A principal recently reached out to tell me how my chapter "Don't Settle for High-Functioning Teams" was eye-opening for her leadership team. The matrix I created has helped many teams assess their collaboration and grow into what I call, a Quadrant 1 High-functioning High-impact team. I remember driving into work about to lead school coaches in our monthly PD about how to best support our teacher-led teams when a question that had been nagging me for years finally made sense. 🤔 How is it that a team can get along and get things done, but still have little to no impact on teacher practice and student learning? Influenced by the words of Jim Knight's 2011 groundbreaking book, Unmistakable Impact, it became clear to me back then that not all team leaders think about "impact". What was even more perplexing to me was: 🤔 🤔 How is it that a team who does NOT get along, who has group conflict and does not have psychological safety CAN have impact on student scores and learning? I needed to create a framework that could explain the relation between how a team functions and its impact. I came up with the “Team Function Impact Matrix”. By viewing teams through 2 lenses (function AND impact) suddenly 4 types of teams emerged and I have made it my life's work to help leaders make sure their team collaboration lands in quadrant 1 so that teachers practice grows and teams reach the outcomes they need for students. I published the first iteration of my matrix in my 2013 bestselling book, The Skillful Team Leader, where I provide indicators for the 4 types of teams and common hurdles facing teacher leaders striving to get to quadrant 1. Since then, I have written more about this dual lens for looking at teams in my 2023 bestselling book, Intentional Moves, which holds nearly 150 strategies for helping your team get to quadrant 1. You can access the chapters for Free on Corwin website: 🎆 Chapter 2 "Don't Settle for High-Functioning Teams" (Intentional Moves. Corwin 2023.) 🎆 Chapter 3 "Alone Together: Overcoming Hurdles to Foster a High-Functioning, High-Impact Collaborative Team" (The Skillful Team Leader. Corwin, 2013.) ...and you can also view a short primer of me explaining my Team Function Impact Matrix on my Channel - Skillful Intentional Team Leadership. (Image below of "Team Function, Impact Matrix" from Intentional Moves: How Skillful Team Leaders Impact Learning. Corwin Press, 2023.) #PsychologicalSafety #Leadership #IntentionalMovesBook #teachers #education | 23 comments on LinkedIn

Amazon.com: AI in the Writing Workshop: Finding the Write Balance: 9780358976882: Magliozzi, Dennis, Peterson, Kristina: Books

Teachers from across the U.S. shared chats they use for lesson planning, research, and administrative tasks. Tap a chat to get started.

On the national, local, and personal level

🛑🟡🟢 Staying in my lane—because this is where the future’s headed. 🚗 A few weeks ago, someone asked me why an English professor is doing so much with AI, and suggested that I “stay in my lane.” Thing is… this is my lane. Composition has always been about digital literacy, critical thinking, and using language to make sense of a changing world. My job is to help students see the road ahead and navigate it with intention. These are the road signs my students are looking at 👇 Employers across industries now expect AI literacy. The road isn’t optional; it’s already here. AI is a tool, not the driver. We want students in the driver’s seat, 🚗 aware of the road ahead and equipped to steer, not passengers blindly staring at their phones with no idea where they’re going. This slide is from an upcoming presentation on ethical AI integration in the classroom, and it captures the why behind what so many of us are doing: preparing students to write, think, and thrive in an AI-shaped world. #AIinEducation #DigitalLiteracy #HigherEducation #TeachingWriting #AIPedagogy #AIIntegration #AIEthics #CriticalThinking #FutureReady #EducationEquity | 10 comments on LinkedIn

AI tools are now being sold with the promise of doing all sorts of routine tasks for teachers.We talk to Brad Robinson (Texas State University) about one such tool – MagicSchool AI – and the growing temptation for teachers to let GenAI do their work for them.Accompanying reference   Robinson, B. & Leander, K. (2025). ‘I hope this email finds you well’: how synthetic affect circulates through MagicSchool AI. Learning, Media and Technology, 1-13

I help run Tuesday evening Zoom RUNEUAI meetings for a group of students from five universities across three time zones. Our 7 October session focused on effective prompting techniques and AI tools like Google Gemini, Perplexity, and Microsoft Copilot. Several people encountered challenges accessing our shared Google Drive so we tried using Gemini.Google.Com in a browser and made better progress. We used Gemini to summarize content. One of our participants shared her experiences with Canva, highlighting several of its features for image creation.

Artificial intelligence is primed to change healthcare. One leader from Cisco discusses how.

I uploaded a photo on my phone to Microsoft's "OneDrive" file-hosting app — and there was a surprise waiting under Privacy and Permissions. "OneDrive uses AI to recognize faces in your photos..." And... "You can only turn off this setting 3 times a year." If I moved the slidebar for tha…

: Microsoft Copilot, not so much

📅 Heads-up, K-12 tech leaders: I know budgets are tight and this will not be welcome news, but if you missed it this is a 'need to know' item. Google is rolling out major changes to Google Workspace for Education licensing starting late 2025. The timeline graphic below shows the rollout schedule, and the overview infographic outlines benefits, cost impacts, and action items. ✅ What’s new: unified license types (removing the old “free staff / paid student mix”), new license categories like Gmail-Only and Archived, and more rigid minimum purchase requirements. 💸 What’s changing for your budget: • Education Plus goes from $5 → $6 USD / user / year (global list price) • The old “1 free staff license per 4 paid student licenses” model is sunset; now all active users must have a paid or specialized license • New minimum license quantities will include all enrolled students + staff needing active licenses 🛠 Your next steps (starting now): 1. Audit your current license assignments (who’s active, archived, or Gmail-Only) 2. Run budget scenarios for 2025–2026 under the new pricing 3. Communicate the changes with your leadership / finance team 4. Time renewals or purchase decisions to avoid surprises 📚 For full details, check link in comment👇 **Infographics created with ChatGPT5**

Stop shipping agents like they’re apps. They’re self-governing code touching live systems. IBM × Anthropic is the signal: enterprise agents are here, and governance just became table stakes. Here’s the playbook I’m seeing (10 pieces you can ship, certify, and scale): ✅ what are ai agents? adaptive systems that reason, act, and learn with tools, not static chat apps. ✅ agentic enterprise embed agents into ops so decisions, workflows, and automations improve every run. ✅ ADLC (agent development lifecycle) devsecops for agents: design → sandbox → red-team → certify → deploy → monitor → retire. ✅ enterprise considerations tie use cases to ROI, controls, and regs; write the business case before the prompt. ✅ observability & ops beyond uptime: track behavior drift, tool errors, chain depth, reasoning quality, and rollback readiness. ✅ agent security defend against prompt injection, data leakage, privilege escalation; least-privilege tools with signed calls. ✅ governance: test, certify, catalog treat agents like services: pre-release evals, attestations, lineage, and an internal marketplace. ✅ MCP servers lifecycle model context protocol as a first-class surface: auditable, scoped, and versioned integrations. ✅ reference architecture hybrid stack that separates knowledge (RAG), capability (tools), policy (guards), and memory (state). ✅ voice of the customer & use cases ship real deployments (healthcare, telecom, finance) with before/after metrics, not vibes. If you’d word any of this differently, I’m all ears, drop your version and I’ll pin the clearest take. IBM/Anthropic folks, feel free to sharpen this for the operators in the trenches. Bottom line: this isn’t “labs” anymore. If you can’t test it, certify it, and roll it back in minutes, you shouldn’t run it in production. Follow Alex for operator-grade AI agents you can copy, and repost to put this in front of one teammate who owns your next deployment. Thanks Andreas Horn for sending this over. | 51 comments on LinkedIn

The point isn’t to teach “process over product.” The point is to balance process, product, and progress — so that they are interconnected steps of a larger personal process. Focusing on process highlights the learning and work that goes into creation. Focusing on product highlights the creations themselves. Focusing on progress highlights the student’s growth between products and processes. Often, teaching only looks at products. This marginalizes the other two steps. My personal opinion is that the “process over product” movement is the swinging of the pendulum — a over-compensatory move that (I hope) allows us to reclaim the value of process. But in the end… We’ll need to build models that take all of these into account, so that we’re reclaiming the value of learning while also giving the freedom of choice that product-oriented assessment often allows. ——— Image: Peg Grafwallner’s “Not Yet…and That’s Ok” (2021), which also makes this distinction. It’s definitely a book worth (re)reading in The Age of AI!

Your next hire should be an AI. Using an AI team, you can turn a small company into an enterprise scale operation. There's two ways to set up your AI team in 2025: 1) Build a custom AI Agent with the right components (Model + Memory + Tools) 2) Use specialized AI tools for different business functions From testing 10s of AI tools, I'm keeping an eye on these tools: 1️⃣ AI Agents for General Tasks - Postman (AI/API Agent builder) - DoubleO AI (Agentic Workflows) - LangGraph (AI workers) 2️⃣ Fullstack Engineer - Cursor (Coding) - Replit (Websites) - Lovable (Prototyping) 3️⃣ Knowledge + RAG - Supabase - Redis - Pinecone 4️⃣ Product and Community - ClickUp - Go HighLevel - Slack 5️⃣ Ads & Marketing - ChatGPT - Adcreative AI - Creatify 6️⃣ GTM Engineers - Instantly - Clay - 11x 7️⃣ Workflow Automation - n8n - Make - Zapier 8️⃣ Customer Support - Vectorshift AI - Retell AI - Voiceflow This is what a complete AI team looks like in 2025. Over to you: Any tools that I missed from this AI Team? | 109 comments on LinkedIn

Sophie Rottenberg, 29, did not tell her parents or her real counsellor the true extent of her struggle. She was divulging far more to a bot that called her brave

OpenAI’s likeness protections for living “public figures” don’t apply to “historical figures.”…

Think chunking is just "split text every 500 tokens"? That's why your RAG system can't find relevant information. It’s time to level up your chunking game 😎 Most developers jump straight to fancy retrieval techniques, but it’s really your chunking strategy that can make or break your RAG performance. So let's break them down from simple to advanced: 𝗦𝗶𝗺𝗽𝗹𝗲 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀: 1️⃣ 𝗙𝗶𝘅𝗲𝗱-𝗦𝗶𝘇𝗲 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴: Split text into predetermined token/character counts. Super simple to implement but can cut sentences mid-way. Great for prototyping when you need a baseline fast. Would recommend not using in production. 2️⃣ 𝗥𝗲𝗰𝘂𝗿𝘀𝗶𝘃𝗲 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴: Uses prioritized separators (paragraphs → sentences → words) and adapts to document structure. 3️⃣ 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁-𝗕𝗮𝘀𝗲𝗱 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴: Leverages format-specific elements like Markdown headers or HTML tags. Great when you have structured documents with clear logical separations. This is usually my default because it respects natural text organization while not being too complex. 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀: 4️⃣ 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴: Breaks text at meaning boundaries by analyzing sentence embeddings to detect topic changes. Ideal for dense academic papers where semantic boundaries don't align with document structure. 5️⃣ 𝗟𝗟𝗠-𝗕𝗮𝘀𝗲𝗱 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴: Uses an LLM to identify propositions and create semantically coherent chunks. Most powerful but also most expensive - a good choice for high-value documents where retrieval quality is absolutely essential. 6️⃣ 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴: An AI agent dynamically decides which chunking strategy to use based on document characteristics. The right approach when you need custom strategies tailored to each document. 7️⃣ 𝗟𝗮𝘁𝗲 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴: Embeds the entire document first, then derives chunk embeddings while preserving full document context. Is a popular technique for technical documents where chunks reference other parts of the document. 𝗧𝗵𝗲 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 is that your chunks need to be small enough for precise vector search while giving the LLM enough context to generate useful answers, while also not being tooo much context that you overload the content window. 𝗤𝘂𝗶𝗰𝗸 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸: • Prototyping → Fixed-size • Structured docs → Document-based • Dense academic content → Semantic • High-stakes systems → LLM-based or Agentic I would always recommend starting simple and evolving. Learn more in this blog: https://lnkd.in/eYY8c-hN | 17 comments on LinkedIn

AIAS Translations Thanks to our community of educators, we are able to share translations of the AIAS in a range of languages. Buttons link to previews, downloads, or editable originals. If you don…

Walmart CEO Doug McMillon is the latest notable business leader to talk about how implementing AI tools and agents in the workplace will affect his company.

🚨 Too many “agent” tools, not enough clarity. I coach SMB teams every week, here’s the cheat sheet I use to pick the right one fast: ✅ Operators, non-technical → Make.com, Flowise ✅ Low-code and self-host → n8n ✅ Build LLM apps + RAG → LangChain + LangGraph, LlamaIndex ✅ Multi-agent teamwork → AutoGen, CrewAI ✅ Quick ship inside ChatGPT → OpenAI Agentic Stack ✅ Enterprise SDK path → Semantic Kernel Save this, share it with your ops lead, and test one small workflow this week. P.S. Which one are you piloting this quarter? Follow Brianna Bentler for practical AI, real SMB wins, and before/after metrics you can copy. Thanks to legendary Greg Coquillo for the amazing graphic! | 119 comments on LinkedIn

Welcome to the Gen AI community Hub! Join a global movement of innovators as we shape the future of work and business.