A Writer’s Toolkit — from wondertools.substack.com by Mallary Tenore Tarpley and Jeremy Caplan
Useful tools for every stage
Per Jeremy Caplan: My friend and colleague Mallary Tenore Tarpley writes an excellent newsletter, Write at the Edge, with helpful writing tips and best practices. She’s a professor, journalist, and author who has written for The New York Times, The Washington Post, and other outlets. She recently published an important debut book, Slip: Life in the Middle of Eating Disorder Recovery.
Mallary recently interviewed me about writing tools. She wrote up a summary of our conversation, highlighting the tools I talked about. I’m turning the rest of this post over to her summary.
Employees from the world’s biggest AI companies want the US to be ready to slow AI development — from cnn.com by Hadas Gold
Top staffers from the biggest AI and technology companies urged the US government to slow the pace of artificial intelligence development so that safety and security measures can catch up in an open letter.
The US government should support an international effort to develop tools that can “deliberately pace the frontier of automated AI development,” according to the letter.
More than 1,000 employees from frontier AI companies signed the letter, including the chief scientist of OpenAI, one of the ChatGPT developer’s original cofounders, some of Anthropic’s cofoundersand vice presidents at Meta, Google and others.
The letter comes on the heels of major advancements and burgeoning threats from rapidly developing AI systems. OpenAI disclosed last week that two of its test models escaped a lab environment, bypassed its systems to gain access to the open internet and hacked a different company’s internal system.
New grads have to compete with AI for entry-level roles, hiring managers say — from hrdive.com by Lara Ewen
Nearly half of organizations now ask a senior worker plus AI to do the work of several entry-level grads, per a new report.
Dive Brief:
- Hiring managers in the U.S. are betting on artificial intelligence over new graduates, with 48% saying they would rather invest in AI tools than hire and train a recent college graduate, according to a Friday report from ResumeTemplates.com.
- The job market does still have space for 2026 graduates, and 65% of hiring managers said they planned to hire the same number or more this year compared to last year, per the report. However, 23% said they expected to hire fewer 2026 grads this year or none at all, and 12% didn’t know how many they would hire.
- Most hiring concerns centered around workplace skills rather than credentials. Nearly 70% of hiring managers said they had “at least one character concern about recent grads,” including 33% who cited “a lack of work ethic.” Another 76% said recent grads required assistance understanding basic documents such as memos, contracts and budgets.
From DSC:
The vision that I’ve been tracking for well over a decade begins with this graphic:
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Could LearnVector be this next-generation company/platform? Perhaps. Time will tell.
Per Matt Tower via The EdSheet Vol. 38 dated 7/31/26:
- Coursera bets $100M on its own co-founder: Andrew Ng’s new venture LearnVector lands one of the largest single checks in edtech this year — from Coursera, the company he founded 14 years ago.
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Why AI Is Changing What It Means to Be Intelligent — from facultyfocus.com by Lydia Elliott, EdD; article may be behind a paywall
Practical adjustments may include:
- oral explanations of written work
- case-based or scenario-based exercises
- reflective reasoning assignments
- feedback conversations
- requiring students to justify decisions
These approaches do not eliminate AI. They place learning where AI cannot substitute:
What China’s Calligraphy Lessons Can Teach Us About AI — from linkedin.com by Rebecca Winthrop
He recalled visiting a primary school classroom in China several years ago where students were practicing calligraphy—the centuries-old art of writing Chinese characters with brush and ink. While the scene appeared entirely traditional, there was something innovative happening beneath the surface. Embedded in students’ desks was an AI system capable of providing immediate feedback on their brushwork, noting misplaced strokes and suggesting corrections.
The image is striking. Students were learning in much the same way generations before them had learned—brush in hand, immersed in a deeply human and cultural practice. Yet AI was quietly enhancing the experience by providing immediate, personalized feedback that would have been difficult for any teacher to provide to her whole class.
That example captures something important for me. The most promising uses of AI may not be the applications that place technology front and center, but those that support learners to do what they have always done: connect with educators, practice with concentration, and learn and grow.
AI Can Build Your Course, but Can it Design the Learning? — from drphilippahardman.substack.com by Dr. Philippa Hardman
Aka, the current state of AI’s instructional design ability & what it means for L&D
So these models have learned design artefacts in extraordinary volume and design method barely at all. They know what e-learning looks like. They have very little access to why any of it looks that way and whether it actually works or not.
That produces a completely consistent behaviour: ask an LLM for an e-learning module and it generates the statistical centre of every e-learning module it has ever seen. What AI produces isn’t the output of a design process. It’s the output of a sampling process — the statistical centre of every e-learning module it has ever seen.
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So the honest conclusion isn’t that AI can design learning; it’s that AI executes design well when someone who already knows what to ask for is doing the asking and the checking.
This finding gives us a clearer division of labour than we’ve had before and with it a clearer picture of what the AI-enhanced workflow might look like:
The Pedagogy of Trading Places: Lessons from an Unexpected Role Reversal with AI — from facultyfocus.com by Sherrie Myers Bartell, PhD
These disruptions matter. They remind us that teaching is not a static identity but a dynamic posture. They show us that our pedagogical selves are not fixed; they are responsive to context, energy, and attention. They reveal that the qualities we value most in our teaching—curiosity, metaphor, play, and expansiveness—are not automatic. They require care and cultivation.
The AI didn’t “teach” me in the traditional sense. But it did something pedagogically adjacent: it surprised me back into myself.
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.
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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:
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- 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.
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:
- Claude diagnosed my washing machine problem in minutes, and it didn’t cost me a thing — from digitaltrends.com
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?
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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.
- Virtual Conferences at Scale — Lucy Gray — Actionable Innovations Events / GLOW
- Micro-Mentorship — Tami Brewster
- Networked, Values-Driven PD — Julie Meltzer — Institute for Humane Education
- AI-Personalized PD Pathways — Marcus Borders
- Share Stories with Voice — Bethany LaDue Nugent
- 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:
- How I Spent My Time at #ISTElive26: Learning, Connecting, and Looking for What’s Next
A few reflections on AI, global learning, libraries, coaching, creativity, and the conversations that made ISTE meaningful.
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.