AI & Accessibility: 5 Practical Ways to Use LLMs to Support Inclusive Learning — from learningguild.com by Dr. Athena Joyce Stanley (B.A., M.A.E., Ph.D.)

The following strategies focus on how instructional designers can design for accessibility by embedding or enabling AI-supported tools within learning experiences. These approaches move beyond access to content and begin to support access to understanding, expression, and participation.

Personalized Clarification Through AI Copilots
Learners often need additional explanation, but may hesitate to ask questions in live sessions or require more individualized support.

Instructional designers can integrate AI copilots, LLM-powered conversational interfaces, into learning environments to provide on-demand clarification. These tools can act as digital coaches, helping learners understand concepts through simplified explanations and relevant examples.

This approach supports learners who benefit from additional processing time and personalized guidance.

Sample Prompt (for IDs Configuring Copilot Behavior)
You are a workplace learning coach.

Explain the following concept in a clear, simple way:

    • Use plain language
    • Provide one practical, job-relevant example
    • Avoid jargon unless necessary (and define it if used)

Then ask one follow-up question to check for understanding.


Jeff’s posting on LinkedIn.


Teaching AI Literacy with Susan Ray — from intentionalteaching.buzzsprout.com by Derek Bruff and Susan Ray

Per Derek Bruff:
In my conversation with Susan, we talk about that syllabus activity, as well as the AI transparency journals she asks her students to keep during her courses. We also talk about how her personal background prepared her to navigate the challenges and opportunities that AI has posed to her teaching, how she approaches assessing student learning in this age of AI—especially in her online asynchronous courses—and much more.


Building the AI-Native University: Student Success and Lifelong Learning — from coursera.org

In this episode, you’ll discover:

  • Why building an AI-native campus requires a data-first foundation
  • How to build an “achievement architecture” for lifelong success
  • How faculty-built AI tutors cut one program’s course attrition from 40% to 10%
  • What it means to be a lifelong learning companion

We’re Raising the First AI Generation. They’re Pushing Back. (Rebecca Winthrop, Brookings) — from humanistxyz.substack.com by Allison Dulin Salisbury
“Young people harbor real anger about AI because they’re already experiencing its consequences in their schools, relationships, job prospects, and feeds.”

As Rebecca told me:

“Kids harbor real anger. They’re pissed about climate impacts. They’re upset about job prospects. They’re outraged about AI being used to plagiarize writing and produce counterfeit artwork. Their relationships are being affected, and they’re seeing deepfakes in their feeds.”

Our conversation explores what follows from taking those concerns seriously. We discuss cognitive stunting, the case for small, purpose-built AI models over frontier models in education, why students should spend far more time in explorer mode, and how schools can cultivate agency, curiosity, and independent thinking in an age of AI.

The interview ends with one of Rebecca’s most practical recommendations: every school should have a student AI council, not as a symbolic gesture, but with real influence over the tools their schools adopt, the ways they’re used, and the data students are asked to hand over.

Every generation of technology seems to relearn the same lesson: build with people, not for them. AI should be no exception.

 

The image model race just split into four lanes –> find which model fits your work — from  heatherbcooper.substack.com by Heather B. Cooper

In today’s edition:

  • Stop asking which image model is “best” – the top 4
  • Seedream 5.0 Pro’s layer trick
  • Image & Video Prompts
 

Cybersecurity Researchers Identify First Fully Autonomous AI-Driven Ransomware Attack — from campustechnology.com by John K. Waters

Key Takeaways

  • Sysdig documented an AI agent independently chaining reconnaissance, credential theft, lateral movement, persistence, and destructive database encryption across more than 600 payloads, entering through an already-patched vulnerability rather than exploiting any novel AI capability.
  • The agent recovered from a failed step in just 31 seconds without human intervention, diagnosing and correcting a technical error on its own, a level of autonomous troubleshooting that researchers say marks a shift beyond earlier AI-assisted attacks.
  • Cybersecurity firm HiddenLayer reports that autonomous AI agents now account for roughly one in eight reported AI-related security breaches, suggesting JADEPUFFER reflects a broader industry trend rather than an isolated incident.
 

I underestimated this NotebookLM feature until it completely changed how I study — from digitaltrends.com by Shimul Sood

That’s exactly where Mind Maps changed things for me.

In a matter of seconds, NotebookLM analyzed everything I’d uploaded and turned it into a visual map of the topic. Instead of facing a wall of documents, I saw one main idea branching into the key themes, with each of those breaking down into smaller concepts. Before I’d properly read a single source, I already understood how everything connected. That completely changed the way I studied. Rather than asking, “Where do I even start?” I had a clear roadmap showing me what to tackle first, what could wait until later, and how each piece fit into the bigger picture.

Looking back, it’s funny that the feature I ignored for so long is now the one I use first. I still love NotebookLM’s summaries and Audio Overview — they’re great for quickly understanding what’s in your documents. But Mind Maps do something different. They help you understand how everything fits together before you even begin studying. That one change has made a bigger difference than I expected. Instead of diving into a stack of PDFs and hoping I eventually make sense of them, I start with the map. Within a minute, I know the main ideas, how they’re connected, and where I should begin. It makes the whole learning process feel less overwhelming and a lot more structured.

Also recently from Shimul Sood:

 

The economy is shutting young adults out of career-entry jobs, analysis finds — from hrdive.com by Laurel Kalser
Artificial intelligence matters, but in a “narrow, early and age-specific way,” researchers at the Federal Reserve Bank of St. Louis said.

Dive Brief:

  • The decline in overall job openings, exacerbated by a rising demand for artificial intelligence-related skills, is causing employment opportunities for young adults aged 18 to 24 to deteriorate, according to researchers at the Federal Reserve Bank of St. Louis.
  • Between April 2023 — when the U.S. labor market was at its strongest — and December 2025, the employment rate among 18- to 24-year-olds fell by more than 2 percentage points, researchers William Rodgers, III, and Alice Kassens reported in a June 30 post. The decrease appeared primarily as higher unemployment, rather than as labor force exits, “indicating that younger workers were still searching for jobs but with fewer opportunities available,” the authors noted.
  • By contrast, there was no comparable slide for workers aged 25-64, whose employment outcomes remained largely stable, the researchers said.
 

Researchers hid a prompt injection inside a PNG, and AI fell for it — from digitaltrends.com by Shimul Sood

A team of security researchers (professor Sudipta Chattopadhyay and researcher Murali Ediga) has demonstrated an unusual attack that doesn’t target the AI model directly. Instead, it targets what the AI doesn’t pay enough attention to during code reviews. Rather than hiding malicious instructions in lines of code, the researchers tucked them inside an image file. Since many AI review tools treat images as decorative assets rather than as something worth inspecting, the pull request can appear perfectly harmless and sail through the review.

They argue that AI review tools need to become “multimodal” in the truest sense — treating images, documentation, configuration files, and other non-code assets with the same level of scrutiny as source code. If an AI can read a picture, it also needs to understand that the picture could be trying to manipulate it. For developers, this is another reminder that AI coding tools still need supervision. They can dramatically speed up software development, but they also open entirely new attack surfaces that didn’t exist before. The next security risk might not be hidden in thousands of lines of code — it could be sitting inside an image that nobody thought was worth opening.


AI has already fallen into the wrong hands and they’re using it to make bombs — from digitaltrends.com by Shimul Sood

Artificial intelligence has quickly become the go-to tool for everything from writing emails and summarizing meetings to helping students study or developers debug code. But the same technology that saves people time can also be misused, and a new report suggests that terrorist organizations are finding ways to do exactly that.

According to a research paper shared with The New York Times ahead of its publication, researchers found evidence that members of Boko Haram have been using popular AI chatbots to support both day-to-day activities and combat-related tasks. Interviews with 27 former members conducted in Nigeria over the past two years suggest that tools such as ChatGPT, Gemini, Claude, Grok, Meta AI, and DeepSeek were used to gather technical information, troubleshoot weapons, and even assist with planning attacks.

This wasn’t just a few bad actors messing around
What makes the findings especially concerning is that this wasn’t described as the work of a few individuals experimenting with AI. The report claims the group’s use of AI had become organized, with dedicated teams, internal training, and knowledge shared between members. Researchers also say some users managed to bypass built-in safety protections designed to prevent AI from responding to requests related to violence.

 

Reconnecting Professional Learning — from elemenous.substack.com by Lucy Gray
Reflections from My Wednesday ISTE Panel

At ISTE this year, I facilitated a Wednesday morning panel on professional learning. The session I organized was titled, The Future of Professional Learning: Connecting Educators Across Borders. I was joined by fellow Apple Distinguished Educators Tami Brewster, Bethany LaDue Nugent, Marcus Borders, Jason Krug and global educator Julie Meltzer. Our focus: How do we move professional development away from isolated, one-time experiences and toward something more connected, meaningful, and human?

The panel was grounded in a few simple but powerful ideas: professional learning should nurture, guide, and empower. It should be relevant, contextual, reflective, and sustained. It should recognize educators as capable professionals, not as people who are broken and need to be fixed. Too often, professional development is still designed around deficit thinking. Too often, teachers’ needs are not the starting point, and sessions become dry, one-directional experiences where information is delivered at them rather than built through conversation and collaboration, with little choice, little personalization, and little connection to the realities of their classrooms. Our panel served as a call to action to re-think professional learning.

We explored six different approaches to professional learning, and while each model was distinct, a clear through line emerged: relationships matter most.

  1. Virtual Conferences at Scale — Lucy Gray — Actionable Innovations Events / GLOW
  2. Micro-Mentorship — Tami Brewster
  3. Networked, Values-Driven PD — Julie Meltzer — Institute for Humane Education
  4. AI-Personalized PD Pathways — Marcus Borders
  5. Share Stories with Voice — Bethany LaDue Nugent
  6. Speak Your Crazy — Jason Krug

  • Our Padlet – Share resources and introduce yourself
  • Our Slides – Meet our panelists and learn about our work
  • Our Google NotebookLM – This notebook contains dozens of resources related to research and best practices in educator professional learning
  • The Connected PD App – This is an app I’m building in Base 44 to help people plan great professional learning experiences

Also from Lucy Gray, see the following for some nice tips and resources:

 

Microsoft Discovery Platform Brings Agentic AI to Scientific Research — from campustechnology.com by Chris Paoli

Key Takeaways

  • Microsoft Discovery reaches general availability, bringing agentic AI to scientific research and development workflows.
  • AI agents support hypothesis generation, experimentation, data analysis, and knowledge management at scale.
  • New Discovery app preview enables researchers to explore AI-driven scientific discovery with lower adoption barriers.
 

“Teachers ban it. Employers demand it.”

 


Also relevant/see:


The Shifting Career Ladder — from nafez.substack.com by Nafez Dakkak
AI is changing how work works and quietly removing the pathways through which young people learn to become experts.

AI is reshaping how people build skills, enter professions, and move along the career ladder and through the labour market.

In this conversation, I sit down with Matt Sigelmen founder of LightCast and now the President of Burning Glass Institute. Matt has dedicated his career to understanding the labor market and helping society improve the connections within in it.

Matt and I explore why people and opportunities are often only “a few skills apart,” why entry-level work may be losing its traditional role as the first rung of expertise, and why schools, universities, and employers now need to rethink the pathways that turn potential into mastery.

Educators need to be deeply aligned with what these changes are, and they need to shift the AI discourse from “how” questions to “what” questions. What do we need to teach? What do we need to keep in the curriculum?

 

LEGAL FUTURISMS: Consumer Legal Tech and the Soul of US Democracy

The “Dollar Legal” Thought Experiment

What if the salvation of our democratic enthusiasm doesn’t come from a political revolution, but a technological one? Enter the thought experiment of Dollar Legal.

Imagine a ubiquitous, consumer-facing legal tech platform—accessible from any smartphone—that commoditizes basic legal defense and assertion of rights. It is AI-driven legal agency for the masses, costing no more than a cup of coffee, or perhaps literally a single dollar.

Dollar Legal wouldn’t exist to litigate complex corporate mergers; it would exist to handle the agonizing friction of everyday survival. It instantly analyzes a notice to quit, drafts a legally sound response asserting warranty of habitability defenses, and files it electronically. It forces the bureaucratic machine to pause. It translates the raw, terrifying human experience of a legal threat into the cold rules and procedures that the justice system understands.

And the price, $1.00, at sufficient scale, would support the operation and maintenance of the service.

By deploying Dollar Legal, we fundamentally shift the balance of power. The democratization of legal leverage changes the calculus for bad actors who rely on the silent default judgments of the unrepresented.

 

The Law School Deans Driving AI Innovation in Legal Education — from natlawreview.com by Shivani Vedhere, AI & the Law Newsletter; via Colin S. Levy

Artificial intelligence is no longer a peripheral issue for legal education. It is quickly becoming one of the central questions facing law schools: how to prepare future lawyers for a profession in which AI will affect research, client counseling, litigation strategy, access to justice, and the business of law.

For decades, law schools treated legal technology as an elective or a niche interest for students already inclined toward innovation. That era is ending. Law firms are adopting AI tools at scale and even investing in developing their own tools. Clients are asking harder questions about efficiency, cost, and competence. Courts are sanctioning lawyers and litigants for AI-generated hallucinations, with the number of identified cases in the United States now exceeding 1,000. Students entering the profession will be expected to keep up with this rapidly changing landscape.

The most forward-looking law schools are responding accordingly. That transformation is being driven in large part by a group of innovative law school deans who are treating AI not as a passing trend, but as a structural change in legal education.

These initiatives signal a broader shift in legal academia where law schools are no longer merely debating whether AI belongs in the curriculum. The more pressing question is how deeply, how early, and how responsibly AI should be integrated into legal education.

 

A Comprehensive Report on Teens, Tweens, and AI — from commonsensemedia.org

To find out what that actually looks like day-to-day, we surveyed more than a thousand 9- to 17-year-olds across the country. We asked them how they use AI, how often, and for what.

The Common Sense Media Census: AI Use by Tweens and Teens (2026) is the first in a series we’ll repeat every year to learn how this generation’s relationship with AI evolves over time.

A few things stood out:

  • Kids are using AI for many things. It’s not just a homework helper anymore. For some kids, AI has become a confidant, even though our research is clear that AI companionship is not safe for anyone under 18.
  • Guardrails are thin to nonexistent. Schools are talking about rules more than safety. Three-quarters of kids say their school has discussed what they can and cannot use AI for, but just over half have been taught how to use AI safely.
  • Just like we saw with smartphones and social media, the conversation is once again lagging behind the technology. Nearly half of kids have never had a conversation with their parents about AI safety.
 

Anthropic, NVIDIA Move AI Agents Deeper into Scientific Workflows — from campustechnology.com by John K. Waters

Key Takeaways

  • Anthropic’s Claude Science beta and NVIDIA’s BioNeMo Agent Toolkit show AI agents moving beyond general productivity into specialized scientific research workflows.
  • The companies are emphasizing auditability and reproducibility, including preserved code, environments, message history, reviewer agents, and integration with existing research tools.
  • For enterprises and research teams, the key test will be whether agentic AI can produce traceable, reliable results while keeping humans in control of sensitive data, compute, and scientific judgment.
 

NotebookLM’s 60-second videos turned my doomscrolling curse into something useful — from digitaltrends.com by Shimul Sood

Google has announced Short Video Overviews for NotebookLM, a feature that turns dense documents and complicated sources into 60-second vertical videos that explain key ideas. Instead of staring at pages of notes, you get a quick visual walkthrough of the concept you’re trying to understand.


 

Artificial Intelligence and the Future of Entry-Level Work: A Framework for Safeguarding and Reinventing Early Career Pathways — from the World Economic Forum (weforum.org) and PwC

Artificial intelligence (AI) is reshaping how organizations hire, develop and advance talent, and this is most visible at entry-level. Globally, more than one in three young workers are employed in occupations with medium to high exposure to AI-driven task change. How these roles evolve will have significant implications for organizational performance, workforce participation and economic mobility.

 
© 2025 | Daniel Christian