that reduce the need for lawyer involvement.
Sphere 2: Legal Judgment. This is higher-value work defined by the unpredictability, complexity, and impact of its challenges. In this sphere, you’ll find hard-decision advice, guidance under uncertainty, systematic dispute avoidance, strategic counsel, critical advocacy, risk prioritization, and high-stakes accountability. It’s likely (but far from certain) that this work will remain outside the reach of Gen AI. This is the sphere that holds the potential to support a future legal profession.

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But not every legal journey is so simple or safe that the client can go it alone. Many times, Point B is more like Point F or Point R: a long and tortuous distance away. Many AI-generated maps will suggest a clear and direct route that bears little resemblance to the messy tangles of reality. On even moderately complex legal journeys, the unwelcome and the unexpected are always lurking. Something arises that was nowhere on the map, and until it gets resolved, the client can’t move any further towards their destination.

Below are some items from Jordan’s article — or by following a rabbit trail from his posting:

AI-Native Firms, Built by Private Equity, Will Strain Legacy Model — from news.bloomberglaw.com by Eric Dodson Greenberg

The emergence of AI-native law firms reveals the limits of a fixed binary that has characterized the legal market over the last year.
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The straightest path to AI law firms isn’t innovation within the legacy model, or capital investing around it, but external capital being deployed to build competitors to legacy firms. These firms use AI and narrow regulatory openings to create from scratch tech-enabled law firms.

Not acquire them. Not invest around them.

Build them.

This third path is no longer theoretical.

The $3,500 Hour vs. The $500 Contract — from legaltechnologyhub.com by Brandi Pack

While rates at the top continue climbing, the operational foundation of legal work is being rebuilt.
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Its pricing reflects that structure. Contract review between three and 50 pages costs $500. Short agreements are $250. Longer contracts are billed per page. Drafting from scratch is offered at a fixed fee.

There is no running clock.

The premise is straightforward. If generative AI materially reduces the time required for standardized work, the cost base changes. And when the cost base changes, pricing models eventually follow.

.

Autonomous agents have already transformed engineering. Legal is next.

Agents are moving beyond assisting individuals to operating across entire workflows. This isn’t just a productivity shift, it’s an organizational one. pic.twitter.com/mMOBmJTfIA

— Harvey (@harvey) April 2, 2026

From DSC:
This next item is not from Jordan, but may also be useful to some of you out there:

Want to Work at Legora, Harvey or Another Legal AI Startup? — from legallydisrupted.com by Zach Abramowitz
Podcast with a Biglaw Partner Who Now Occupies a Senior Role at Legora

In Episode 45 of Zach Abramowitz is Legally Disrupted, Kyle and dive into why building tech workflows and writing AI prompts should absolutely be considered billable work. We also explore why AI commoditizing the legal “grinders” and “minders” means old-school social skills are about to become your single biggest competitive advantage. Finally, Kyle goes into great detail about how exactly how he landed a top role at Legora and how others can do the same (hint: merely dropping your resume into a web portal is not enough).

The quest to build a better AI tutor [Barshay]

On 04/06/2026, in 21st century, 24x7x365 access, Artificial Intelligence/Agents/LLMs/and Related, digital learning, education, education technology, emerging technologies, engagement / engaging students, future, game-changing environment, generational differences, homeschooling/homeschoolers, human-computer interaction (HCI), ideas, innovation, instructional design, intelligent systems, intelligent tutoring, K-12 related, learning, learning agents, learning ecosystem, Learning Experience Design, Learning from the Living [Class] Room, learning theories, online learning, participation, personalized/customized learning, research, student-related, teachers, technologies for your home, United States, universities, by Daniel Christian

The quest to build a better AI tutor — from hechingerreport.org by Jill Barshay
Researchers make progress with an older ed tech idea: personalized practice

One promising idea has less to do with how an AI tutor explains concepts and more with what it asks students to practice next.

A team at the University of Pennsylvania, which included some AI skeptics, recently tested this approach in a study of close to 800 Taiwanese high school students learning Python programming. All the students used the same AI tutor, which was designed not to give away answers.

But there was one key difference. Half the students were randomly assigned to a fixed sequence of practice problems, progressing from easy to hard. The other half received a personalized sequence with the AI tutor continuously adjusting the difficulty of each problem based on how the student was performing and interacting with the chatbot.

The idea is based on what educators call the “zone of proximal development.” When problems are too easy, students get bored. When they’re too hard, students get frustrated. The goal is to keep students in a sweet spot: challenged, but not overwhelmed.

The researchers found that students in the personalized group did better on a final exam than students in the fixed problem group. The difference was characterized as the equivalent of 6 to 9 months of additional schooling, an eye-catching claim for an after-school online course that lasted only five months.
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To address this, Chung’s team combined a large language model with a separate machine-learning algorithm that analyzes how students interact with the online course platform — how they answer the practice questions, how many times they revise or edit their coding, and the quality of their conversations with the chatbot — and uses that information to decide which problem to serve up next.

Why Educators Must Become AI Literate, And How to Start [Soundar-Shah]

On 03/28/2026, in Artificial Intelligence/Agents/LLMs/and Related, teachers, by Daniel Christian

Why Educators Must Become AI Literate, And How to Start — from edmentum.com by Priten Soundar-Shah

Much of our focus these few years has been spent helping students learn how to use AI responsibly, especially to combat cheating and plagiarism, and also with consideration given to productive learning, critical thinking, and online safety. But we are still behind on building fundamental literacy for teachers. Recent data supports this literacy gap. For example, Microsoft Education found that 80% of teachers say they are using AI, but 60% have received no or little training. We cannot continue to expect teachers to build student AI literacy without defining what success looks like for educator literacy, and there we’re falling short.

In some instances, AI literacy in the classroom is being defined as the ability to use chat tools to produce some sort of outcome. By that standard, we’re doing much better than we were three years ago. Students and teachers are increasingly turning to AI tools to produce study aids, outlines, drafts, and other content. And, some schools do provide training that is often concentrated on a particular vendor’s tool and how to use it effectively in the classroom.

However, we are leaving out the training that is necessary to help educators learn how to decide when to use or not use the technology and what the implications of that are. For example, I’ve spoken to teachers who have access to a variety of AI tools, have received training on how to use them, but still don’t incorporate them into their workflow, because they don’t know if it’s “right.”

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