A few hours ago, Google published a white paper laying out their vision for the Future of Learning. Here's the TLDR: The Headline: 👉 Global learning is at a crossroads: learner outcomes have dropped sharply worldwide, and UNESCO projects a shortage of 44 million teachers by 2030. 👉 AI is positioned as *the* tool to save us from an impending education crisis BUT... 👉 The real "secret weapon" for improving education isn't the tech: it's the learning science we build into it. According to Google, the four biggest opportunities offered by AI in education are: 🔥 Learning Science at Scale – Embed evidence-based methods (retrieval practice, spaced repetition, active feedback) directly into everyday tools. 🔥 Making Anything Learnable – Adjust explanations, examples and complexity to meet each learner where they are. 🔥 Universal Access – Break down language, literacy and disability barriers through AI-powered translation and transformation. 🔥 Empowering Educators – Free up teacher time through AI-assisted lesson planning, resource creation and differentiation. Overall, Google's latest white paper signals an evolving ed-tech culture which centres on a more substantive partnership between ed & tech: 👉 Co-Creation: Google commits to investing in evidence-based approaches to learning design and development and to rigorous evaluation, pilot studies and educator-led research to test and demo impact. 👉 Collaborative Development: Google commits to working with schools, NGOs, researchers and learning scientists to co-design tools for learning. You can read the white paper in full using the link in comments. Happy innovating! Phil 👋 | 26 comments on LinkedIn

Ein Impuls für individuelle Lernpfade durch Künstliche Intelligenz im Bereich L&D „Kunden, die diese Bettwäsche kauften, kauften auch …“ Dieses Prinzip kennen Sie sicher von E-Commerce-Plattformen. Ihr Verhalten wird erfasst, verglichen und in Empfehlungen übersetzt. Das Ziel: Sie kaufen bestenfalls mehr als diesen einen Artikel. Übertragen auf Lernplattformen heißt das: Klicks, Quiz-Ergebnisse und Suchanfragen lassen […]

Explore the mandate for chief learning officers to cultivate environments in which work and learning seamlessly converge, driven by technology, data, and a robust culture of continuous development.

I road‑tested Google Gemini's Guided Learning mode - here’s my hot take on how it performs & how it compares to OpenAI's Study Mode: ✔️ What Gemini's Guided Learning Gets Right → Retrieval Practice – Interactive quizzes and flashcards make you generate answers from memory, harnessing the Generation Effect for more durable learning (Slamecka & Graf, 1978; Jacoby, 1978) → Cognitive Load management – Chunks complex topics into digestible steps, preventing the overwhelm that kills learning (Sweller, 1988; Sweller, van Merriënboer & Paas, 1998) → Multimodal Delivery – Draws on a blend of text, diagrams, YouTube videos & interactive practice to deliver learning content, enhancing both engagement and outcomes (Paivio, 1990) → Patient but Provocative Tutoring – Creates psychological safety through non‑judgmental guidance, encouraging the risk‑taking essential for deep learning (Edmondson, 1999) A solid B+ performance — Study Mode’s strength is Socratic questioning, but Guided Learning’s multimodal content ecosystem & more "strict" tutoring style gives it the instructional edge. ❌ Critical Gaps → No Persistent Learner Profiling – Like Study Mode, Guided Learning misses the persistent knowledge & adaptation that defines effective tutoring (Brusilovsky, 2001). Note: as observed by Claire Zau, a Google Classroom integration could layer in persistent learner profiles — something that could change the game & which OpenAI can’t match. → ZPD Blind Spot – Like Study & Learn mode by OpenAI, Guided Learning doesn’t ask questions that help define your learning level or Zone of Proximal Development (ZPD). Whether you’re K12 or advanced, it doesn't calibrate the challenge or scaffolding to your actual developmental stage up front, missing a key step for truly adaptive support (Vygotsky, 1978). → Productive Struggle Deficit – While it pushes back more than Study Mode by OpenAI, Guided Learning still jumps in with help too quickly, robbing learners of the cognitive friction that builds problem‑solving resilience & drives learning (Kapur, 2008, 2014; Bjork & Bjork, 2011) → Shallow Self‑Reflection – Rarely pushes for deep metacognitive thinking (“Why that approach?”), limiting transfer to new contexts (Chi et al., 1989, 1994; VanLehn, Jones & Chi, 1992) → Recognition Bias – While quizzing is strong, it could and should use more open‑ended generation tasks that embed learning more effectively (Slamecka & Graf, 1978; Jacoby, 1978) 📊 The Verdict: Guided Learning by Google Gemini Vs Study Mode by OpenAI While Study Mode remains stronger in Socratic questioning, Guided Learning edges ahead overall thanks to multimodal content, advanced cognitive load management & more provocative tutoring. However, both tools share some fundamental limitations: no learner persistence, limited metacognitive depth & overly-sycophantic tutoring. Have you tried Guided Learning yet? How does it compare with Study Mode for you? Happy experimenting, Phil 👋

„Am besten lässt sich das so beschreiben: eine ständig erreichbare, allwissende Sprechstunde rund um die Uhr“ Heute wurde der Lernmodus in ChatGPT gelauncht. Ich freue mich schon darauf die Funktion genauer auszuprobieren. Ich bin gespannt ob es uns der Vision von #VibeLearning näher bringt. https://lnkd.in/e-2JgZVR Wer hat es schon ausprobiert und erste Erfahrungen gemacht? OpenAI / ChatGPT for Education

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

Excited to share our latest commentary just published in Nature Reviews Psychology! ✨ ""    Generative AI tools such as ChatGPT are reshaping education, promising improvements in learner performance and reduced cognitive load. 🤖 🤔But here's the catch: Do these immediate gains translate into deep and lasting learning? Reflecting on recent viral systematic reviews and meta-analyses on #ChatGPT and #Learning, we argue that educators and researchers need to clearly differentiate short-term performance benefits from genuine, durable learning outcomes. 💡 📌 Key takeaways: ✅ Immediate boosts with generative AI tools don't necessarily equal durable learning ✅ While generative AI can ease cognitive load, excessive reliance might negatively impact critical thinking, metacognition, and learner autonomy ✅ Long-term, meaningful skill development demands going beyond immediate performance metrics 🔖 Recommendations for future research and practice: 1️⃣ Shift toward assessing retention, transfer, and deep cognitive processing 2️⃣ Promote active learner engagement, critical evaluation, and metacognitive reflection 3️⃣ Implement longitudinal studies exploring the relationship between generative AI assistance and prior learner knowledge Special thanks 🙏 to my amazing collaborators and mentors, Samuel Greiff, Jason M. Lodge, and Dragan Gasevic, for their invaluable contributions, guidance, and encouragement. A big shout-out to Dr. Teresa Schubert for her insightful comments and wonderful support throughout the editorial process! 🌟 👉 Full article here: https://lnkd.in/g3YDQUrH 👉 Full-text Access (view-only version): https://rdcu.be/erwIt #GenerativeAI #ChatGPT #AIinEducation #LearningScience #Metacognition #Cognition #EdTech #EducationalResearch #BJETspecialIssue #NatureReviewsPsychology #FutureOfEducation #OpenScience

It's different from what we've had before: 𝐔𝐩𝐬𝐤𝐢𝐥𝐥𝐢𝐧𝐠 ("teach me") - commonly done through hands-on learning with feedback and reflection, such as scenario simulations, in-person role-plays, facilitated discussions, building and problem-solving. None of that has become less relevant, but AI has enabled scale through AI-enabled role-plays, coaching, and other avenues for personalised feedback. 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐬𝐮𝐩𝐩𝐨𝐫𝐭 ("help me") - support in the flow of work, previously often in the format of short how-to resources located in convenient places. AI has elevated that in at least two ways: through knowledge management, which helps retrieve the necessary, contextualised information in the workflow; and general & specialised copilots that enhance the speed and, arguably, the expertise of the employee. Yet, 𝐞𝐧𝐚𝐛𝐥𝐞𝐦𝐞𝐧𝐭 (‘do it for me’) is different – it takes the task off your plate entirely. We’ve seen hints of it with automations, but the text and analysis capabilities of genAI mean that increasingly 'skilled' tasks are now up for grabs. Case in point: where written communication was once a skill to be learned, email and report writing are now increasingly being handed off to AI. No skill required (for better or worse) – AI does it for you. But here's a plot twist: a lot of that enablement happens outside of L&D tech. It may happen in sales or design software, or even your general-purpose enterprise AI. All of which points to a bigger shift: roles, tasks, and ways of working are changing – and L&D must tune into how work is being reimagined to adapt alongside it. Nodes #GenAI #Learning #Talent #FutureOfWork #AIAdoption | 13 comments on LinkedIn

1. Set clear learning goals  ↳ Knowing what you want to learn makes it easier. ↳ Claude helps you define your path. 2. Provide context for your knowledge  ↳ Understanding the bigger picture is key.  ↳ Claude connects new ideas to what you already know. 3. Request detailed explanations  ↳ Sometimes, you need more than a quick answer.  ↳ Claude can dive deep into complex topics. 4. Get real-world examples  ↳ Learning is better with practical applications.  ↳ Claude shows how concepts work in the real world. 5. Practice writing and receive feedback  ↳ Writing helps solidify your knowledge.  ↳ Claude gives instant feedback to improve your skills. 6. Role-play for languages or coding  ↳ Learning by doing is effective.  ↳ Claude can simulate conversations or coding scenarios. 7. Fact-check surprising claims  ↳ Misinformation is everywhere.  ↳ Claude helps you verify facts and claims. 8. Take breaks and reflect on learning  ↳ Reflection is vital for understanding.  ↳ Claude reminds you to pause and think. 9. Keep a learning journal  ↳ Tracking your progress is important.  ↳ Claude can help you log your journey. 10. Iterate and refine understanding  ↳ Learning is a process.  ↳ Claude encourages you to improve your knowledge.  | 246 comments on LinkedIn

When I say a ‘traditional course’, I mean learning content (instructor-led or self-service) delivered online or face-to-face either, going from beginning to end with little variation in content or delivery. These courses have been the mainstay of training at work since I started in the classroom in the 1980s. For some, the idea that the traditional course is doomed come as a shock. For others, it’s self-evident. Listening to researchers, experts and practitioners like Dani Johnson, Dr Philippa Hardman and Gregg Collins, I am convinced that within 5 years most organisational training will abandon these traditional courses. Why? It is now easy to personalise content, even in a simple fashion, with AI. With extra effort, you can deliver content via adaptive delivery that understands where you are succeeding and failing and changes what you learn, and how you learn it, to ensure you reach competency faster. It’s more effective, more enjoyable, and faster. All of this is already happening, and it's only going to get easier and more wide-spread. But the real drive will come not from the technology, but from the learners. The technology enables the change. The learners will demand it. Once enough people have experienced content delivered with this flexibility – probably initially in their private lives, as consumers – they will start to ask why their employers aren’t delivering content the same way. So I have three questions for you this Friday: · Do you agree that the traditional course doomed? · If so, is the timescale of 2-5 years reasonable? · What are the implications of all this? I’d love to hear your thoughts. | 77 comments on LinkedIn

This week I detail the whole story: how the $360 billion corporate learning market is being blown up by AI.This tidal wave has now arrived, with every major ...

Key findings: - STEM students, particularly in Computer Science, are early adopters of AI tools like Claude, accounting for 36.8% of conversations despite representing only 5.4% of U.S. bachelor's degrees. - Students interact with AI in four primary ways: Direct Problem Solving, Direct Output Creation, Collaborative Problem Solving, and Collaborative Output Creation, each occurring at similar rates. - Claude is mainly used for creating and improving educational content (39.3%), technical explanations (33.5%), and higher-order cognitive functions like Creating (39.8%) and Analyzing (30.2%). Students are not just seeking quick answers; they're using AI as a collaborative tool to enhance their learning journey. This trend highlights the transformative potential of AI in higher education. And it shows: students are smarter than many teachers think or fear. I also liked this graphic which however is also nice marketing showing Claude being used for higher order thinking via creation something new… which is the strength of LLMs obviously. #AIinEducation #HigherEducation #STEM #Innovation #FutureofLearning

This week I discuss this massive shift toward “AI-Native” applications and systems which are radically different from traditional HR Tech, with a particular ...

If broadly true, this is massive. "Our findings reveal that AI enhances general human capital (cognitive abilities and education) by facilitating adaptability… | 44 comments on LinkedIn

Regt zum nachdenken an, auch wenn der Inhalt manchmal Stirnrunzeln verursacht. 😅

Generative AI is transforming all areas of corporate training. Here are the four huge use-cases to learn about.

As digital and AI trends continue to disrupt work, 85% of business leaders agreed that the need for skills development will dramatically increase, according to Gartner, Inc. Thi

AI tutors for every child? OpenAI's Altman predicts personalized learning, job market shifts and the dawn of the Intelligence Age.

Explore the potential downsides of gen AI in education. Drawing on over 40 years of academic research, this article examines AI’s potential to harm learning due to lack of empathy, bypassing essential knowledge accumulation, and digital distractions.

A recent dissertation by Pat Pataranutaporn explores AI's potential to enhance human flourishing through Wonder, Wisdom and Wellbeing, outlining Human-AI…

clean up their catalogue, which is full of legacy… | 18 comments on LinkedIn

Here's the TLDR: ➔ When a group of high school students used OpenAI's GPT-4…

Die rasante Entwicklung von generativer KI revolutioniert das Corporate Learning und eröffnet Learning Professionals völlig neue Möglichkeiten bei der...

Ihr wollt verstehen, warum eine neue Art von Workforce für die KI-Ära unerlässlich ist?In dieser Keynote geht es nicht nur um die technische Details, wir fok...

What is Plan B? Not Plan A!

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We’re rolling out NotebookLM, an experimental offering from Google Labs to summarize information, complex ideas and brainstorm new connections.

Meine Ansprüche sind gewachsen, ich möchte unabhängig vom Ort schnell und effizient lernen, moderne Technologien helfen mir dabei die Inhalte passend für meinen Context (Ort, Zeit, Lernstil, Lernsituation) in vielen Formaten (pptx, docx, miro, anki-cards, video, blogs) aufzubereiten. Wie das funktioniert zeige ich hier in dem Blogbeitrag. Am Ende des

We have now written three papers on how to use AI in teaching, each containing prompts, along with details and instructions. Our most recent is on directly… | 48 comments on LinkedIn

Domain-specific, educational AI-powered models enable greater personalization—and 100% proficiency—but ChatGPT can greatly reduce the cost of making such models.