Kling 3.0 just launched. The best video model yet. — from heatherbcooper.substack.com by Heather Cooper
& workflows from Imagine Art 1.5 pro, Pixverse Real-Time Video & Genspark

In today’s edition:

  • Kling 3.0: Everyone a Director
  • Character consistency, native audio, 15-second generations & first results
  • Image & Video Prompts
  • Imagine Art 1.5 Pro, Genspark AI Workspace 2.0 & PixVerse Real-Time Video Workflows

Kling 3.0: Everyone a Director
Kling just dropped version 3.0, and it’s a legitimate leap forward for AI video production (Kling is the GOAT). After spending early access time testing the new capabilities, I can confirm this is the most significant update to video generation tools I’ve seen in months.

Key highlights:

  • Character & Element Consistency:
  • Flexible Video Production:
  • Native Audio with Dialogue & Singing:
  • Enhanced Image Generation:
  • Professional Output:
 

Amid AI and Labor Market Changes, Companies Look to Grow Their Own Skilled Workers — from workshift.org by Colleen Connolly

The explosion of artificial intelligence, combined with slowing growth in the labor force, has many companies reconsidering how they hire and develop workers. Where they once relied on colleges and universities for training, a growing number of companies are now looking in-house.

Investment in developing employees and would-be hires is becoming a key differentiator for companies, according to a new report from the Learning Society, a collaborative effort led out of the Stanford Center on Longevity. And that’s true even as AI adoption grows.

The Big Idea: The report authors interviewed 15 human resources executives from major firms, which ranged in size from Hubbell, an electric and utility product manufacturer with about 17K employees, to Walmart with more than 2M employees. The authors asked about four topics: the impact of AI and technology on work, skill building and talent development, supporting workers over longer working lives, and new partnerships between businesses and higher education.

 

Jim VandeHei’s note to his kids: Blunt AI talk — from axios.com by CEO Jim VandeHei
Axios CEO Jim VandeHei wrote this note to his wife, Autumn, and their three kids. She suggested sharing it more broadly since so many families are wrestling with how to think and talk about AI. So here it is …

Dear Family:
I want to put to words what I’m hearing, seeing, thinking and writing about AI.

  • Simply put, I’m now certain it will upend your work and life in ways more profound than the internet or possibly electricity. This will hit in months, not years.
  • The changes will be fast, wide, radical, disorienting and scary. No one will avoid its reach.

I’m not trying to frighten you. And I know your opinions range from wonderment to worry. That’s natural and OK. Our species isn’t wired for change of this speed or scale.

  • My conversations with the CEOs and builders of these LLMs, as well as my own deep experimentation with AI, have shaken and stirred me in ways I never imagined.

All of you must figure out how to master AI for any specific job or internship you hold or take. You’d be jeopardizing your future careers by not figuring out how to use AI to amplify and improve your work. You’d be wise to replace social media scrolling with LLM testing.

Be the very best at using AI for your gig.

more here.


Also see:


Also relevant/see:

 

Anthropic unveils Claude legal plugin and causes market meltdown — from legaltechnology.com

Generative AI vendor Anthropic has unveiled a legal plugin that helps customise its large language model Claude for legal tasks such as document review, sending public legal software stocks into an ensuing spin today (3 February).

Anthropic entering the legal tech fray comes as part of the launch of a number of different plugins that help users instruct Claude on how to get work done and what tools and data to pull from. A sales plugin, for example could connect Claude to your CRM and knowledge base to help with prospect research and follow ups. The legal plug-in is described as being capable of, for example, reviewing documents, flagging risks, NDA triage, and tracking compliance. The significance is that Anthropic is shifting from model supplier to the application layer and workflow owner.

The announcement is hitting public publishing and legal software companies hard.


Also related/see:

Anthropic’s Legal Plugin for Claude Cowork May Be the Opening Salvo In A Competition Between Foundation Models and Legal Tech Incumbents — from lawnext.com by Bob Ambrogi

Two weeks after introducing a new general-purpose “agentic” work mode called Claude Cowork, Anthropic has now rolled out a legal plugin aimed squarely at the legal workflows of in-house counsel, including contract review, NDA triage, compliance checks, briefings and templated responses.

It is configurable to an organization’s own playbook and risk tolerances, and Anthropic explicitly frames it as assistance, not advice, cautioning that outputs should be reviewed by licensed attorneys.

It may sound like just another feature drop in a crowded AI market. But for legal tech, it is landing more like a tsunami than a drop. For the first time, a foundation-model company is packaging a legal workflow product directly into its platform, rather than merely supplying an API to legal-tech vendors.

 

The Essential Retrieval Practice Handbook — from edutopia.org
Retrieval practice is one of the most effective ways to strengthen learning. Here’s a collection of our best resources to use in your classroom today.
January 29, 2026


Also see:

What is retrieval practice? — from retrievalpractice.org

When we think about learning, we typically focus on getting information into students’ heads. What if, instead, we focus on getting information out of students’ heads?


 

Farewell to Traditional Universities | What AI Has in Store for Education

Premiered Jan 16, 2026

Description:

What if the biggest change in education isn’t a new app… but the end of the university monopoly on credibility?

Jensen Huang has framed AI as a platform shift—an industrial revolution that turns intelligence into infrastructure. And when intelligence becomes cheap, personal, and always available, education stops being a place you go… and becomes a system that follows you. The question isn’t whether universities will disappear. The question is whether the old model—high cost, slow updates, one-size-fits-all—can survive a world where every student can have a private tutor, a lab partner, and a curriculum designer on demand.

This video explores what AI has in store for education—and why traditional universities may need to reinvent themselves fast.

In this video you’ll discover:

  • How AI tutors could deliver personalized learning at scale
  • Why credentials may shift from “degrees” to proof-of-skill portfolios
  • What happens when the “middle” of studying becomes automated
  • How universities could evolve: research hubs, networks, and high-trust credentialing
  • The risks: cheating, dependency, bias, and widening inequality
  • The 3 skills that become priceless when information is everywhere: judgment, curiosity, and responsibility

From DSC:
There appears to be another, similar video, but with a different date and length of the video. So I’m including this other recording as well here:


The End of Universities as We Know Them: What AI Is Bringing

Premiered Jan 27, 2026

What if universities don’t “disappear”… but lose their monopoly on learning, credentials, and opportunity?

AI is turning education into something radically different: personal, instant, adaptive, and always available. When every student can have a 24/7 tutor, a writing coach, a coding partner, and a study plan designed specifically for them, the old model—one professor, one curriculum, one pace for everyone—starts to look outdated. And the biggest disruption isn’t the classroom. It’s the credential. Because in an AI world, proof of skill can become more valuable than a piece of paper.

This video explores the end of universities as we know them: what AI is bringing, what will break, what will survive, and what replaces the traditional path.

In this video you’ll discover:

  • Why AI tutoring could outperform one-size-fits-all lectures
  • How “degrees” may shift into skill proof: portfolios, projects, and verified competency
  • What happens when the “middle” of studying becomes automated
  • How universities may evolve: research hubs, networks, high-trust credentialing
  • The dark side: cheating, dependency, inequality, and biased evaluation
  • The new advantage: judgment, creativity, and responsibility in a world of instant answers
 

FutureFit AI — helping build reskilling, demand-driven, employment, sector-based, and future-fit pathways, powered by AI
.


The above item was from Paul Fain’s recent posting, which includes the following excerpt:

The platform is powered by FutureFit AI, which is contributing the skills-matching infrastructure and navigation layer. Jobseekers get personalized recommendations for best-fit job roles as well as education and training options—including internships—that can help them break into specific careers. The project also includes a focus on providing support students need to complete their training, including scholarships and help with childcare and transportation.

 

The Learning and Employment Records (LER) Report for 2026: Building the infrastructure between learning and work — from smartresume.com; with thanks to Paul Fain for this resource

Executive Summary (excerpt)

This report documents a clear transition now underway: LERs are moving from small experiments to systems people and organizations expect to rely on. Adoption remains early and uneven, but the forces reshaping the ecosystem are no longer speculative. Federal policy signals, state planning cycles, standards maturation, and employer behavior are aligning in ways that suggest 2026 will mark a shift from exploration to execution.

Across interviews with federal leaders, state CIOs, standards bodies, and ecosystem builders, a consistent theme emerged: the traditional model—where institutions control learning and employment records—no longer fits how people move through education and work. In its place, a new model is being actively designed—one in which individuals hold portable, verifiable records that systems can trust without centralizing control.

Most states are not yet operating this way. But planning timelines, RFP language, and federal signals indicate that many will begin building toward this model in early 2026.

As the ecosystem matures, another insight becomes unavoidable: records alone are not enough. Value emerges only when trusted records can be interpreted through shared skill languages, reused across contexts, and embedded into the systems and marketplaces where decisions are made.

Learning and Employment Records are not a product category. They are a data layer—one that reshapes how learning, work, and opportunity connect over time.

This report is written for anyone seeking to understand how LERs are beginning to move from concept to practice. Whether readers are new to the space or actively exploring implementation, the report focuses on observable signals, emerging patterns, and the practical conditions required to move from experimentation toward durable infrastructure.

 

“The building blocks for a global, interoperable skills ecosystem are already in place. As education and workforce alignment accelerates, the path toward trusted, machine-readable credentials is clear. The next phase depends on credentials that carry value across institutions, industries, states, and borders; credentials that move with learners wherever their education and careers take them. The question now isn’t whether to act, but how quickly we move.”

– Curtiss Barnes, Chief Executive Officer, 1EdTech

 


The above item was from Paul Fain’s recent posting, which includes the following excerpt:

SmartResume just published a guide for making sense of this rapidly expanding landscape. The LER Ecosystem Report was produced in partnership with AACRAO, Credential Engine, 1EdTech, HR Open Standards, and the U.S. Chamber of Commerce Foundation. It was based on interviews and feedback gathered over three years from 100+ leaders across education, workforce, government, standards bodies, and tech providers.

The tools are available now to create the sort of interoperable ecosystem that can make talent marketplaces a reality, the report argues. Meanwhile, federal policy moves and bipartisan attention to LERs are accelerating action at the state level.

“For state leaders, this creates a practical inflection point,” says the report. “LERs are shifting from an innovation discussion to an infrastructure planning conversation.”

 
 

Planning Your L&D Hiring for Next Year? Start With Skills, Salary Ranges, and Realistic Expectations — from teamedforlearning.com

Salary transparency laws across many states now require organizations to publish compensation ranges. While this can feel like a burden, the truth is: transparency can dramatically speed up hiring. Candidates self-select, mismatches decrease, and teams save time.

But transparency only works when the salary range itself is grounded in reality. And that’s where many organizations struggle.

Posting a salary range is the easy part.
Determining a fair, defensible range is where the work happens.

Also from Teamed for Learning, see:

Hiring Trends For 2026 
The learning industry shifts fast, and this year is no exception. Here’s what’s shaping the hiring landscape right now:

  • AI is now a core skill, not a bonus
  • Project management is showing up in every job description
  • Generalists with business awareness are beating tool-heavy candidates
  • Universities and edtech companies are speeding up content refresh cycles
  • Hiring budgets are tight – but expectations aren’t easing up
 
 

From Stephanie T.’s posting out on LinkedIn

The lesson isn’t to make school reports more like Spotify Wrapped.

It’s to design reports that are accessible, timely, and readable — without losing the humanity that makes teacher insight meaningful.

If a report is too difficult to access, or arrives too late to matter, who is it really for?

 
 

Which AI Video Tool Is Most Powerful for L&D Teams? — from by Dr. Philippa Hardman
Evaluating four popular AI video generation platforms through a learning-science lens

Happy new year! One of the biggest L&D stories of 2025 was the rise to fame among L&D teams of AI video generator tools. As we head into 2026, platforms like Colossyan, Synthesia, HeyGen, and NotebookLM’s video creation feature are firmly embedded in most L&D tech stacks. These tools promise rapid production and multi-language output at significantly reduced costs —and they deliver on a lot of that.

But something has been playing on my mind: we rarely evaluate these tools on what matters most for learning design—whether they enable us to build instructional content that actually enables learning.

So, I spent some time over the holiday digging into this question: do the AI video tools we use most in L&D create content that supports substantive learning?

To answer it, I took two decades of learning science research and translated it into a scoring rubric. Then I scored the four most popular AI video generation platforms among L&D professionals against the rubric.
.

 


For an AI-based tool or two — as they regard higher ed — see:

5 new tools worth trying — from wondertools.substack.com by Jeremy Kaplan

YouTube to NotebookLM: Import a Whole Playlist or Channel in One Click
YouTube to NotebookLM is a remarkably useful new Chrome extension that lets you bulk-add any YouTube playlists, channels, or search results into NotebookLM. for AI-powered analysis.

What to try

  • Find or create YouTube playlists on topics of interest. Then use this extension to ingest those playlists into NotebookLM. The videos are automatically indexed, and within minutes you can create reports, slides, and infographics to enhance your learning.
  • Summarize a playlist or channel with an audio or video overview. Or create quizzes, flash cards, data tables, or mind maps to explore a batch of YouTube videos. Or have a chat in NotebookLM with your favorite video channel. Check my recent post for some YouTube channels to try.
 

What AI-Generated Voice Technology Means For Creators And Brands — from bitrebels.com by Ryan Mitchell

Voice has become one of the most influential elements in how digital content is experienced. From podcasts and videos to apps, ads, and interactive platforms, spoken audio shapes how messages are understood and remembered. In recent years, the rise of the ai voice generator has changed how creators and brands approach audio production, lowering barriers while expanding creative possibilities.

Rather than relying exclusively on traditional voice recording, many teams now use AI-generated voices as part of their content and brand strategies. This shift is not simply about efficiency; it reflects broader changes in how digital experiences are produced, scaled, and personalised.

The Future Role Of AI-Generated Voice
As AI voice technology continues to improve, its role in creative and brand workflows will likely expand. Future developments may include more adaptive voices that respond to context, audience behaviour, or emotional cues in real time. Rather than replacing traditional voice work, AI-generated voice is becoming another option in a broader creative toolkit, one that offers speed, flexibility, and accessibility.

 
© 2025 | Daniel Christian