Technology In Education

The Department for Education has published its "Technology in Schools Survey 2024–25", and the findings offer a clear picture of how digital capability is evolving across the sector Here are some of the insights that stood out: 🔹 Digital strategies are becoming the norm 70% of secondary schools now have a digital strategy in place, and teacher engagement in digital planning has increased significantly since 2023. But only around a third of schools have any framework for evaluating the impact of their technology use, a reminder that strategy alone isn’t enough without measurement. 🔹 AI adoption is accelerating 44% of teachers report using Generative AI tools, mainly for lesson planning and admin. Younger teachers are leading the way, while leaders report growing challenges around pupil use, from plagiarism to misinformation. Most schools expect to expand AI training over the next two years. 🔹 Infrastructure is improving at pace Schools are rapidly upgrading: Wi-Fi 6, full-fibre broadband, improved cybersecurity practices and greater use of cloud-based storage. Secondary settings continue to demonstrate higher “digital maturity” overall. 🔹 Technology is increasingly linked to workload reduction and attainment 61% of leaders and 43% of teachers say technology has reduced workload over the last three years, with the biggest time-savers being planning, data management, resource sharing and communication. Two-thirds of leaders believe technology has improved pupil outcomes. 🔹 But classroom device use remains limited Despite better access to hardware, most teachers (around 75%) still use end-user devices in fewer than a quarter of lessons. The shift towards more digital-first pedagogy is happening, but slowly. 🔹 Financial barriers remain the biggest challenge Budget constraints continue to top the list of obstacles. Staff confidence and access to CPD are also recurring themes, particularly as technology evolves faster than training can keep up. The report reinforces a message many of us know well: technology alone doesn’t improve learning, strategy, capability, culture and investment do. AI literacy, cloud-first infrastructure, accessibility tools, and digitally confident staff are becoming essential components of a modern, resilient, future-ready institution.

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

I believe that AI is evolving education into something more powerful. It’s reshaping the entire learning experience. Teachers are becoming personalized learning architects - designing AI-driven curricula that adapt to each student's pace and strengths. Administrators are evolving into data-driven leaders who use AI insights to predict student needs and optimize resources. Curriculum specialists are shifting toward adaptive content design while AI handles routine delivery. But the bigger story? The entirely new roles that will emerge in education: - AI Curriculum Architects design learning pathways that adapt in real-time - Learning Analytics Specialists analyze patterns across thousands of students to identify what actually works - Digital Instruction Coaches help teachers integrate AI without losing the human connection - AI Ethics Coordinators ensure algorithms don't disadvantage any student groups In this newsletter, I break down the roles emerging in education, identify the skills that matter most, and share how education leaders can position their teams for this shift. Education is entering its most exciting chapter yet - one where learning becomes deeply personal, data-driven, and accessible at scale.  What AI roles in Education are you most excited about? #Education #AI #FutureOfWork

Today’s Irish Times reports that secondary school teachers in Ireland are seeking indemnity against legal risks posed by AI-generated coursework in Leaving Cert exams. The concern is that teachers, who must authenticate student work as original, could inadvertently certify AI-assisted submissions. This could lead to penalties for students and professional liability for teachers. The debate highlights the tension between outdated educational frameworks and the rapid evolution of artificial intelligence. This controversy, in my view, is only a symptom of a much deeper and more urgent problem. The widening AI literacy divide is the real crisis in education. The key issue is not whether students are using AI, but who is learning to use it effectively. The divide between those who master AI tools and those who do not will shape educational and career outcomes far more than previous technological shifts. Access to AI tools is increasingly stratified. Many of the most powerful AI systems require monthly subscriptions. This creates an economic barrier, but the more significant divide is not financial. There are excellent free AI systems available, yet their effectiveness depends on literacy. A child who knows how to use AI, who understands how to prompt, refine, and critically assess outputs, will have an enormous advantage over a child who does not. Parents who actively teach their children how to use AI are setting them up for success in ways that go far beyond any single school assignment. This gap is far more significant than disparities in high-speed internet access or even access to personal computers that we saw 25 years ago (as a “geriatric millennial” it’s close to my heart). When the internet became widely available, there were those who embraced it and learned how to navigate the vast world of online information. Others saw it as a problem, something to be constrained and sometimes even banned. The result was a generation of digital natives who thrived and a generation that struggled to adapt. AI will produce an even starker divide, but this time, the consequences will be more profound. I have seen firsthand how transformative AI can be in education. My children use AI constantly. Through voice features they created HTML code for a video game they could play on my phone, refining their understanding of coding through interactive experimentation. When reading books about sharks, they use chatgpt’s voice and video features to explore the subject in greater depth, asking follow-up questions and engaging with the material in a way that traditional textbooks simply cannot facilitate. They are not just consuming information. They are actively shaping their learning experience. This is what we should be teaching all children. The world has changed, and there is no going back. Pandora’s box is open. The choice we face is not whether AI will be used in education but whether we will prepare students to use it intelligently and effectively.

📘 New evidence from the European Commission’s JRC on Generative AI in secondary education – and why VET should pay close attention https://lnkd.in/eAG6E5T3 💡This JRC study explores how early adopters across five EU countries are already using Generative AI (GenAI) in schools. While focused on general secondary education, its insights are highly relevant for VET, skills systems and workforce development, where the boundary between learning and work is even thinner. 🔍 GenAI is moving fast from “disruption risk” to “pedagogical tool” – but policy, skills and institutions are struggling to keep up. 🧠 Key themes and takeaways (with a VET lens): 🤖 AI literacy is now a core skill ▪️GenAI reshapes what “digital competence” means for learners and teachers. ▪️AI literacy goes beyond tools: it includes critical thinking, ethics, bias awareness and human agency. ▪️For VET, this is directly linked to employability, adaptability and lifelong learning. 👩🏫 Teaching practices are already changing ▪️Early adopters use GenAI to personalise learning, simplify complex concepts and generate feedback. ▪️Teachers save time, but only if they understand how the system works. ▪️In VET, this mirrors the need to support trainers in dual systems, work-based learning and skills validation. 📝 Assessment is under pressure ▪️Traditional assessment models are challenged by GenAI’s capacity to generate content ▪️Shift needed towards competence-based, authentic and performance-oriented assessment ▪️This aligns perfectly with long-standing VET principles – but requires system-level support 👥 Students are ahead of institutions ▪️Learners use GenAI extensively as a “personal assistant” ▪️They value efficiency, but still want human feedback, presence and trust. ▪️Risk: widening digital divides if access and guidance are unequal – a major concern for inclusive VET ⚖️ Ethics, integrity and governance lag behind ▪️Concerns focus on plagiarism, bias and data protection ▪️Broader issues (environmental impact, digital sovereignty, labour behind AI) are largely absent ▪️VET has a responsibility to reconnect AI use with values, citizenship and decent work 🏗️ Policies and support are not ready (yet) ▪️Teachers and leaders report insufficient guidance, training and infrastructure ▪️The AI Act, DigComp 3.0 and the upcoming AI Literacy Framework are crucial – but implementation will be key ▪️For VET, this calls for systemic approaches, not isolated pilots 🧩 Looking ahead: ▪️Excellence in skills systems will increasingly depend on how well we combine: technological capability, pedagogical innovation, inclusion, and human judgement ▪️Centres of Vocational Excellence, strong teacher/trainer development, and coherent skills governance are more relevant than ever #ArtificialIntelligence #DigitalEducation Romina Cachia, PhD Daniel Villar-Onrubia, DPhil Christian Rietz Hannele Niemi Dr. Michael Hallissy Robert Reuter EU Employment and Skills

Education technology is easy to build in theory. The real challenge is making it work in the hands of a student whose internet drops mid-lesson, or a working mum who is logging into university for the first time on a shared device. The test is not in creating EdTech tools but in making them work for the people who need them most. When we started uLesson in 2019, we built a platform with high-quality video lessons, quizzes, and practice tests. Everything worked perfectly in our offices in Jos and then, Abuja. But that changed when we tried to get them into the hands of students in towns and villages where electricity was unreliable, data was expensive, and smartphones were often shared among siblings. The same lessons appeared when we launched Miva Open University, an affordable, accessible university that delivers quality education with the same rigour as a physical campus. Creating the platform was one challenge; helping working adults adapt to digital learning for the first time was another. Some of our students had never studied without the structure of a physical classroom. Many were logging in from places where network connectivity was patchy at best. These challenges sit against a larger backdrop: According to Quartz, only 1 in 4 students applying to university will get accepted. Not because they didn’t study hard enough, instead, in many cases, it is because there simply isn’t enough room for all of them. From these experiences, I’ve learnt that successful EdTech implementation requires: - Designing for context: Tools must work offline or in low-bandwidth environments. - Investing in people: Teachers, facilitators, and students need training, support, and trust to use technology effectively. - Patience in adoption: Communities don’t adopt new systems overnight. Value has to be proven, and trust earned, over time. I remain convinced that EdTech will play a central role in the future of African learning. But for it to truly work, it must be built not just for ambition, but for reality. It has to be built for students walking kilometres to school, for families sharing a single device, and for communities learning to trust digital tools for the first time. We’re still learning. We’ll keep improving. And with each iteration, we get closer to delivering not just access, but quality learning wherever a student lives.

𝐄𝐝𝐓𝐞𝐜𝐡’𝐬 𝐁𝐫𝐨𝐤𝐞𝐧 𝐏𝐫𝐨𝐦𝐢𝐬𝐞: 𝐖𝐡𝐚𝐭 𝐭𝐡𝐞 𝐄𝐯𝐢𝐝𝐞𝐧𝐜𝐞 𝐍𝐨𝐰 𝐒𝐚𝐲𝐬 A recent piece in The Economist offers a sobering reckoning with five decades of classroom technology. The story of McPherson Middle School in Kansas, which recently rolled back laptop-centric learning after disappointing results, mirrors what rigorous research has been warning for years. Despite bold claims of “personalization” and “adaptive learning,” large-scale evidence remains thin. A 2024 meta-analysis of 119 studies on early-literacy technologies led by researchers at Stanford University found, at best, marginal test score gains. Many interventions showed no effect or even negative outcomes. Neuroscientist reviews covering tens of thousands of studies reach a blunt verdict: 𝘤𝘭𝘢𝘴𝘴𝘳𝘰𝘰𝘮 𝘵𝘦𝘤𝘩𝘯𝘰𝘭𝘰𝘨𝘺 𝘳𝘢𝘳𝘦𝘭𝘺 𝘤𝘳𝘰𝘴𝘴𝘦𝘴 𝘵𝘩𝘦 𝘵𝘩𝘳𝘦𝘴𝘩𝘰𝘭𝘥 𝘰𝘧 𝘮𝘦𝘢𝘯𝘪𝘯𝘨𝘧𝘶𝘭 𝘭𝘦𝘢𝘳𝘯𝘪𝘯𝘨 𝘪𝘮𝘱𝘢𝘤𝘵. And yet spending continues to surge. American schools now spend around $30 billion annually on edtech within a $165 billion global industry. Adoption has been driven less by evidence than by marketing, free pilots, and the administrative appeal of dashboards and automation. Teachers often report not liberation, but added surveillance, compliance work, and fragmented attention. The most troubling signal is longitudinal. National reading and subject scores in the US rose steadily until around 2012–15, precisely when in-class screen use accelerated. Since then, performance has declined. Cross-national data show a consistent pattern: heavier classroom computer use correlates with lower achievement, while classrooms with minimal or no device use tend to perform best. Why? Distraction is only the surface problem. Many platforms privilege gamification over concept mastery, short feedback loops over sustained thinking, and screen mediation over human interaction. Digital drills can help in narrow domains like spelling, arithmetic, or specific learning disabilities. But transfer beyond the app environment remains weak. Researchers increasingly argue for age-sensitive restraint. For younger children, peer and teacher interaction matters more than any interface. For older students, technology works only when its use is limited, intentional, and clearly subordinate to pedagogy. More than a decade ago, Bill Gates suggested it would take ten years to know whether edtech really works. Hundreds of billions later, the answer is clearer than the marketing suggests. Perhaps the most unsettling question raised by the article is this: 𝐰𝐡𝐚𝐭 𝐦𝐢𝐠𝐡𝐭 𝐡𝐚𝐯𝐞 𝐡𝐚𝐩𝐩𝐞𝐧𝐞𝐝 𝐢𝐟 𝐞𝐯𝐞𝐧 𝐚 𝐟𝐫𝐚𝐜𝐭𝐢𝐨𝐧 𝐨𝐟 𝐭𝐡𝐢𝐬 𝐢𝐧𝐯𝐞𝐬𝐭𝐦𝐞𝐧𝐭 𝐡𝐚𝐝 𝐠𝐨𝐧𝐞 𝐢𝐧𝐭𝐨 𝐭𝐞𝐚𝐜𝐡𝐞𝐫𝐬, 𝐜𝐥𝐚𝐬𝐬𝐫𝐨𝐨𝐦𝐬, 𝐥𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬, 𝐚𝐧𝐝 𝐭𝐢𝐦𝐞 𝐟𝐨𝐫 𝐭𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐢𝐧𝐬𝐭𝐞𝐚𝐝? #EdTech #EducationResearch #EvidenceBasedPolicy #LearningSciences #TeachingAndLearning #ClassroomPractice #DigitalEducation

India’s edtech space may be opening up a fresh opportunity for a new generation of players to innovate, as incumbents either die out or get consolidated. This time, I hope we do it right and avoid repeating past mistakes - including our own, and carry forward some hard-earned learnings: → There are only so many truly good teachers. → Some students simply don’t want to learn - don’t build for them or acquire them. → Many adults want outcomes without effort; adult learning/skilling is ngmi. → Certification is a bullshit business; even if people buy it, it doesn’t improve actual skills - otherwise we’d have seen the impact by now. → Not everyone wants to be an engineer/doctor/CA/lawyer. → A growing number of students genuinely don’t know what they want to do. → Star teachers are a boutique business - stop trying to scale them. → AI might be the best teacher we’ve been waiting for. → Personalized one-on-one human support will stay relevant for a long time. → Colleges that bring students together IRL will remain necessary - we just need a new generation of them. → High achievers need separate schools; their capability is getting stifled in normal ones. → We’ll need new vocational colleges for new-age professions - built by a new wave of education entrepreneurs. → Entrepreneurship-focused high schools may be the best way to nurture students who want to build. → We need a research-focused college for talent that doesn’t fit the current system - we’re losing far too much potential. → Teaching “Bharat adults” real, practical stuff (not random linkbait) is a massive opportunity enabled by AI-led content and personalization. → AI companions can finally solve the “motivation” layer in education (we tried and failed at this), and that can truly move the needle. → A “build and learn” model may be the most effective way to learn - we’re trying to enable some of this with AI Grants India. There are many more lessons from the incredible education entrepreneurs who built over the past decade in India. I hope these aren’t lost - and instead fuel the next generation.

Medical schools are preparing students for a world that may no longer exist. This hit home when a medical student asked me: “How should we explain it to patients when we are utilising AI tools as part of their care?” I realized - no one taught them this. No one taught any of us. And AI isn't some future concept. It's already here: 🔸 AI scribes transforming doctor-patient interactions 🔸 Imaging AI detecting conditions faster than radiologists 🔸 Clinical decision support tools analyzing complex patient data 🔸 AI-powered diagnostics in pathology and dermatology 🔸 Language models helping doctors stay current with research Yet here's what's missing in medical education: 🔸 Understanding AI isn't optional anymore – it should be fundamental 🔸 We're teaching future doctors to interpret lab results but not AI outputs 🔸 Students learn medical ethics but not AI ethics in healthcare 🔸 They master clinical reasoning but not prompt engineering The gap is widening: ➡️ AI tools are evolving daily ➡️ Medical curricula update every few years ➡️ Students graduate into a world we didn't prepare them for This isn't about creating AI experts. It's about preparing competent doctors for tomorrow's medicine. The modern doctor needs to know: 1. When to trust AI and when to trust their instincts 2. How to combine AI efficiency with human empathy 3. Ways to communicate AI use that build patient trust 4. Methods to maintain clinical judgment while leveraging AI tools The time to act is now. Medical educators, administrators, and practitioners: we must bridge this gap together. If you're involved in medical education: 🔹 Push for AI literacy in your curriculum 🔹 Partner with tech companies to bring real-world AI experience to students If you're a practicing physician: 🔹Don't wait for formal training - start learning about AI tools in your specialty 🔹Share your AI experiences, both successes and challenges 🔹Mentor students and junior doctors in practical and safe AI applications If you're a medical student: 🔹Take initiative to learn about AI in medicine through available resources 🔹Ask questions about how AI is being integrated into the clinical workflow during your placements The stethoscope revolutionized medicine in 1816. Today's AI revolution is equally transformative. We must ensure that medical education evolves to meet this moment, and prepares doctors not just for using AI, but for being better doctors because of it. If you are in this space - How is your institution preparing doctors for an AI-enabled future? What challenges are you facing? Would love to hear your thoughts!

500 students share one computer in Niger. Yet they're conducting advanced physics experiments that students at elite schools can't access. The secret? WebAR turning basic smartphones into portable STEM labs. Think about that. In Sub-Saharan Africa, fewer than 10% of schools have internet. Student-to-computer ratios hit 500:1. Yet mobile subscriptions jumped from single digits to 80% in a decade. Students already carry the infrastructure—we just weren't using it right. Traditional EdTech Reality: ↳ VR headsets: $300+ per student ↳ Heavy apps requiring 5G speeds ↳ Labs costing millions to build ↳ Rural schools: permanently excluded The WebAR Revolution: ↳ Runs in any browser, optimized for 3G ↳ No app store, minimal storage ↳ Science scores improving 10-15% ↳ Every smartphone becomes a laboratory But here's what grabbed me: A physics teacher in rural South Africa has one broken oscilloscope. No budget. Her students scan printed markers, and electromagnetic fields pulse across their desks. They run experiments infinitely—no equipment damaged, no reagents consumed. One student told her: "Engineering is for people like me now. The lab fits in my pocket." What changes everything: ↳ Mobile-first matches actual connectivity ↳ Browser-based works offline ↳ Teachers need training, not new buildings ↳ Inequality becomes irrelevant The Multiplication Effect: 1 teacher with markers = 30 students experimenting 10 schools sharing content = communities transformed 100 districts adopting = educational equality emerging At scale = STEM education without infrastructure gaps We spent decades waiting for labs that won't arrive. Now any browser becomes one. Because when a student in rural Africa explores the same 3D molecules as someone at MIT—using the phone already in their pocket—you realize: WebAR isn't shiny technology. It's a quiet equaliser making world-class STEM education fit into 3G connections and $50 phones. Follow me, Dr. Martha Boeckenfeld for innovations where accessibility drives transformation. ♻️ Share if you believe quality education shouldn't require perfect infrastructure.