The full report is being presented from 2–4 September at UNESCO’s Digital Learning Week 2025 in Paris. It’s a must-read for anyone interested in learning, technology, and the future of education — packed with insights and practical perspectives. 𝗧𝗵𝗲 𝗿𝗲𝗽𝗼𝗿𝘁 𝗰𝗼𝘃𝗲𝗿𝘀 𝘁𝗵𝗲 𝗳𝗼𝗹𝗹𝗼𝘄𝗶𝗻𝗴 𝗮𝗿𝗲𝗮𝘀: ⬇️  𝟭. 𝗔𝗜 𝗳𝘂𝘁𝘂𝗿𝗲𝘀 𝗶𝗻 𝗲𝗱𝘂𝗰𝗮𝘁𝗶𝗼𝗻: 𝗣𝗵𝗶𝗹𝗼𝘀𝗼𝗽𝗵𝗶𝗰𝗮𝗹 𝗽𝗿𝗼𝘃𝗼𝗰𝗮𝘁𝗶𝗼𝗻𝘀 → AI futures aren’t just about intelligence scores — they push us to rethink what “knowing” really means. And the whole debate isn’t only technical but philosophical: how do we define learning, progress, and human identity in an AI age? 𝟮. 𝗗𝗲𝗯𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗽𝗼𝘄𝗲𝗿𝘀 𝗮𝗻𝗱 𝗽𝗲𝗿𝗶𝗹𝘀 𝗼𝗳 𝗔𝗜 → AI in schools and universities is not inevitable — education systems have choices, agency, and the power to shape direction. The core tension here: opportunity for reinvention vs. risks of over-automation and cultural bias. 𝟯. 𝗔𝗜 𝗽𝗲𝗱𝗮𝗴𝗼𝗴𝗶𝗲𝘀, 𝗮𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝗲𝗺𝗲𝗿𝗴𝗶𝗻𝗴 𝗲𝗱𝘂𝗰𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗳𝘂𝘁𝘂𝗿𝗲𝘀 → Classrooms can’t be reduced to data points — AI must respect the incomputable nature of learning. And hyper-personalization risks turning education into an isolated bubble rather than a social dialogue. 𝟰. 𝗥𝗲𝘃𝗮𝗹𝘂𝗶𝗻𝗴 𝗮𝗻𝗱 𝗿𝗲𝗰𝗲𝗻𝘁𝗲𝗿𝗶𝗻𝗴 𝗵𝘂𝗺𝗮𝗻 𝘁𝗲𝗮𝗰𝗵𝗲𝗿𝘀 → Teachers remain the backbone of education — AI should amplify their work, not sideline it. Building AI “with” educators, not “for” them, is the only path to trust and adoption. 𝟱. 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗮𝗻𝗱 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝗺𝗽𝗲𝗿𝗮𝘁𝗶𝘃𝗲𝘀 𝗳𝗼𝗿 𝗔𝗜 𝗳𝘂𝘁𝘂𝗿𝗲𝘀 𝗶𝗻 𝗲𝗱𝘂𝗰𝗮𝘁𝗶𝗼𝗻 → AI in schools demands an ethics of care — transparent, fair, and accountable by design. Governance can’t be outsourced to tech — it requires democratic oversight and public participation. 𝟲. 𝗖𝗼𝗻𝗳𝗿𝗼𝗻𝘁𝗶𝗻𝗴 𝗰𝗼𝗱𝗲𝗱 𝗶𝗻𝗲𝗾𝘂𝗮𝗹𝗶𝘁𝗶𝗲𝘀 𝗶𝗻 𝗲𝗱𝘂𝗰𝗮𝘁𝗶𝗼𝗻 → AI can close divides — but only if it is localized, contextualized, and designed for inclusion. Without clarity, bias will persist: marginalized groups risk being left behind. 𝟳. 𝗥𝗲𝗶𝗺𝗮𝗴𝗶𝗻𝗶𝗻𝗴 𝗔𝗜 𝗶𝗻 𝗲𝗱𝘂𝗰𝗮𝘁𝗶𝗼𝗻 𝗽𝗼𝗹𝗶𝗰𝘆: 𝗘𝘃𝗶𝗱𝗲𝗻𝗰𝗲 𝗮𝗻𝗱 𝗴𝗲𝗼𝗽𝗼𝗹𝗶𝘁𝗶𝗰𝗮𝗹 𝗿𝗲𝗮𝗹𝗶𝘁𝗶𝗲𝘀 → Policy must keep pace with fast-moving AI capabilities, balancing human and machine intelligence. AI will shape every industry — but in education, it will shape society itself. Download: https://lnkd.in/dbc6ZJi4 Enjoy reading! And please share your views: ⬇️ 𝗣.𝗦. 𝗜𝗳 𝘆𝗼𝘂 𝗹𝗶𝗸𝗲 𝘁𝗵𝗶𝘀, 𝘆𝗼𝘂’𝗹𝗹 𝗹𝗼𝘃𝗲 𝗺𝘆 𝗻𝗲𝘄 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. 𝗜𝘁’𝘀 𝗳𝗿𝗲𝗲 𝗮𝗻𝗱 𝗿𝗲𝗮𝗱 𝗯𝘆 𝟮𝟬,𝟬𝟬𝟬+ 𝗽𝗲𝗼𝗽𝗹𝗲: https://lnkd.in/dbf74Y9E | 39 comments on LinkedIn

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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

I've been researching and writing about what's happening with AI in L&D for the past 18 months.

We have in ChatGPT o1 a brilliant teaching tool, because it shows its thinking.  Yes I know, It is not 'thinking' as we do". But it is might...

AI will teach this class a lesson but won't be hanging in the teacher's lounge

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An update to our map of the current state-of-the-art

What is Plan B? Not Plan A!

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In this video I show I was able to install an open source Large Language Model (LLM) called h2oGPT on my local computer for 100% private, 100% local chat wit...

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

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In this podcast I explain how Large Language Models and neural networks are reinventing the HR Technology market. After more than 30 in-depth interviews with HR Tech vendors, we see three generations of AI solutions: - Emerging: AI features added on - First Generation: AI capabilities built-in - Second Generation: Platforms built on AI As I discuss in this week's podcast, vendors in category 3 are the most transformational of all. These vendors are building their entire product set around Large Language Models and expansive neural networks. While they were once considered new, as you'll learn in this podcast I believe these "built on AI" platforms are the future of the market. Stay tuned for our new whitepaper on the HR Tech vendor market and how AI is changing the entire marketplace. The Josh Bersin Company https://joshbersin.com The Josh Bersin Academy https://bersinacademy.com Additional Information: [How AI Is Disrupting The HR Tech Marketplace](https://joshbersin.com/2023/04/how-ai-is-disrupting-the-hr-tech-marketplace/) [SeekOut Brings GPT4 To Recruiters. Eightfold Launches Copilots For HR.](https://joshbersin.com/2023/04/seekout-brings-gpt4-to-recruiters-eightfold-launches-copilots-for-hr/) [What Is A Neural Network?](https://www.youtube.com/watch?v=aircAruvnKk&t=4s) (Great overview video)

Ein Diskussionspapier von KI-Campus skizziert Zukunfstszenarien für den Einsatz von KI in der Bildung und Gelingensbedingungen bzw. Handlungsfelder für die Umsetzung.