Open New Learning Lab Resources

Groß Lindow, Juli 2025 - Der Bildungs-Stakeholder "Betriebsrat" nimmt eine zentrale Rolle ein, wenn es um Compliance im Unternehmen geht. Der Betriebsrat verfügt nämlich über ein gesetzlich verankertes Mitbestimmungsrecht bei allen Maßnahmen, die compliance-relevante Aspekte betreffen – etwa Regelungen zum Datenschutz oder zur Leistungs- und Verhaltenskontrolle. Und da mittlerweile nahezu jedes IT-System, jedes Tool oder jede Anwendung – insbesondere solche mit KI-Funktionalitäten – personenbezogene Daten erhebt und verwendet, ist es nachvollziehbar, dass der Betriebsrat in diesen Zeiten stark gefordert ist. » MEHR

If I could wave a magic wand over Learning & Development, I’d remove one thing. One thing that would immediately improve everything from our impact to our efficiency. That one thing would be: Everybody’s expectations of what L&D is supposed to do. And I mean everybody… Senior leaders Line managers The workforce HR And yes, even us in L&D Because the biggest barrier to impactful Learning & Development isn’t budget, bandwidth or buy-in… It’s the baggage. We’re carrying around decades of assumptions about what L&D should look like that have very little to do with actually improving performance or closing skills gaps. If we could hit reset and define our role from scratch we’d operate very differently. We’d prioritise: - Support over solutions - Performance over participation - Outcomes over optics But we don’t, because everyone thinks they know what L&D should be. And that’s what’s holding us back. So here’s the real challenge: Can we slowly but surely rewrite the narrative starting with how we talk about both: what we do and the value we bring? Thoughts? | 30 comments on LinkedIn

Druckfrisch aus dem Weissen Haus: Der AI Action Plan der USA. "WINNING THE RACE" ist die Ansage. Ich bin mal sehr gespannt auf die Europäische Antwort. Mein GPT sagt dazu ganz wertfrei: "Wird 2025 zum Jahr der globalen AI-Doktrin? Mit dem 28-seitigen „America’s AI Action Plan“ legt die Trump-Administration ein kompromisslos ambitioniertes Strategiepapier vor – ein geopolitisches Manifest für technologische Vorherrschaft, das Innovation, Infrastruktur und Diplomatie radikal neu denkt. Ziel: globale AI-Dominanz. Kein „Könnte“, kein „Sollte“. Sondern ein „Wird“ – mit einer Regierung, die AI als Schlüssel zur wirtschaftlichen, militärischen und kulturellen Zukunft Amerikas versteht. Das Dokument ruft eine neue industrielle Revolution, eine Informationsrevolution und eine digitale Renaissance gleichzeitig aus. Der Plan umfasst: _ • Deregulierung und Priorisierung von Open-Source-Modellen • Milliarden-Investitionen in Halbleiter, Cloud-Infrastruktur, Energie und AI-Forschung • staatlich geförderte AI-Sandboxes für Healthcare, Bildung, Verteidigung und Industrie • nationale Reallabore, Skills-Offensiven und beschleunigte Adoption im öffentlichen Sektor • Exportoffensive für ein „American AI Stack“ – Hardware, Modelle, Standards • strikte Exportkontrollen und diplomatische Isolierung Chinas in Governance-Gremien • Cyber- und Biosecurity-Maßnahmen gegen Missbrauch von Frontier-Modellen • juristische Anpassung zur Bekämpfung von Deepfakes und synthetischer Evidenz Bemerkenswert ist der offen geopolitische Ton: Die USA verstehen sich wieder als Gestalter einer neuen Weltordnung - mit AI als Hebel. Wer das Rennen macht, schreibt die Regeln. Für Europa stellt sich damit dringender denn je die Frage: Wollen wir nur regulieren - oder auch gestalten?" Quelle: https://lnkd.in/eXwTUGzv

The social signals behind employee retention "Research has long shown that employees at the center of an organizational network—those with many active connections—are 24 percent less likely to leave." 🤔 Michael Arena and Aaron Chasan highlight an important insight: employee connection, not just engagement, is the true bedrock of retention: 👉 “In today’s networked workplace, social withdrawal is often the first—and most reliable—indicator that someone’s already halfway out the door.” For HR to genuinely impact business performance and employee experience, we must leverage social signals to build robust internal networks. Michael and Aaron outline four high-impact ways HR can proactively employee connection and significantly reduce attrition: 🔎 Utilise network analysis: Identify early flight risks by spotting employees with few or declining connections. 🔎 Facilitate connection moments: Deliberately create opportunities for interaction, especially in hybrid settings, using tools like interest-based matching. 🔎 Support relationship-rich teams: Encourage cross-functional initiatives and invest in psychologically safe team cultures. 🔎 Routinely pulse central employees: Their engagement profoundly influences the entire network. "In today’s networked workplace, social withdrawal is often the first—and most reliable—indicator that someone’s already halfway out the door." 👉 This report is featured in the June edition of the Data Driven HR Monthly, which you can access here: https://lnkd.in/exEqY-Hn 👈 #humanresources #organizationalnetworkanalysis #peopleanalytics #leadership #culture #socialcapital | 18 comments on LinkedIn

I was recently looking at how Gen AI shapes and potentially impacts cognitive skills - a topic that matters for education and for work. Here are a few resources I reviewed. 1️⃣ Your Brain on ChatGPT - What Really Happens When Students Use AI MIT released a study on AI and learning. Findings indicate that students who used ChatGPT for essays showed weaker brain activity, couldn't remember what they'd written, and got worse at thinking over time https://shorturl.at/qaLie 2️⃣ Cognitive Debt when using AI - Your brain on Chat GPT There is a cognitive cost of using an LLM vs Search Engine vs our brain in e.g. writing an essay. The study indicates that there is a likely decrease in learning skills, the more we use technology as substantial replacement of our cognitive skills. https://lnkd.in/drVa_YNg 3️⃣ Teachers warn AI is impacting students' critical thinking One of many articles about the importance of using Gen AI smartly, in Education but also at work. https://lnkd.in/dSbGjusu 4️⃣ The Impact of Gen AI on critical thinking Another interesting study on the same topic. https://shorturl.at/74OO6 5️⃣ Doctored photographs create false memories In psychology, research indicated a long time ago that our memory - our recollection of past events - is susceptible to errors, biases, can be fragmentary, contain incorrect details, and, oftentimes, be entirely fictional. Memories are a reconstruction of our past to respond to our need for coherence in life. A rigorous 2023 study shows that doctored photographs – think Photoshop or today, AI – create false memories. Why it matters? Memory is essential for learning, recall of episodical and factual happenings, and it’s a basis for the integrity of sources of truth in organizations.   https://shorturl.at/hdgtN 6️⃣ The decline of our thinking skills Another great article on AI and critical thinking from IE University. https://shorturl.at/rGl99 7️⃣ Context Engineering Ethan Mollick recently wrote a blog on "context engineering" - how we give AI the data and information it needs to generate relevant output. The comments on the post were even more interesting than the post itself. Personally I think that good part of context engineering is not in organizations documents or processes, it is in peoples ability to think critically and understand relevant parameters of their environment to nurture AI/Gen AI. Gotta follow up on this one ;-) https://shorturl.at/sfnuV #GenAI #CriticalThinking #AICognition #AIHuman #ContextEngineering | 29 comments on LinkedIn

Upskilling in the AI era is fundamental for every company regardless of size and is often viewed as a necessary business investment. Additionally, many companies connect workforce upskilling to business functions (operations, manufacturing, legal, etc.

I have commented in my newsletter that what people have been describing as 'ethical AI principles' actually represents a specific political agenda, and not an ethical agenda at all. In this post, I'll outline some ethical principles and work my way through them to make my point.

EY just broke the biggest lie in corporate learning. They trained 44,000 employees internally. Then launched an AI Academy using 200 real-world use cases. The result? 50+ actual AI projects launched across five enterprises. Plus leadership-driven AI manifestos. That's the story I shared recently. It got more reactions and shares than anything I've posted. The support: ↳ Those who liked and shared it ↳ "We're already doing this approach" ↳ "This is exactly what we want to implement" But what I liked the most is how the experts responded. The pushback: ↳ "50 projects means nothing without context" ↳ "What about the projects that failed?" ↳ "Where's your control group?" ↳ "This sounds like survivor bias" ↳ "Did you measure actual skill transfer?" ↳ "Will this work for topics other than AI?" ↳ "Correlation isn't causation" Fair points. All of them. But here's what I'm doubling down on: We're measuring the wrong damn things. L&D isn't in the learning business. We're in the building business. L&D's focus should be simple: ↳ What solutions can our people build? Our measurement strategy should be reimagined around that core ability. Not: ↳ Did they complete the course? ↳ Did they pass the quiz? ↳ Did they like the content? While we debate attribution methodology: ↳ 97% of enterprises still cite talent gaps ↳ $366B spent annually on corporate training ↳ Minimal business impact to show for it The uncomfortable truth? Perfect measurement of learning consumption ≠ Performance change EY's numbers might be messy. ↳ Their attribution might be flawed. ↳ Their methodology might be incomplete. But they're asking the right question: "What did people actually build?" The experts want rigor. I want it too. But let's get rigorous about what matters: ↳ Solutions created ↳ Problems solved ↳ Value delivered I'm building a coalition of L&D leaders ready to abandon traditional metrics. If you're already measuring "what people build" - I want to hear your story. If you're ready to start - let's connect and figure this out together. Who's in? [Check out my original post and the expert responses - link in comments] #WorkplaceLearning #LearningAndDevelopment #PerformanceSupport #ReimagineLND 🔁 Resonates? Share it—let's reimagine L&D together! ➕ Follow me, Santhosh Kumar, for unconventional insights that challenge how we lead and learn.

📊 Welchen Reifegrad hat eure L&D-Arbeit? Wo steht ihr und wie könnt ihr euch gezielt weiterentwickeln? 💡In diesem Video stelle ich euch das L&D-Reifegradmo...

📊 Welchen Reifegrad hat eure L&D-Arbeit? Wo steht ihr und wie könnt ihr euch gezielt weiterentwickeln? 💡In diesem Video stelle ich euch das L&D-Reifegradmo...

L&Ds aren't very happy with their LMS platforms, that's for sure 🥲 We recently launched our first tools report, and below 👇🏻 you can find 7 insights around LMSs & LXPs. Want to read more? － Download the free report 👉 https://lnkd.in/dBZzW6TZ － Join the Offbeat Fellowship to explore all our insights 👉 https://lnkd.in/dx3REqBh Hope you'll find this useful! 💜 #learninganddevelopment #learningmanagementsystem #learningexperienceplatform #learningtools

We’re at a turning point. AI can generate faster, broader, cheaper content than ever. So the real differentiator isn’t knowledge, it’s experience design that actually influences behaviour and builds people's capability. And that’s tough. Because designing for experience means starting with the challenges people face, not topics or content. It means accepting that 'learning' doesn’t always feel good, in fact, it’s often the sting of experience that actually drives change. It’s layered too. Like an onion. The micro layer: our senses and emotions The meso layer: the interactions and activities we’re part of The macro layer: the strategic shifts in thinking and behaviour Some of the best experiences are completely invisible. Others stop us in our tracks and change us forever. But they’re rarely a one-off event. The most powerful ones are embedded in how we work, not added on after the fact. So let’s stop trying to make learning cute or entertaining (ie the Disneyfication effect). Let’s stop pretending every experience needs to feel good. Be honest. Build what’s real. And what actually makes a difference. Because if AI owns the content, then we’ve got to own the context. This means suba diving (see previous post..it will make sense, i promise), not snorkelling. If you’re interested in learning design that genuinely supports performance and growth, feel free to get in touch. As many know, I love talking about it.

Berufswahl im Zeitalter der lernenden Maschinen – Offener Brief an meine Nichte (Abi-Jahrgang 2025) Liebe Anna, als du mich fragtest, ob „Informatik, Medienwissenschaft oder Politik“ noch zukunftssicher sind, merkte ich, wie löchrig die alte Landkarte der Arbeit geworden ist. Code wird von KI vervollständigt, Diagnosen von Algorithmen unterstützt, Routineverträge von Bots geprüft. Laut Weltwirtschaftsforum wird bis 2030 fast jede zweite Kompetenz umgeschrieben. Was also studieren? Meine Empfehlung: Drei Felder, die weniger vom Titel als vom Skill-Mix leben. Warum? Weil sie Eigenschaften bieten, die KI kaum kopieren kann: direkten Menschenkontakt, interdisziplinäres Denken und sinnliche Materialerfahrung. Sie bilden zusammen einen „Human Moat“ – einen Schutzwall gegen reine Automatisierung. 1 | HEALTH & HUMAN SERVICES – BERUFE MIT EMPATHIE-FAKTOR Das ist erwartbar: Pflege, Sozialarbeit, Therapie oder Pädagogik bleiben knapp, weil Demografie und Krisen Resilienz verlangen. Typische Rollen: Pflegefachfrau+, Physician Assistant, Tele-Coach Mental Health. Schlüssel-Skills: evidenzbasierte Pflege, interkulturelle Kommunikation, Basiswissen Medizinrecht und Datenschutz. 2 | TWIN-TRANSITION CAREERS – KLIMA × TECHNOLOGIE Smarte Mash-Ups: Unternehmen brauchen Talente, die CO₂-Reduktion mit Datenkompetenz verbinden. Typische Rollen: Nachhaltigkeits-Data-Analyst, Circular-Economy-Ingenieurin, KI-Policy-Analyst, Energy-Systems-Modeler. Schlüssel-Skills: Life-Cycle-Assessment, Python/R, EU-Regulatorik (CSRD, AI Act), Systemdenken. 3 | CRAFT & EXPERIENCE DESIGN – WERT DES EINZIGARTIGEN Je perfekter Massenware KI-optimiert ist, desto höher steigt der Wert des Nicht-Skalierbaren. Typische Rollen: Produktdesigner*in für Bio-Materialien, Restaurator, Schreinerin mit CNC-Know-how, UX-Designer für phygitale Erlebnisse. Schlüssel-Skills: Materialkunde, CAD/CAM & 3-D-Druck, Storytelling, Customer-Journey-Mapping. Das ist natürlich nur ein Ausschnitt. Aber ich denke, die Muster dahinter sind klar, um es selbst weiterzudenken. WAS VERSCHWINDET? Alles, was rein repetitiv ist: Standard-Reporting, einfache Software-Tests, seitenlange Vertragsprüfungen. Die Maschine erledigt es schneller und billiger – doch jemand muss die Systeme entwerfen, mit Daten füttern und ethisch beaufsichtigen. MEIN RAT IN DREI SÄTZEN >> Suche kein Joblabel, sondern ein Problem, das dich elektrisiert. << Kombiniere digitale Grundfitness, empathische Kommunikation und moralischen Kompass. Dann arbeitest du nicht gegen Maschinen, sondern mit ihnen – und kannst dir jederzeit einen neuen Beruf erfinden. Vielleicht startest du als Pflege-Informatikerin, wirst später KI-Ethikerin und eröffnest irgendwann eine Bäckerei, in der Roboter den Teig kneten, während du den Sauerteig fütterst und Kund:innen berätst. Zukunftssicherheit entsteht nicht aus einem Studium, sondern aus lebenslanger Lernlust. Die Welt bleibt turbulent, doch wer Richtung Sinn steuert, hat immer Rückenwind. Dein Onkel Stefan | 59 Kommentare auf LinkedIn

👉 Prompt-Sammlung: https://simon-eckert.com/prompt-sammlung/👉 Bindungsstil-Selbsttest: https://simon-eckert.com/bindung-selbsttest/-- Notfallkontakte --Tel...

My site: https://natebjones.com/My links: https://linktr.ee/natebjonesMy substack: https://natesnewsletter.substack.com/Takeaways 1. Software 3.0 Paradigm Sh...

An eagerness to learn is essential for innovation. But the way we learn—and the order in which we partake in various learning activities—can make the difference between effective growth and potential missed opportunities. Jean-François Harvey, Johnathan Cromwell, Kevin J. Johnson, and I studied more than 160 innovation teams and found that the key to faster, clearer progress is: Structured learning 👷🏗️ Our research, published in the Administrative Science Quarterly Journal, highlights four distinct types of learning behaviors used by high-performing teams and examines variations in the sequence and blend of these types of team learning. Without a deliberate rhythm, teams risk becoming overwhelmed by continual information intake, leading to confusion and burnout. But by honing a team's ideal 'learning rhythm,' you can avoid overwhelm and instead focus on strategic decision-making and sustainable innovation. Read our research summary now in the Harvard Business Review: https://lnkd.in/e5nU-Kka | 90 comments on LinkedIn

The Learning Journey That Led to Nowhere The carefully curated “learning journey.” Polished decks. Inspiring speakers. Branded workbooks. The kickoff, the modules, the reflection points, the wrap-up. The capstone . It all looked beautiful. But no one changed. No one led differently. No one made a better decision, helped somebody else, solved a harder problem, or grew in any measurable way. They left as they came—only now with a certificate and a champagne toast. And we called it success. Why? Because the survey said they liked it. Because someone said it “landed well.” Because it fit the budget, the time box, and the LMS tracked completion. But deep down, we know better. We know that most learning programs don’t stick. They don’t demand enough. They don’t disturb the old habits. They don’t connect to the real pressures people actually face at work and in life. We’ve made learning comfortable when it’s supposed to be disruptive and difficult. We’ve made it a journey—when it should’ve been an expedition. As practitioners, we carry some of the blame. We built what would be approved, not what was required. We chased polish over power. And we told ourselves that “awareness” was enough. Because if they leave the same way they arrived—was it a journey at all. Or was it a scenic loop.? | 65 comments on LinkedIn

Technology often outpaces the ability of organizations to embrace it. We look at how businesses can overcome the growing challenge of gen AI adoption.

How AI Works 101. Brief morning rant, in case anyone is persistently bamboozled by AI discussions. #AI #learning #education

Your best coach can't be everywhere at once. 𝘉𝘶𝘵 𝘵𝘩𝘦𝘪𝘳 𝘈𝘐 𝘵𝘸𝘪𝘯 𝘤𝘢𝘯. Scaling world-class coaching is one of the biggest headaches in L&D. You bring in a top-tier expert for a workshop, and the C-suite loves it; then what? The knowledge fades, and the cost to retain them for 1-on-1 coaching across the org is astronomical. Well, the ability to have experts available 24/7 is now a reality. Google is quietly testing a potential solution in its Labs. 𝗜𝘁'𝘀 𝗰𝗮𝗹𝗹𝗲𝗱 𝗣𝗼𝗿𝘁𝗿𝗮𝗶𝘁𝘀. It’s more than a chatbot. It’s a library of voice-enabled, AI-powered avatars of real-world experts, trained only on their unique ideas and content. What that means: → Minimal AI hallucinations → No generic advice → Just the expert's authentic perspective, on-demand Check out this screenshot of Google Portraits. That’s an AI version of storytelling expert Matt Dicks. He’s coaching me to find the "heart of a story" in a seemingly dull, everyday moment — cutting grass. It's a very immersive experience as he walks me through finding the "story" in my experience. Think about the possibilities: → Democratize coaching: Assign a storytelling coach or a feedback sparring partner to every new manager. → Practice in private: Let employees rehearse difficult conversations in a safe and controlled environment before the real thing. → Scalable IP: A new model for licensing and deploying the knowledge of the world's best minds across your entire company. This is the future of personalized, scalable learning. It’s moving from static courses to dynamic, conversational experiences. The big question for us in L&D: Is this the scalable future we've been waiting for, or are we losing the essential human element of coaching? | 12 comments on LinkedIn

𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗠𝗖𝗣 𝗶𝘀 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗼𝘀𝗲 𝗿𝗮𝗿𝗲 “𝗼𝗵 𝗱𝗮𝗺𝗻, 𝘁𝗵𝗶𝘀 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴” 𝗺𝗼𝗺𝗲𝗻𝘁𝘀! I’ve been in tech for years, and MCP (Model Context Protocol) is one of those rare innovations that deserves every bit of the hype. I really can’t believe how much smoother everything gets. 𝗜𝗳 𝗜 𝗵𝗮𝗱 𝘁𝗼 𝗯𝗲𝘁 𝗼𝗻 𝗼𝗻𝗲 𝗽𝗿𝗼𝘁𝗼𝗰𝗼𝗹 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗶𝗻 𝗔𝗜, 𝗶𝘁’𝘀 𝗠𝗖𝗣. MCP sounds complex — but it’s really not. Think of it as a guide that helps your AI agents understand: → what tools exist → how to talk to them → and when to use them 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝟵 𝗳𝘂𝗹𝗹𝘆 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗲𝗱 𝗠𝗖𝗣 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗲𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱 𝘄𝗶𝘁𝗵 𝘃𝗶𝘀𝘂𝗮𝗹𝘀 & 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 𝗰𝗼𝗱𝗲 (𝘁𝗼 𝗴𝗲𝘁 𝘆𝗼𝘂 𝘀𝘁𝗮𝗿𝘁𝗲𝗱): ⬇️ 1. 100% Local MCP Client → Build a local MCP client using SQLite + Ollama — no cloud, no tracking. → Full docu: https://lnkd.in/gtaEGvFZ 2. MCP-powered Agentic RAG → Add fallback logic, vector search, and agents in one clean flow. → Full docu: https://lnkd.in/gsV62MDE 3. MCP-powered Financial Analyst → Fetch stock data, extract insights, generate summaries. → Full docu: https://lnkd.in/g2\_EaJ\_d 4. MCP-powered Voice Agent → Speech-to-text, database queries, and spoken responses — all local. → Full docu: https://lnkd.in/gweH8Rxi 5. Unified MCP Server (with MindsDB) → Query 200+ data sources via natural language using MindsDB + Cursor. → Full docu:https://lnkd.in/gCevVqKK 6. Shared Memory for Claude + Cursor → Build cross-app memory for dev workflows — share context seamlessly. → Full docu: https://lnkd.in/giDXdtXd 7. RAG Over Complex Docs → Tackle PDFs, tables, charts, messy layouts with structured RAG. → Full docu: https://lnkd.in/gMHqHvBR 8. Synthetic Data Generator (SDV) → Generate synthetic tabular data locally via MCP + SDV. → Full docu:https://lnkd.in/ghyUyByS 9. Multi-Agent Deep Researcher → Rebuild ChatGPT’s research mode, fully local with writing agents. → Full docu: https://lnkd.in/gp3EsrZ2 Kudos to Daily Dose of Data Science! 𝗜 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲𝘀𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁𝘀 — 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗺𝗲𝗮𝗻 𝗳𝗼𝗿 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 — 𝗶𝗻 𝗺𝘆 𝘄𝗲𝗲𝗸𝗹𝘆 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. 𝗬𝗼𝘂 𝗰𝗮𝗻 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝗵𝗲𝗿𝗲 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲: https://lnkd.in/dbf74Y9E | 49 comments on LinkedIn

AI can help, or hurt, our thinking

𝙃𝙖𝙗𝙩 𝙞𝙝𝙧 𝙨𝙘𝙝𝙤𝙣 𝙫𝙤𝙣 𝘼𝙄 𝙇𝙚𝙖𝙥 2025 𝙜𝙚𝙝ö𝙧𝙩? AI Leap ist eine landesweite KI-Bildungsinitiative aus #Estland, die 20.000 Schüler:innen der 10. und 11. Klasse sowie 3.000 Lehrkräften einen kostenlosen Zugang zu KI-basierten Lernwerkzeugen und entsprechender Schulung gewährt. Bereits letztes Jahr war ich von der politischen Haltung und konsequenten Umsetzung Estlands fasziniert, als ich u.a. mit der Botschafterin der Republik Estland, Marika Linntam, auf dem Panel der IHK Berlin über die Arbeitswelt der Zukunft diskutieren durfte. AI Leap ist Estlands Antwort auf die vielseitigen Herausforderungen im Bildungsbereich und fördert frühzeitig notwendige Schlüsselkompetenzen, die für den Arbeitsmarkt der Zukunft unerlässlich sind. Estland hat erkannt, dass ein professioneller Umgang mit KI-Technologien der wichtigste Wettbewerbsfaktor der Zukunft sein wird. Das war auch eine meiner insgesamt 4 Thesen, die ich vorab in einer Keynote vorstellen durfte, den kompletten Vortrag findet ihr hier: https://lnkd.in/dTdXMGuA 🅰🅱🅴🆁: 🎯 WO STEHEN WIR IN DEUTSCHLAND❓ 🎯 Wie können wir trotz Bildungsförderalismus schnell wirksam werden❓ Spannende Fragen für unsere neue Regierung v.a. mit Blick auf das Bundesministerium für Digitales und Staatsmodernisierung unter Leitung von Dr. Karsten Wildberger, das die #Digitalisierung und die #KI #KünstlicheIntelligenz in Deutschland auf ein nächstes Level heben will. Was mir gefällt ist die Aufbruchstimmung und ein #WirMachen. Ich hoffe, dass es gelingt, etwas zu bewegen und die entsprechenden Stakeholder einzubinden. Ich bin gerne dabei, denn da gibt es noch VIEL ZU TUN. Estland macht es vor! Es ist zwar viel kleiner als Deutschland, dennoch können wir viel von Estland (und anderen Ländern) lernen v.a. wenn wir in globale Kooperationen und in Public-Private-Partnership Modelle investieren. Quelle: https://lnkd.in/eUzXiSza #FutureOfWork #FutureSkills #SmartLearning :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: 🔔 Du möchtest mehr über die Arbeitswelt im Wandel zu erfahren? Let's connect! 💌 Du interessierst Dich für eine Zusammenarbeit? Schreib mir gerne!

When I think about the future of learning with AI, I don’t imagine it as more content and courses. A rewiring of what we do and how we do it is happening right now. While most teams are stuck at the point of innovations from 2 years back, you can be ahead of this. Yet...I still see a lot of talk and not so much action, sprinkled with a lot of misinformation and actual understanding of Gen AI's power and limitations. That creates a problem if the L&D industry wishes to thrive in the new world of work with AI. That’s not to say I have “all the answers”, coz I don’t What I do have is a barrel load of real-world experiences working with teams on making AI adoptions a success. In tmrw's Steal These Thoughts! newsletter I'm going to share some of that with 5 insights that'll challenge everything you think you know about AI in L&D. Like the sound of that? → Join us by clicking 'subscribe to my newsletter' on this post and my profile. #education #learninganddevelopment #artificialintelligence

This is the feature I've been waiting for OpenAI to release. It's not "game-changing", but it's incredibly useful. Uses can now select the model you want to use with a custom GPT. Which is perfect for those using my performance consulting coach GPT. Switch the model to o3 and use it as it was intended in my original design. Here's a little how-to video with my GPT in action. Find my GPT: https://lnkd.in/e2pdCKt8 #education #artificialintelligence #learninganddevelopment

For a long time I felt that if we wanted to answer questions such as ‘how do we design learning experiences?’, ‘how do we measure learning?’, ‘what is our pedagogy based on?’ - or even just explain to stakeholders what it is that we do - then it would help to have an understanding of learning. Thanks again to Ben Gallacher and the #Inrehearsal team for creating this series. #learning #pedagogy #learningdesign #education #training

We might think we have an AI adoption story, but the reality is different: we still have a huge AI understanding gap! Here are some key stats from the report that honestly made me do a double-take: ▪️99% of education leaders, 87% of educators worldwide & 93% of US students have already used generative-AI for school at least once or twice! ▪️Yet only 44% of those educators worldwide & 41% of those US students say they “know a lot about AI.” ‼️this means our usage is far outpacing our understanding & that’s a significant gap! When such powerful tools are used without real fluency, we would see: ▪️complicated implementation with no shared strategy (sounds familiar?)! ▪️anxious students who’d fear being accused of cheating (I've heard this from so many students!) ▪️overwhelmed teachers who feel alone, unsupported & unprepared (this one is a common concern by some of my teacher friends)! The takeaway that jumped out at me: ▪️the schools that win won't be the ones that adopt AI the fastest, but the ones that adopt it the wisest! So here's what I’d think we should consider: ✅building a "learning-first" culture across institutions & understanding when AI supports our learning vs. when it gets in the way! ▪️more like, we need to swap the question "Are we using AI?" for "Can we show any learning gains?" ⚠️so, what shifts does this report data point us to? Here is my takeaway: ✅Building real AI fluency: ▪️moving beyond simple "prompting hacks" to true literacy that includes understanding ethics, biases & pedagogical purposes, ▪️this may need an AI Council of faculty, IT, learners & others working together to develop institution-wide policies on when AI helps or harms our learning, ▪️it's about building shared wisdom, not just industry-ready skills ✅Creating collaborative infrastructure: ▪️the "every teacher for themselves" approach seems to be failing, ▪️shared guidelines, inclusive AI Councils & a culture of open conversation are now needed to bridge this huge gap! ✅Shifting focus from "using AI tools" to "achieving learning outcomes": ▪️this one really resonated with me because unlike other tech rollouts we've witnessed, AI directly affects how our students think & learn, ▪️our institutions need coordinated assessments tracking whether AI use makes our learners better thinkers or just faster task completers! The goal that keeps coming back to us ▪️isn't to get every student using AI! ▪️but to make sure every learner & teacher really understands it! ⁉️I’m curious, where is your institution on this journey? 1️⃣ individual use: everyone is figuring it out on their own (been there!) 2️⃣ shared guidelines: we have policies, but they're not yet deeply integrated (getting closer!) 3️⃣ fully integrated strategy: we have a unified approach with a learning-first, outcome-tracked focus (this is the goal!) | 24 comments on LinkedIn