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.
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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.

 

Corporate Training Solutions That Actually Improve Performance — from blog.upsidelearning.com by Unnati Umare

Designing Learning Around Performance in the Flow of Work
Once it becomes clear that completion does not reliably translate into changed behavior, the next question tends to surface on its own. If training is not failing outright, then what it should be designed around becomes harder to ignore.

In most organizations, the answer remains content. Content is easier to define, easier to build, and easier to track, even when it explains very little about how work actually gets done.

Performance-aligned learning design shifts that starting point by paying closer attention to how work unfolds in practice. Instead of organizing learning around topics or courses, design decisions begin with what a role requires people to notice, decide, and act on during real situations.  

 

Nvidia, Eli Lilly announce $1 billion investment in AI drug discovery lab — from finance.yahoo.com by Laura Bratton

AI chipmaker Nvidia (NVDA) and pharmaceutical giant Eli Lilly (LLY) on Monday announced that the two companies would jointly invest $1 billion to create a lab in San Francisco focused on using AI to accelerate drug discovery.

The $1 billion investment will be spent over five years on infrastructure, compute, and talent for the lab. Nvidia’s engineers will work alongside Lilly’s experts in biology, science, and medicine to generate large-scale data and build AI models to advance medicine development. The lab’s work will begin early this year, the companies said.

 

Shoppers will soon be able to make purchases directly through Google’s Gemini app and browser.



Google and Walmart Join Forces to Shape the Future of Retail — from adweek.com by Lauren Johnson
At NRF, Sundar Pichai and John Furner revealed how AI and drones will shape shopping in 2026 and beyond

One of the biggest reveals is that shoppers will be able to purchase Walmart and Sam’s Club products through Google’s AI chatbot Gemini.


 

The Hard Part of Legal AI Isn’t the Technology — from linkedin.com by Colin S. Levy

Selecting AI tools for legal teams is no longer about novelty or experimentation. It is about aligning technology with judgment, workflows, and risk tolerance. Teams that approach AI with specificity, skepticism, and operational discipline place themselves in a far stronger position to capture real value and avoid unwelcome surprises once the demo environment disappears.


Addendum on 1/8/26:

 

6 Ed Tech Tools to Try in 2026 — from cultofpedagogy.com by Jennifer Gonzalez

It’s that time again ~ the annual round-up of tech tools we think are worth a look this year. This year I really feel like there’s something for everyone: history teachers, math and science teachers, people who run makerspaces, teachers interested in music or podcasting, writing teachers, special ed teachers, and anyone whose course content could be made clearer through graphic organizers.


Also somewhat relevant here, see:


 

At CES 2026, Everything Is AI. What Matters Is How You Use It — from wired.com by Boone Ashworth
Integrated chatbots and built-in machine intelligence are no longer standout features in consumer tech. If companies want to win in the AI era, they’ve got to hone the user experience.

Beyond Wearables
Right now, AI is on your face and arms—smart glasses and smart watches—but this year will see it proliferate further into products like earbuds, headphones, and smart clothing.

Health tech will see an influx of AI features too, as companies aim to use AI to monitor biometric data from wearables like rings and wristbands. Heath sensors will also continue to show up in newer places like toilets, bath mats, and brassieres.

The smart home will continue to be bolstered by machine intelligence, with more products that can listen, see, and understand what’s happening in your living space. Familiar candidates for AI-powered upgrades like smart vacuums and security cameras will be joined by surprising AI bedfellows like refrigerators and garage door openers.


Along these lines, see
live updates from CNET here.


ChatGPT is overrated. Here’s what to use instead. — from washingtonpost.com by Geoffrey A. Fowler
When I want help from AI, ChatGPT is no longer my default first stop.

I can tell you which AI tools are worth using — and which to avoid — because I’ve been running a chatbot fight club.

I conducted dozens of bot challenges based on real things people do with AI, including writing breakup texts and work emailsdecoding legal contracts and scientific researchanswering tricky research questions, and editing photos and making “art.” Human experts including best-selling authors, reference librarians, a renowned scientist and even a Pulitzer Prize-winning photographer judged the results.

After a year of bot battles, one thing stands out: There is no single best AI. The smartest way to use chatbots today is to pick different tools for different jobs — and not assume one bot can do it all.


How Collaborative AI Agents Are Shaping the Future of Autonomous IT — from aijourn.com by Michael Nappi

Some enterprise platforms now support cross-agent communication and integration with ecosystems maintained by companies like Microsoft, NVIDIA, Google, and Oracle. These cross-platform data fabrics break down silos and turn isolated AI pilots into enterprise-wide services. The result is an IT backbone that not only automates but also collaborates for continuous learning, diagnostics, and system optimization in real time.


Nvidia dominated the headlines in 2025 — these were its 15 biggest events of the year — from finance.yahoo.com by Daniel Howley

It’s difficult to think of any single company that had a bigger impact on Wall Street and the AI trade in 2025 than Nvidia (NVDA).

Nvidia’s revenue soared in 2025, bringing in $187.1 billion, and its market capitalization continued to climb, briefly eclipsing the $5 trillion mark before settling back in the $4 trillion range.

There were plenty of major highs and deep lows throughout the year, but these 15 were among the biggest moments of Nvidia’s 2025.


 

 

How Your Learners *Actually* Learn with AI — from drphilippahardman.substack.com by Dr. Philippa Hardman
What 37.5 million AI chats show us about how learners use AI at the end of 2025 — and what this means for how we design & deliver learning experiences in 2026

Last week, Microsoft released a similar analysis of a whopping 37.5 million Copilot conversations. These conversation took place on the platform from January to September 2025, providing us with a window into if and how AI use in general — and AI use among learners specifically – has evolved in 2025.

Microsoft’s mass behavioural data gives us a detailed, global glimpse into what learners are actually doing across devices, times of day and contexts. The picture that emerges is pretty clear and largely consistent with what OpenAI’s told us back in the summer:

AI isn’t functioning primarily as an “answers machine”: the majority of us use AI as a tool to personalise and differentiate generic learning experiences and – ultimately – to augment human learning.

Let’s dive in!

Learners don’t “decide” to use AI anymore. They assume it’s there, like search, like spellcheck, like calculators. The question has shifted from “should I use this?” to “how do I use this effectively?”


8 AI Agents Every HR Leader Needs To Know In 2026 — from forbes.com by Bernard Marr

So where do you start? There are many agentic tools and platforms for AI tasks on the market, and the most effective approach is to focus on practical, high-impact workflows. So here, I’ll look at some of the most compelling use cases, as well as provide an overview of the tools that can help you quickly deliver tangible wins.

Some of the strongest opportunities in HR include:

  • Workforce management, administering job satisfaction surveys, monitoring and tracking performance targets, scheduling interventions, and managing staff benefits, medical leave, and holiday entitlement.
  • Recruitment screening, automatically generating and posting job descriptions, filtering candidates, ranking applicants against defined criteria, identifying the strongest matches, and scheduling interviews.
  • Employee onboarding, issuing new hires with contracts and paperwork, guiding them to onboarding and training resources, tracking compliance and completion rates, answering routine enquiries, and escalating complex cases to human HR specialists.
  • Training and development, identifying skills gaps, providing self-service access to upskilling and reskilling opportunities, creating personalized learning pathways aligned with roles and career goals, and tracking progress toward completion.

 

 
© 2025 | Daniel Christian