Remote Learning Technologies

Common Sense Media recently released a comprehensive risk assessment of AI teacher assistants/lesson planning tools. Their findings reveal that while these tools promise increased productivity and creative support, they're also creating "invisible influencers" that could fundamentally undermine educational quality. Unlike GenAI foundation model chatbots, these tools are specifically designed for instructional planning and classroom use and are rapidly being adopted across districts. Key Concerns from their report: • "Invisible Influencers" in Student Learning: AI-generated content directly shapes what students learn through potentially biased perspectives and historical inaccuracies that teachers may miss; evidence also shows these tools suggest different approaches and responses based on student race/gender • “Outsourced Thinking" Problem: Tools make it dangerously easy to push unreviewed AI instructional content straight to classrooms, while novice teachers lack experience to spot subtle errors and biasses • High-Stakes Outputs: IEP and behavior plan generators create official-looking documents that could impact student educational trajectories even though these plans should be human-generated (and in the case of IEP goals are mandated to be human generated) • Undermining High-Quality Instructional Materials: Without proper integration, these tools fragment learning and can undermine coherent, research-backed curricula Recommendations from the report: • Experienced educator oversight required for all AI-generated educational content • Clear district policies and guidelines for AI teacher assistant implementation • Integration with existing high-quality curricula rather than replacement of established materials • Robust teacher training on identifying bias and evaluating AI outputs • Careful oversight of real-time AI feedback tools that interact directly with students We'd also recommend foundational AI literacy for teachers before they begin using GenAI teacher assistants, so that they are aware of the potential limitations. While AI teacher assistants aren't inherently problematic, they require the same careful implementation and oversight we'd expect for any tool that directly impacts student learning. The potential for enhanced productivity is real, but so are the risks to educational equity and quality. This report underscores the urgent need for GenAI EdTech tool makers to provide evidence of how their tools mitigate these issues along with evidence-based policies and professional development to help educators navigate AI tools responsibly. All of which underline how important AI Literacy is for the 2025-2026 school year. Link in the comments to check out the full report. Also check out our 5 Questions to Ask GenAI EdTech Providers resource in the comments if you are planning to implement any of these tools in your school or district. #AIinEducation #ailiteracy #Education #K12 AI for Education

Did you know you can create engaging educational games for your class using Canva Code? And no, Code here doesn’t mean coding. There’s no programming involved. No tech headaches. Just you, your creativity, and the power of AI. Canva recently rolled out a new feature called Canva Code, which lets you design interactive learning games using simple prompts. That’s right, you describe the game, and Canva builds it for you. But here’s the catch: the output is only as good as your prompt. So the more detailed you are, the better your game. Think of it as AI-powered instructional design. Some of the games you can create include: 1. Flashcard games 2. Puzzle games 3. Memory match 4. Interactive story games 5. Digital board games 6. Spelling bees and more... I created the visual below to give you a quick overview of 10 learning games you can build using Canva Code. Check it out. #EdTech #CanvaCode #GameBasedLearning #AIinEducation #InstructionalDesign #TeachingWithAI #TeachersOfLinkedIn #medkharbach #educatorstechnology

𝐇𝐨𝐰 𝐜𝐚𝐧 𝐰𝐞 𝐝𝐞𝐬𝐢𝐠𝐧 𝐀𝐈 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐨𝐧𝐬 𝐭𝐡𝐚𝐭 𝐩𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐬𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐨𝐯𝐞𝐫 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞? 𝗧𝗵𝗲 𝗰𝗼𝗿𝗲 𝗮𝗿𝗴𝘂𝗺𝗲𝗻𝘁: Large language models (LLMs) are rapidly transforming knowledge work by improving the quality and efficiency of tasks such as writing, coding, and data analysis. However, their growing use in education has exposed a learning-performance paradox: while they can enhance short-term task performance, they may also undermine genuine learning, including cognitive growth, knowledge transfer, and metacognitive development. I'm thrilled to share that I have been working with the dream team Dragan Gasevic, Shazia Sadiq, Lixiang (Jimmie) Yan, Jason M. Lodge, Jason Tangen, Paul Denny, Kristen Eignor DiCerbo, Simon Buckingham Shum, and Ryan Baker to ground a response that tackles this head-on: 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗜 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗼𝗻𝘀 𝘁𝗵𝗮𝘁 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘀𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗼𝘃𝗲𝗿 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲. 📄 Preprint available here: https://lnkd.in/gbts24D2 This paper addresses the question of how artificial intelligence should be designed and used to support learning rather than merely improve immediate outputs. We introduce the concept of AI learning companions, defined as adaptive, pedagogically informed, LLM-powered agents designed for integration into learning environments, built around three foundations: 🧠 𝗣𝗲𝗱𝗮𝗴𝗼𝗴𝗶𝗰𝗮𝗹 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻: How students learn with AI, grounded in deep and interactive learning, guided scaffolding, metacognitive development, and contextual, authentic engagement. 🔄 𝗔𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻: How AI learns about students, through a continuous cycle of capturing learner data, modelling cognitive and affective states, adapting instruction, and evolving over time. 🛡️ 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗱𝗲𝘀𝗶𝗴𝗻 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻: How companions act with integrity, remaining transparent, accountable, inclusive, and secure. The paper features five case studies spanning global K-12 tutoring (𝗞𝗵𝗮𝗻𝗺𝗶𝗴𝗼), AI-assisted co-creation of educational content (𝗥𝗶𝗣𝗣𝗟𝗘), scaffolded programming support (𝗖𝗼𝗱𝗲𝗛𝗲𝗹𝗽), AI-assisted course discussion and formative feedback (𝗝𝗲𝗲𝗽𝘆𝗧𝗔), and institution-wide AI companion design for higher-order learning (𝗥𝗲𝗰𝗮𝘀𝘁 at UTS), each illustrating what a deliberately designed AI learning companion can look like in practice. 𝗧𝗵𝗲 𝗸𝗲𝘆 𝗶𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Designing AI for learning is not a minor prompt-engineering adjustment. It requires building systems that model learners, adapt to their needs over time, and are grounded in pedagogical principles, shifting from optimising task outputs to cultivating learners who are more reflective, more metacognitively aware, and better equipped to learn independently in an AI-rich world. 💬 Have you developed or used AI tools that prioritise learning over performance? We'd love to hear about them in the comments.

Microsoft Education has just released its 2025 Artificial Intelligence in Education report - a global snapshot of how AI is shaping learning, leadership, and future-ready skills across K–12 and higher education. A few key insights resonate with me, with the final one surely essential: * 𝐀𝐈 𝐢𝐬 𝐢𝐧 𝐜𝐥𝐚𝐬𝐬𝐫𝐨𝐨𝐦𝐬, 𝐛𝐮𝐭 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞 𝐢𝐬 𝐥𝐚𝐠𝐠𝐢𝐧𝐠. While 86% of institutions report using generative AI, fewer than half of educators and students feel confident navigating it. AI fluency is quickly becoming as essential as digital literacy once was. * 𝐒𝐭𝐮𝐝𝐞𝐧𝐭𝐬 𝐚𝐫𝐞 𝐭𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐛𝐢𝐠𝐠𝐞𝐫 𝐰𝐢𝐭𝐡 𝐀𝐈. Tools like Copilot empower learners to brainstorm, reflect, and expand ideas - especially when combined with peer collaboration and thoughtful instructional design. * 𝐀𝐈 𝐚𝐦𝐩𝐥𝐢𝐟𝐢𝐞𝐬, 𝐧𝐨𝐭 𝐫𝐞𝐩𝐥𝐚𝐜𝐞𝐬, 𝐭𝐫𝐚𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠. From reading comprehension to writing, AI is most effective as an assistant that supports deep learning rather than a substitute for it. * 𝐄𝐝𝐮𝐜𝐚𝐭𝐨𝐫𝐬 𝐚𝐫𝐞 𝐞𝐦𝐛𝐫𝐚𝐜𝐢𝐧𝐠 𝐀𝐈 𝐭𝐨 𝐫𝐞𝐜𝐥𝐚𝐢𝐦 𝐭𝐢𝐦𝐞. By reducing lesson prep, supporting differentiated instruction, and streamlining administrative tasks, AI helps teachers focus on what matters most: relationships and pedagogy. * 𝐄𝐪𝐮𝐢𝐭𝐲 𝐦𝐮𝐬𝐭 𝐠𝐮𝐢𝐝𝐞 𝐀𝐈 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧, 𝐧𝐨𝐭 𝐡𝐲𝐩𝐞. Early data suggests AI isn’t widening socioeconomic gaps, but inclusive access, scaffolding, and diverse representation in tool development remain critical. * 𝐀𝐈 𝐬𝐤𝐢𝐥𝐥𝐬 𝐚𝐫𝐞 𝐛𝐞𝐜𝐨𝐦𝐢𝐧𝐠 𝐜𝐚𝐫𝐞𝐞𝐫 𝐜𝐮𝐫𝐫𝐞𝐧𝐜𝐲. With 66% of employers unwilling to hire without AI literacy, students must learn to lead with AI, not just use it. * 𝐓𝐞𝐚𝐜𝐡𝐞𝐫𝐬 𝐰𝐚𝐧𝐭 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 - 𝐚𝐧𝐝 𝐭𝐡𝐞𝐲 𝐦𝐞𝐚𝐧 𝐢𝐭. High-quality, contextual, job-embedded professional development is non-negotiable. This can’t be another “figure it out as you go” initiative. * 𝐒𝐭𝐮𝐝𝐞𝐧𝐭 𝐯𝐨𝐢𝐜𝐞 𝐢𝐬 𝐞𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥. Young people want to shape how AI is integrated into their learning. Let’s co-design the future with them, not just for them. This report is a timely reminder: AI can be more than a time-saver. Done right, it can spark creativity, enhance equity, and help us reimagine learning cultures grounded in agency, curiosity, and care. (Full report can be found in comments)

One of the premises of AI for education is the opportunity to create a more engaging and customized learning experience. Today we are introducing a new research experiment, Learn Your Way, which uses generative AI to transform static educational content into a learner-driven engaging experience. For textbook material, it generates multiple representations based on the source material - from mind maps and audio lessons to immersive text with interactive quizzes. Our recent efficacy study shows this approach can lead to improved learning outcomes on both short and long term recall tests. The system is grounded in learning science and powered by our pedagogy-infused family of models, LearnLM, which is now integrated directly into Gemini 2.5 Pro. Try the experience via Google Labs: https://lnkd.in/drGfTZpw Read more about the research on our blog: http://goo.gle/3KqM8i0 And in technical paper: https://lnkd.in/dZuUeKpa

A great method to understand how ML models work: (fully interactive and covers 20+ models/concepts) Interactive ML is a web platform where every algorithm comes with a live visualization you can manipulate directly. It covers: - Regression (linear, polynomial, Lasso, Ridge) - Classification (logistic regression, KNN, SVM, decision trees, naive Bayes, neural networks) - Clustering (K-Means, hierarchical) - Evaluation (confusion matrix, ROC curves, precision-recall, K-Fold CV, bias-variance tradeoff) - Deep learning (backpropagation visualization) There's also an exercises section for testing your understanding after each module. Find the platform in the replies! ____ Share this with your network if you found this insightful ♻️ Find me → Avi Chawla. Every day, I share tutorials and insights on DS, ML, LLMs, and RAGs.

Stop letting outdated methods and inefficient systems limit your training impact. Here's what educators and trainers face: 1. Outdated delivery methods: Traditional-only approaches limit your reach and scalability. Your training systems should amplify, not restrict, your expertise. 2. Limited engagement tools: Basic presentation tools and static content fail to capture modern learners' attention. Today's students expect interactive, multimedia experiences. 3. Administrative overload: Manual scheduling, payment processing, and student management drain valuable time that could be spent on content creation and delivery. Here's how to transform your training business: → Hybrid learning solutions: Implement platforms that blend live sessions with on-demand content, reaching more learners while maintaining personal connection. → Interactive technology: Engage learners through mobile-friendly platforms, gamification, and collaborative tools that make learning stick. → Automated systems: Streamline operations with integrated platforms handling everything from enrollment to certification, letting you focus on what matters - teaching. Stop struggling with basic tools. Stop losing students to more tech-savvy competitors. The takeaway? Modern learners demand modern solutions. Don't let your expertise go unnoticed because of outdated delivery systems or inefficient processes. Ready to scale your training business with solutions that match your expertise? #EdTech #CorporateTraining #OnlineEducation #ProfessionalDevelopment #CoachingBusiness #DigitalTransformation

I’ve been using n8n to connect my Learning Record Store (LRS) with real-world user interactions. Right now, when an xAPI statement (learner interaction data) comes in, it can trigger a robot to dance when it scans for specific data in the LRS. Next, I’m layering in Slack messages that respond to specific learner interaction data. It’s a simple way to demonstrate a bigger idea. When we collect granular xAPI data from learning in the flow of work, we can actually do something with it. For example, a customer service simulation could be delivered directly in Slack as a link or interactive chat. The rep completes the scenario right where they work. Each response, such as how they phrase answers, how quickly they respond, and whether they resolve the issue, sends detailed xAPI data to your LRS. That data does not stop there. It could connect with performance data from real customer calls. If those calls show that a rep struggles with empathy or tone, the system can automatically generate a custom simulation to practice that specific skill. After completing it, the rep receives personalized feedback or follow-up practice in Slack based on what the system detected. This could be done in so many different ways like with GenAI to create adaptive practice or add an agent with memory that connects chat data, call insights, and internal systems to deliver coaching that feels timely and contextual. This moves learning from a single event to a continuous, adaptive experience that fits naturally into how people already work. #xAPI #learningdesign #learningintheflowofwork #LRS #GenAI #n8n #instructionaldesign #learninganddevelopment #futureoflearning

🤖 AI + Learning Differences: Designing a Future with No Boundaries 🌍 💡 A powerful new white paper from the Stanford Accelerator for Learning explores how Artificial Intelligence (#AI) can transform #education for learners with diverse abilities — turning inclusion into innovation. 🔍 Why it matters: ▪️AI can help redesign learning environments to serve every learner, but only if co-created with those who experience learning differences firsthand. ▪️This document offers a roadmap for a more inclusive, human-centered AI future — one that enhances both learning equity and skills for life and work. 💬 Key Themes & Insights: 🧩 Co-design & Collaboration: Inclusive innovation starts with people — learners, parents, educators, and technologists — designing together. Co-design ensures that AI tools reflect real experiences and reduce barriers, not reinforce them. 🎯 Learning for the edges: “Providing students what they need is not an edge — it’s just learning.” AI can help design flexible, personalized learning that values variability and fosters a sense of belonging and agency for all learners. 📘 Special Education & IEPs: AI-powered tools can simplify and personalize Individualized Education Plans (IEPs) — from real-time feedback to adaptive learning supports — freeing teachers to focus on human connection. 🧠 Early Identification & Mediation: AI can assist in early detection of learning differences and support tailored interventions, provided it is transparent, bias-aware, and always guided by human judgment. 💞 Social & Emotional Well-Being: Beyond academics, AI can nurture emotional intelligence, empathy, and positive relationships — essential for lifelong learning and well-being. 🦾 AI as Assistive Technology: From speech recognition to adaptive tutoring, AI can extend independence and agency for learners, redefining what “support” means. 👩🏫 AI in Teacher Development: Teachers need career-long learning to use AI ethically and effectively. AI can also personalize professional learning and reduce administrative burden. 💼 AI and the Workforce: Preparing all learners for an AI-shaped economy demands inclusive pathways to quality work, ensuring no one is left behind in the digital transition. 🌐 Interdependence & Life Satisfaction: The ultimate goal: AI that fosters autonomy, community, and well-being across a lifetime — learning without boundaries. 🧭 Call to Action Developers, educators, researchers, and policymakers must work together to ensure that AI systems are co-designed, equitable, and responsive to human diversity. #AIinEducation #InclusiveInnovation EfVET European Association of Institutes for Vocational Training (EVBB) European Vocational Training Association - EVTA EUproVET EURASHE eucen WorldSkills International OECD Education and Skills International Labour Organization Cedefop European Training Foundation EU Employment and Skills UNESCO-UNEVOC National Centre for Vocational Education Research (NCVER) CoP CoVEs

AI isn’t replacing instructional designers—it’s expanding what we’re capable of. Over the past few months, I’ve been hosting workshops to help real IDs to bring their AI projects to life. And these aren’t surface-level demos.... We dive into real use cases, engineer effective prompts, and sometimes even write JavaScript to power the experience. I just published a new video that showcases four of the most impressive AI-powered projects to come out of these workshops. In it, you'll see: 1. A Storyline roleplay that simulates group facilitation with six unique AI-powered personas 2. A custom-coded web object that teaches people to read bearing rate graphs (built without writing code) 3. A GPT designed to help teachers reframe their experience for ID interviews 4. A de-escalation scenario with an AI character and a coaching assistant that gives personalized feedback After each demo, the creator joins me to break down their process, lessons learned, and advice for others who want to start building with AI—even if they’re not developers. If you're curious about what AI makes possible in learning design, this video will give you a glimpse into the future—and the people already building it. You can check out the video with the link in the comments. And let me know which project stands out to you. 😄 #LinkedInWithDevlin #InstructionalDesign #Learning #LearningAndDevelopment #eLearning