Remote Learning Technologies

Last week Google announced Learn Your Way - a research experiment to reimagine the most overused, under-loved artifact in education: the textbook. The problem is obvious: textbooks are one-size-fits-all. Written once, updated rarely, inflicted equally. Great for industrial-scale learning, terrible for actual students. Learn Your Way tries to fix that with AI: a student picks their grade level and interests (sports, music, food). The system then “relevels” the text, swaps out generic examples for personalized ones (Newton’s apple becomes a soccer ball), and builds a personalized core. From there, it spins out multiple formats: immersive text with visuals, section-level quizzes, narrated slides, Socratic dialogues, even mind maps. In a controlled trial with 60 high schoolers, it beat the humble PDF reader across the board: comprehension, retention, and preference. AI is going to fundamentally change education. The way I see it, we will move from: ▪️Standardization → Personalization: Education has been built for scale: 1 teacher, 30 students, 1 chalkboard. AI flips that. Materials adapt to pace and interest; assessment becomes continuous, not blunt. ▪️Knowledge Transfer → Cognitive Coaching: When facts are instantly accessible, memorization stops being the scarce skill. The real edge is knowing when AI is wrong, asking sharper questions, and connecting ideas across disciplines. ▪️Classrooms → Learning Ecosystems: Teachers shift from lecturers to facilitators and motivators. AI covers explanations and drills; humans teach judgment, values, and meaning. Peer learning deepens when everyone brings AI-augmented insights. ▪️Exams → Evidence of Thinking: With AI co-pilots, recall-based tests lose power. Evaluation moves to process, projects, and defense - not “what’s the answer?” but “show your reasoning.” ▪️Scarcity → Abundance (with new inequities): AI promises tutoring for anyone with a smartphone. But access to devices, connectivity, and high-quality models could widen divides. A new gap may emerge between students trained to use AI critically and those who consume it passively. Here's the irony: in making information abundant, AI paradoxically revives the oldest form of teaching. Socrates didn’t assign PDFs; he asked questions until you realized you didn’t know what you thought you knew. His role wasn’t to supply answers but to train skepticism. That is the teacher’s role again. Not to out-explain Gemini, but to show when not to trust it. To cultivate judgment, doubt, and the art of better questions. AI hasn’t reinvented education so much as rerouted it back to its roots: the Socratic method - only now Socrates is paired with a chatbot that never sleeps and never hesitates.

Quarterly trainings don’t build mastery, daily reps do. When I started my career, “practice” meant awkwardly presenting in front of a manager or peer. They were scripted, rushed, and rarely stuck. Learning happened in events, not in habits. Fast forward to today, and the landscape looks completely different. Last week, we had the privilege of joining the Google Mastery team in San Francisco to dive deeper into this shift. What stood out to me in those conversations is how much learning has evolved, and how leaders are rethinking it as a continuous, embedded process. Huge shoutout to Myles Riseborough, Shruti Shah, Dr. Janine Lee, MBA, Ed.D., Chase Knowles, and Jennifer Raven-Harris, and the team for championing this movement 🙌 Why now? 1) AI has unlocked scalable practice. No more waiting for facilitators or fixed scripts, adaptive simulations can run anytime, anywhere. The “practice room” is always open. 2) Tech ecosystems are finally integrated. CRMs, enablement tools, and conversation intelligence systems are no longer silos. This creates a rich data fabric to trigger personalized practice and feedback loops. 3) Measurement is automated. Skill growth used to be subjective. Now, AI scorecards quantify behaviors in real time and tie them directly to business outcomes. 4) Culture has caught up. Shorter product cycles and competitive markets mean one-off training isn’t enough. Continuous “everboarding” is becoming the norm. 🔑 Takeaway: Learning is shifting from events to ecosystems. The most effective organizations will blend high-impact moments (like onboarding or certifications) with always-on, learner-led development, embedded right in daily workflows. To leaders: Does your company have a culture of continuous practice, not just when training is scheduled?

Reimagining Bloom’s Taxonomy with AI: The Future of Learning Design For decades, Bloom’s Taxonomy has been the foundation for structuring learning objectives. But with AI tools, we can now unlock each level of Bloom’s hierarchy in more practical, personalized, and scalable ways—transforming how learners absorb, apply, and innovate knowledge. Here’s how AI supports each stage, with outcomes that matter for modern L&D: 🔹 Create – Tools like ChatGPT, Canva AI, Gamma help design projects, assessments, and innovative solutions. 👉 Outcome: Encourages innovation, design-thinking, and co-creation—key drivers for organizational growth in the digital era. 🔹 Evaluate – Tools like Consensus, Eduaide, Claude assist learners in critiquing arguments and peer-reviewing work. 👉 Outcome: Develops judgment, discernment, and evidence-based evaluation skills needed in leadership roles. 🔹 Analyze – Tools like Perplexity, Claude, Elicit help compare perspectives, organize data, and identify patterns. 👉 Outcome: Enhances critical thinking and decision-making, vital for solving ambiguous and complex business problems. 🔹 Apply – Tools like MagicSchool AI, Gemini, Photomath demonstrate step-by-step problem-solving. 👉 Outcome: Learners practice application in simulated environments, boosting confidence to solve workplace challenges. 🔹 Understand – Tools like ChatGPT, Otter.ai, Brisk Teaching simplify complex concepts using analogies and real-world examples. 👉 Outcome: Learners move beyond rote memorization to grasp concepts deeply, enabling transfer to new situations. 🔹Remember – Tools like QuizGPT, Kahoot, Quizizz generate flashcards, quizzes, and recall games. 👉 Outcome: Strengthens foundational knowledge, reduces cognitive load, and ensures faster retrieval of information. AI doesn’t replace Bloom’s Taxonomy; it elevates it into a dynamic ecosystem where learning is continuous, contextual, and customized. For L&D leaders, this means moving from "training delivery" to "learning orchestration." The future is clear: by embedding AI into Bloom’s framework, organizations can build smarter learning journeys that not only measure learning outcomes but also directly impact business performance. How is your organization blending AI with Bloom’s Taxonomy to build future-ready learners? #LearningAndDevelopment #AI #FutureOfWork #InstructionalDesign #BloomTaxonomy #DigitalLearning #WorkplaceLearning

Digital learning isn’t failing because of a lack of content. It’s failing because most tools weren’t built for the realities learners face. In many underserved communities, students may have a phone—but limited data, limited support, and limited motivation. This is where most digital solutions break. But it’s also where the opportunity for truly transformative, scalable impact begins. In my latest article with the Global Partnership for Education , I share how Solve Education! Foundation builds technology for real-world constraints—not tech-rich ideals. This includes: • 𝗲𝗱𝗯𝗼𝘁.𝗮𝗶 — an AI-powered chatbot that works on basic Android phones and uses minimal data • 𝗧𝗵𝗲 𝗚𝗔𝗜𝗡 𝗺𝗲𝘁𝗵𝗼𝗱 — combining Gamification, AI Coaching, Incentives, and peer Networks • 𝗧𝗲𝗮𝗰𝗵𝗲𝗿-𝗰𝗲𝗻𝘁𝗲𝗿𝗲𝗱 𝗱𝗲𝘀𝗶𝗴𝗻 that reduces workload and strengthens classroom practice • 𝗖𝗼𝘂𝗻𝘁𝗿𝘆-𝗹𝗲𝘃𝗲𝗹 𝗶𝗺𝗽𝗮𝗰𝘁 where more than 90% of learners show measurable improvement From Indonesia’s 17M+ learning sessions to Malaysia’s 98% improvement rates, the pattern is clear: 📊 When learning tools are built for context and evidence-based engagement, every dollar invested delivers deeper, more transparent impact. If you’re exploring scalable, cost-efficient models that turn funding into measurable learning outcomes, I’d love to connect. 📩 Read the full GPE article here: https://lnkd.in/gnMXWktM What do you believe is the next frontier for digital learning in emerging markets?

𝐖𝐡𝐞𝐧 𝐄𝐚𝐫𝐥𝐲 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐌𝐞𝐞𝐭𝐬 𝐄𝐝𝐓𝐞𝐜𝐡: 𝐆𝐚𝐢𝐧𝐬 𝐀𝐫𝐞 𝐑𝐞𝐚𝐥, 𝐛𝐮𝐭 𝐒𝐨 𝐀𝐫𝐞 𝐭𝐡𝐞 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐖𝐞 𝐀𝐫𝐞𝐧’𝐭 𝐀𝐬𝐤𝐢𝐧𝐠 A mobile app for early numeracy and language is showing measurable gains among children from low-income communities in Ghaziabad (UP). Usage has grown, teachers observe progress and families are participating. For classrooms struggling with foundational learning, this is significant. Yet a critical reading shows deeper structural questions. 1. 𝐖𝐡𝐲 𝐚𝐫𝐞 𝐄𝐝𝐓𝐞𝐜𝐡 𝐞𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐬 𝐜𝐨𝐧𝐜𝐞𝐧𝐭𝐫𝐚𝐭𝐞𝐝 𝐢𝐧 𝐥𝐨𝐰-𝐢𝐧𝐜𝐨𝐦𝐞 𝐬𝐜𝐡𝐨𝐨𝐥𝐬? Most large-scale pilots in India appear in government schools, not elite private ones. Research (Banerjee et al., 2023; EdTech Hub) shows these interventions often focus on basic skills, while privileged students access inquiry, reasoning, and creative pedagogies. This risks producing two distinct learning trajectories: targeted remediation for the poor, cognitive expansion for the privileged. Higher-order thinking and meta-cognition remain absent from the design. 2. 𝐀𝐜𝐜𝐞𝐬𝐬 𝐠𝐚𝐩𝐬 𝐬𝐡𝐚𝐩𝐞 𝐨𝐮𝐭𝐜𝐨𝐦𝐞𝐬 𝐦𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐭𝐡𝐞 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐢𝐭𝐬𝐞𝐥𝐟 Shared phones, unstable networks, limited data, and low digital literacy among caregivers are not side issues; they structure who benefits. Technology often amplifies existing social conditions (Selwyn et al., 2023). 3. 𝐏𝐞𝐝𝐚𝐠𝐨𝐠𝐲, 𝐧𝐨𝐭 𝐬𝐨𝐟𝐭𝐰𝐚𝐫𝐞, 𝐝𝐫𝐢𝐯𝐞𝐬 𝐢𝐦𝐩𝐚𝐜𝐭 The most effective element here is not the app but the ecosystem around it: teacher-led support, WhatsApp-based engagement, and blended learning practices. Evidence from Reich (2020) and Escueta et al. (2020) shows that digital tools improve learning only when embedded in coherent instructional practice. 4. 𝐄𝐧𝐠𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐦𝐞𝐭𝐫𝐢𝐜𝐬 𝐜𝐚𝐧𝐧𝐨𝐭 𝐬𝐮𝐛𝐬𝐭𝐢𝐭𝐮𝐭𝐞 𝐟𝐨𝐫 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 Minutes spent on an app indicate activity, not conceptual depth. Quizzes may measure recall but reveal little about reasoning, explanation, or confidence as learners. The risk is mistaking performance traces for understanding. 5. 𝐓𝐡𝐞 𝐩𝐫𝐨𝐦𝐢𝐬𝐞 𝐢𝐬 𝐫𝐞𝐚𝐥, 𝐛𝐮𝐭 𝐠𝐮𝐚𝐫𝐝𝐫𝐚𝐢𝐥𝐬 𝐚𝐫𝐞 𝐞𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥 EdTech can support early learning, but it cannot replace investments in teachers, libraries, home environments, or school infrastructure. Equity requires: (i) slow and supervised use (Livingstone & Blum-Ross, 2020) (ii) pedagogical redesign before technological redesign (Reich, 2020) (iii) structural investment in teachers, families, and public systems (iv) ethical frameworks centred on children’s rights and agency #EdTech #AIinEducation #FoundationalLearning #CriticalEdTech #Childhood #DigitalDivides #HigherOrderThinking #LearningFutures #EducationPolicy #PublicSchools

Digital learning is not failing because of the technology. It is failing because of the design around it. A meta-analysis covering 60 studies (Wu, 2023) and more than 13,000 learners found that digital technology can significantly improve deep learning. But the biggest gains did not come from the platform itself. They came from the conditions surrounding the learner. The research found stronger outcomes when learning was: 💪 Structured rather than fragmented 💪 Guided rather than self-abandoned 💪 Collaborative rather than isolated 💪 Blended with human interaction rather than purely online That distinction matters. Because many organisations still confuse access to content with capability development. Uploading modules is not a learning strategy. Neither is buying another platform. Deep learning happens when people are challenged to think, reflect, discuss, apply, and connect ideas to real-world problems. Technology can support that. But it cannot compensate for weak learning architecture. The most important line in the study for me: Fragmented digital learning showed little meaningful impact. That feels highly relevant to modern workplace learning. Too many organisations have built digital libraries. Too few have built learning systems.

Could a new layer of openness help unlock truly adaptive learning? Most learning materials still come in a single flavour: one language, one reading or grade level, one version for all. Open Educational Resources (OER) made a leap forward with free, openly licensed, remixable content. Yet most OER remain ‘fixed’, to be used ‘as is’. 𝐀𝐈 𝐭𝐨𝐨𝐥𝐬 𝐚𝐫𝐞 𝐛𝐞𝐠𝐢𝐧𝐧𝐢𝐧𝐠 𝐭𝐨 𝐬𝐡𝐢𝐟𝐭 𝐭𝐡𝐢𝐬 𝐩𝐚𝐫𝐚𝐝𝐢𝐠𝐦 With AI tools, this is changing. For example, UNICEF’s Accessible Digital Textbooks tool can already convert a single source file into multiple languages and accessible formats for learners with disabilities. Prompts can provide deeper personalisation, and emerging prompt libraries are a good start. But what if we reimagined prompts in the spirit of OER? What if they were openly licensed, shared, remixed and iteratively improved? This leads to a question:  𝐂𝐨𝐮𝐥𝐝 𝐰𝐞 𝐢𝐦𝐚𝐠𝐢𝐧𝐞 𝐬𝐨𝐦𝐞𝐭𝐡𝐢𝐧𝐠 𝐥𝐢𝐤𝐞 𝐎𝐩𝐞𝐧 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐏𝐫𝐨𝐦𝐩𝐭𝐬 (𝐎𝐋𝐏)? Picture prompts not as one-liners, but as modular, openly licensed learning objects that span subject areas, contexts, themes, and pedagogical models. They could: ● Live in a public, version‑controlled repository under open licences, where community feedback and up‑votes both surface the most effective versions and guide ongoing iteration ● Adapt automatically to learner and teacher profiles (such as language, reading level, accessibility needs, preferred themes and other interests) ● Support peer review, localisation, reuse across platforms, and model-agnostic design ● Integrate with national digital learning systems rather than sitting on the side‑lines We’re already seeing glimpses - like Gemini Gems and custom GPTs that package multi-step logic. But combining open licensing, profile-aware design, cross-platform integration, and iterative improvement could unlock more meaningful, accessible and scalable personalisation across contexts. There would be many challenges, of course: digital divides, bias in outputs, language limitations, and - who builds and maintains it? Would love to hear from others - educators, developers, AI practitioners, accessibility advocates, startups, and anyone exploring the intersection of learning and technology: What might help - or hinder - such a system to accelerate personalised learning opportunities across different contexts?

𝗛𝗼𝘄 𝘁𝗼 𝗖𝗿𝗲𝗮𝘁𝗲 𝗮 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺 🌐 Struggling with disconnected learning platforms and resources? I get it—fragmented learning experiences can derail your L&D programs, making them less efficient and effective. When your team has to juggle multiple systems, it hampers their ability to learn and grow seamlessly. Here’s how you can build an integrated learning ecosystem to connect all your platforms, resources, and tools for a smooth, unified learning experience: 📌 Centralize Your Resources: Start by consolidating all learning materials into a single, accessible repository. This can be a Learning Management System (LMS) or a centralized digital library where employees can easily find what they need. 📌 Integrate Platforms: Use APIs and integration tools to link your LMS with other systems like HR software, productivity tools, and communication platforms. This ensures a cohesive experience where data flows seamlessly between platforms. 📌 Standardize Processes: Develop standardized protocols for content creation, curation, and deployment. This includes using consistent formats and templates, which help maintain quality and uniformity across all learning materials. 📌 Personalize Learning Paths: Leverage data analytics to create personalized learning paths for employees. Tailored content keeps learners engaged and ensures they acquire the skills most relevant to their roles. 📌 Foster Collaboration: Encourage peer-to-peer learning and knowledge sharing through forums, social learning platforms, and collaborative projects. This builds a community of continuous learning and support. 📌 Track Progress and Feedback: Implement tools to monitor learning progress and gather feedback. Use this data to continuously improve your L&D programs, ensuring they remain relevant and effective. By developing an integrated learning ecosystem, you’ll transform fragmented experiences into a cohesive journey that enhances learning efficiency and effectiveness. Your team will thank you for making their learning process smoother and more intuitive. What strategies have you used to create a seamless learning ecosystem? Share your insights below! ⬇️ #LearningAndDevelopment #TrainingInnovation #OnlineLearning #EdTech #LMS #EmployeeEngagement

The first 10 years at MTD Training, we built learning events, the last 15 years we’ve been building learning ecosystems without even knowing! Looking back, that shift didn’t happen because I’d read some new L&D trend report or bought into the latest buzzword. Instead, it happened because the traditional training model simply wasn’t delivering the performance change our clients expected and I refused to pretend otherwise. In those early years, we ran brilliant management training and sales development workshops. They were engaging, practical, well-reviewed. But the same pattern kept surfacing: learners loved the training, yet very little actually changed in the day-to-day. I remember thinking, “We can’t keep calling this success.” So instead of doubling down on events, we quietly started redesigning everything around what would genuinely help people perform better. We didn’t call it an ecosystem at the time, no one was using that language, but that’s exactly what we were building. We built learning journeys long before they became fashionable. We involved line managers when most programmes didn’t even mention them. We created performance support tools because we knew people needed help in the flow of work. We added coaching, reinforcement, diagnostics, nudges, application tasks… not because the market asked for them, but because the learners needed them. Clients started saying, “This feels different.” And it was. We weren’t delivering training, we were engineering the conditions for behaviour change. The funny thing? For years I thought we were just being practical. Only later did I realise we were ahead of the curve. What people now label a “learning ecosystem” was simply our way of making sure training wasn’t a one-off event but part of a system that supported real improvement. And that’s still the biggest mindset shift L&D needs today. If you’re only delivering events, you’re fixing symptoms. If you’re building ecosystems, you’re fixing performance. It took me a decade to see that difference and another decade to refine it. But it’s the reason MTD evolved, and the reason our clients see results that training alone could never achieve. Do you believe in creating learning ecosystems? ------------ Follow me at Sean McPheat for more L&D content and and then hit the 🔔 button to stay updated on my future posts. ♻️ Repost to help others in your network.

The World Economic Forum insight report, "Shaping the Future of Learning: Education Readiness for the Age of AI," global education systems are facing a profound structural transformation as artificial intelligence redefines the economic landscape and pedagogical baselines. While AI holds immense promise for scaling one-to-one personalized tutoring and optimizing administrative institutional capacities, unstructured adoption carries severe risks, including student cognitive atrophy, widespread misinformation via algorithmic hallucinations, a breakdown of academic integrity, and the erosion of critical human connection. To navigate this disruption, the report outlines a comprehensive AI readiness framework structured around four core pillars: enabling foundations, institutional capacities, pedagogical practices, and learner experiences. Ultimately, the framework highlights that establishing digital literacy, data-privacy guardrails, and continuous teacher upskilling is absolutely vital to ensure that AI serves as an equitable enhancer of human potential rather than an unchecked mechanism for educational fragmentation. The primary behavioral driver altering the learning ecosystem is the friction between automated algorithmic convenience and the neurocognitive requirement for "productive struggle" in youth development. When students are exposed to unstructured, always-on AI assistants that instantly solve complex prompts, synthesize reading materials, and generate essays, the core cognitive effort necessary to build critical thinking, executive functioning, and long-term working memory is bypassed. This immediate offloading of intellectual labor threatens to trigger a form of cognitive atrophy, where learners lose the ability to navigate ambiguity, synthesize conflicting information, or separate factual data from algorithmic hallucinations. To counter this threat, pedagogical practices must shift away from evaluating static outputs toward structuring active-learning environments that intentionally preserve human-centric, effort-driven problem-solving. A second critical driver transforming educational delivery is the expanding capability chasm between the linear adaptation rates of traditional institutional governance and the exponential advancement of generative AI tools. While educational software providers deploy highly agile, adaptive learning platforms, the front-line teaching workforce frequently lacks the data literacy, algorithmic oversight capabilities, and curriculum flexibility required to safely integrate these systems into everyday classrooms. This mandates a transition toward flexible professional development frameworks that empower educators to transition from passive proctors to active, AI-augmented instructional guides.