Science Education Technology Integration

Just came across a significant policy development out of #Beijing: Mandatory AI education for ALL primary and secondary students starting Sept 1, 2025. This includes children as young as 6, receiving at least 8 hours annually. This is truly a massive move – requiring substantial changes in school infrastructure, teacher training, and curriculum integration across subjects. It clearly underscores #China's strategic push to cultivate AI skills from the ground up and potentially gain a competitive edge in the global AI race. For me, this raises critical questions about the future of #education and the #FutureOfWork. What are the implications of introducing AI concepts at such a foundational age (6+)? How does this potentially impact global #SkillsGap dynamics? And what does it mean for childhood development and the evolving role of educators? I'm genuinely interested in hearing the diverse perspectives from this network on this development. What are your initial reactions – do you see this as brilliant preparation for the future, or does it raise concerns about pace or approach? Let's discuss in the comments. #AIinEducation #EducationPolicy #EdTech #WorkforceDevelopment

📌 The 3 Types of BI Dashboards to Build (And why building the wrong one leads to failure) One of the biggest reasons for low dashboard adoption is relevance. In other words, we try to make one dashboard for everything. We mix KPIs with granular filters. We track long-term strategy and real-time operations in the same view. We hope it works for executives, analysts, and frontline teams all at once. That’s a recipe for confusion and low adoption. Different users = Different needs = Different dashboards The reality is that there are 3 different types of dashboards and each one serves a very specific purpose. Some dashboards are made for monitoring operations → Operational Dashboards Others are built for deep analysis → Analytical Dashboards And some are designed for executive oversight → Executive Dashboards This cheat sheet breaks down the 3 most common types of BI dashboards. 👉 Save this framework. Share it with your team. It might be the clarity you need before your next build. Because you can’t just build a single dashboard and expect adoption across the board. #BusinessIntelligence #DashboardDesign #DataAnalytics

Just out: the new UNESCO report on AI and the future of education with a contribution from me and Dr Jasper Roe SFHEA. Our chapter: “𝑻𝒉𝒆 𝒆𝒏𝒅 𝒐𝒇 𝒂𝒔𝒔𝒆𝒔𝒔𝒎𝒆𝒏𝒕 𝒂𝒔 𝒘𝒆 𝒌𝒏𝒐𝒘 𝒊𝒕: 𝑮𝒆𝒏𝑨𝑰, 𝒊𝒏𝒆𝒒𝒖𝒂𝒍𝒊𝒕𝒚 𝒂𝒏𝒅 𝒕𝒉𝒆 𝒇𝒖𝒕𝒖𝒓𝒆 𝒐𝒇 𝒌𝒏𝒐𝒘𝒊𝒏𝒈” explores our prediction of the split between digitally advantaged and digitally marginalised contexts as GenAI tools continues to erode assessment practices. The first draft of our piece was described as “bleak” by one reviewer, and perhaps rightly so. We argue that traditional forms of assessment are collapsing under the pressure of GenAI and that the tools available today already challenge our ability to verify originality or authenticity. Detection of GenAI usage is unreliable, and even the exam hall is no safe harbour as we enter into a postplagiarism era (Sarah Elaine Eaton, PhD). Looking ahead, the risks are clear: • Digitally advantaged contexts may be able to experiment with new, AI-integrated assessments, moving towards human-centred skills like judgement, ethics, and collaboration. • Digitally marginalized contexts, however, risk being pushed back into outdated, rote-based exams, widening divides in opportunity and recognition. This raises urgent questions of power, equity, and epistemic justice: whose knowledge is validated, whose voices are sidelined, and how we build assessment systems that serve all learners. To prevent this split, we believe the following needs to happen: • Share AI infrastructure and training so all learners benefit. • Back multilingual and open-source AI to reduce language and cultural bias. • Invest in assessment practices that values relational expertise, ethical reasoning, and real-world impact. A big thank you to Shafika Isaacs (PhD) for her leadership on this, and Glen Hertelendy for the amazing organisation and production of the report. There are some huge names featured here, so it really is an honour to be among them. PS. If you are feeling depressed after reading our piece, read the chapter directly following ours 'The ends of tests: Possibilities for transformative assessment and learning with generative AI' by William Cope Mary Kalantzis and Akash Kumar Saini for an alternative perspective on the potential benefits that GenAI may bring to testing and assessment in the GenAI era.  

A nice review article "Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation" covers the scope of tools and approaches for how AI can support science. Some of areas the paper covers: (link in comments) 🔎 Literature search and summarization. Traditional academic search engines rely on keyword-based retrieval, but AI-powered tools such as Elicit and SciSpace enhance search efficiency with semantic analysis, summarization, and citation graph-based recommendations. These tools help researchers sift through vast scientific literature quickly and extract key insights, reducing the time required to identify relevant studies. 💡 Hypothesis generation and idea formation. AI models are being used to analyze scientific literature, extract key themes, and generate novel research hypotheses. Some approaches integrate structured knowledge graphs to ground hypotheses in existing scientific knowledge, reducing the risk of hallucinations. AI-generated hypotheses are evaluated for novelty, relevance, significance, and verifiability, with mixed results depending on domain expertise. 🧪 Scientific experimentation. AI systems are increasingly used to design experiments, execute simulations, and analyze results. Multi-agent frameworks, tree search algorithms, and iterative refinement methods help automate complex workflows. Some AI tools assist in hyperparameter tuning, experiment planning, and even code execution, accelerating the research process. 📊 Data analysis and hypothesis validation. AI-driven tools process vast datasets, identify patterns, and validate hypotheses across disciplines. Benchmarks like SciMON (NLP), TOMATO-Chem (chemistry), and LLM4BioHypoGen (medicine) provide structured datasets for AI-assisted discovery. However, issues like data biases, incomplete records, and privacy concerns remain key challenges. ✍️ Scientific content generation. LLMs help draft papers, generate abstracts, suggest citations, and create scientific figures. Tools like AutomaTikZ convert equations into LaTeX, while AI writing assistants improve clarity. Despite these benefits, risks of AI-generated misinformation, plagiarism, and loss of human creativity raise ethical concerns. 📝 Peer review process. Automated review tools analyze papers, flag inconsistencies, and verify claims. AI-based meta-review generators assist in assessing manuscript quality, potentially reducing bias and improving efficiency. However, AI struggles with nuanced judgment and may reinforce biases in training data. ⚖️ Ethical concerns. AI-assisted scientific workflows pose risks, such as bias in hypothesis generation, lack of transparency in automated experiments, and potential reinforcement of dominant research paradigms while neglecting novel ideas. There are also concerns about the overreliance on AI for critical scientific tasks, potentially compromising research integrity and human oversight.

As we continue to work with schools and districts, we are being asked more and more about the best way to identify GenAI EdTech tools to pilot. Based on our experience as EdTech builders in the past, we created this guide to anchor conversations with tool providers (newly revised and re-designed). Here's what we suggest: Human Oversight and Quality Control Our users need to trust AI-generated content from your platform. What human oversight and quality control measures do you employ? Are there user warnings about accuracy of outputs? How do you ensure that feedback from users is being collected and actioned? Mitigating Bias in Outputs It’s important that the tools we use do not cause harm to our students or teachers. What steps are you taking to identify and mitigate biases in the underlying GenAI models your product uses? How will you ensure fair and unbiased outputs? Student Privacy and Ethical Data Use Protecting student data privacy and ensuring ethical use of data is our priority. What third parties have access to our data (e.g., OpenAI, Google)? Is our data used to train any internal or external GenAI models? What policies and safeguards can you share to address privacy concerns? Evidence of Impact We need evidence that your AI tool will improve learning outcomes for our student population and/or effectively support our teachers. Can you provide examples, metrics and/or case studies of positive impact in similar settings? Accessibility and Inclusive Design Our school needs to accommodate diverse learners and varying technical skills among staff. How does your tool ensure accessibility and usability for all our students and staff? What ongoing support and training is available? Link in the comments to save or download the PDF version! AI for Education #GENAI #edTech #responsibleAI

I’ve been reflecting on how we often consider future skills, digital transformation, or STEM careers without addressing a hard truth: socioeconomic disadvantage continues to block millions from accessing opportunity. And in the UK, that disadvantage is often as simple—and as serious—as a lack of internet. Here’s what that looks like: 📉 1.5 million UK homes are without internet access. For many students, this means no online homework, no virtual STEM clubs, and no exposure to the digital skills needed for tomorrow’s jobs. 🧪 STEM education is still uneven. Pupils from the most deprived areas are less likely to access advanced science and maths courses, and much less likely to pursue STEM careers. 🔌 Connectivity is key—and telecoms can help. A brilliant example? The National Databank, supported by Virgin Media O2 and Good Things Foundation. It’s been called a “food bank for data,” offering free mobile data, texts, and calls to people who can’t afford connectivity. Many O2 stores across the UK now serve as data donation hubs—bringing digital access right into local communities. 🧠 The result? Students stay connected. Adults can retrain. Families can access services. And no one is locked out of opportunity because they can’t afford data. Tech and telecoms companies have a real role in levelling the playing field—not just in innovation, but in inclusion. 💬 What other examples have you seen of organisations using infrastructure for impact? Let’s build a future where no potential is wasted because of a postcode. #DigitalInclusion #NationalDatabank #STEMAccess #TechForGood #LevellingUp #UKTech #SocialMobility #Telecommunications #DigitalEquity #FutureOfWork #InclusionMatters

Oxford University Press (OUP) recommends governments, school leaders, and education business leaders to support AI in schools while prioritizing educational quality. Their report acknowledges AI's potential but emphasizes education-leading technology, not the other way around. It highlights that 49% of teachers feel unprepared for AI's impact and calls for empowering teachers and students for an AI-enabled future. The report suggests: 👉Support, Don't Substitute Teachers: AI should aid teachers rather than replace them. When using AI-powered technology, prioritize teachers as guides, advisors, and supporters. 👉Quality Resources: Education publishers' resources maintain high standards and learning outcomes. Governments should consider regulations to preserve teaching and learning quality amidst AI experimentation. 👉Empower Teachers with Digital Skills: Provide comprehensive support and training for teachers to confidently use digital technologies in the classroom, including AI. 👉Equip Students with Essential Skills: Embed human skills like critical thinking, problem-solving, and digital literacy across curricula to complement AI. Regulation in educational AI is crucial to safeguard students from misinformation risks. 👉Prioritize Genuine Understanding: Encourage exploration, reflection, and interaction in learning experiences instead of relying solely on AI tools for knowledge. Adapt curricula and assessments accordingly. #AI #education #futureskills #criticalthinking #creativeproblemsolving #digitalliteracy

Learn fast, but act more slowly Authored by the UK Department for Education with input from leading practitioners and researchers such as Prof. Rose Luckin, Cheryl Shirley, Chris Goodhall and others, “The Safe and Effective Use of AI in Education – Leadership Toolkit” (June 2025) is a practical guide that helps school and college leaders plan, implement and govern generative-AI in line with national policy. The report is organised into seven video-based sections—Introduction, Audit of current practice, Safety, Opportunities, Embedding AI in a digital strategy, Department for Education guidance, and Planning for implementation—each broken down into focused sub-topics such as data/IP, safeguarding, staff workload, CPD and edtech frameworks.  Its goal is to give leaders an evidence-informed roadmap that aligns AI use with statutory duties, digital-technology standards and whole-school improvement priorities. Aimed primarily at head-teachers, trust and college executives, governors and IT/data-protection leads, the toolkit distils five headline messages / challenges: (1) begin with an honest audit to map gaps before adopting tools ;  (2) make safety non-negotiable—protect data, intellectual property and children’s welfare at every step ;  (3) harness AI to ease administrative load and personalise learning while keeping a “human-in-the-loop” to check accuracy and bias ;  (4) embed AI within a wider digital-strategy that covers policy, infrastructure, governance and sustained staff CPD ; and  (5) treat implementation as an iterative, evidence-driven process—monitor, reflect and adapt as technology, risks and pedagogical needs evolve . Source: https://lnkd.in/e5yjekwH

🍱 Design Patterns For Effective Dashboards (https://lnkd.in/ed6Rr_sC), with practical guidelines for designing better dashboards and practical UX patterns to keep in mind. Neatly put together by Benjamin Bach. 🚫 Don’t destroy user value by oversimplification. ✅ Oftentimes life is complex and tools must match life. ✅ Dashboard value is measured by useful actions it prompts. ✅ Aim to create understanding, rather than showing raw data. ✅ Start by studying audience, tasks and decisions to make. ✅ Choose what data is important for a user in each task. ✅ Choose a structure: single page, parallel pages, drill-downs. ✅ Select charts depending on data + level of detail to show. ✅ Then set layout density: open, table, grouped or schematic. ✅ Design interactions for exploration, filters, personalization. ✅ More data → more filters/views, less data → single values. ✅ Design for interface expertise levels: low, medium, high. ✅ Low: large text size, progressive disclosure, extra spacing. ✅ Medium: regular size/spacing, more data cards, shortcuts. ✅ High: small text size, heavy data, customization, filters. ✅ Support user’s transition between levels of proficiency. Dashboards are often seen as a way to organize and display data at a glance. And as such, too often it shows a lot of data without being actionable or meaningful. Yet the main task of a dashboard isn’t that — it’s to explain trends and communicate insights. Start by studying levels of user’s expertise. Segment the audience and explore what data they need to make decisions. Think carefully what charts would be both accurate and meaningful — rather than being an oversimplification or guide to misleading interpretations. Review defaults, presets and templates. Allow users to re-arrange and customize data density and widgets. Explore where a data table might help draw better conclusions. Most importantly: test your charts and dashboards meticulously. We don’t need to reveal all raw data at once, to everyone, and at the same scale and pace. But we need to support pathways for people to face complexity when they must, and discover only a set of actionable insights when they need. ✤ Useful Resources Dashboard Design Patterns & Workflow, by Benjamin Bach https://lnkd.in/eSCasdKG Practical Guide For Dashboard UX, by Taras Bakusevych https://lnkd.in/e5gMMgXv FT Visual Vocabulary (PDF), via Stéphanie Walter https://lnkd.in/ezu2w8Vr How To Design A Dashboard (free book), by David Matthew https://lnkd.in/enU-CxwU Data Dashboards UX Benchmarking, by Creative Navy UX Agency https://lnkd.in/edUgTH3G You Might Not Need A Dashboard, by Irina Wagner, PhD https://lnkd.in/eBSEkCyb #ux #design

A student once asked me, ‘Sir, will AI replace teachers?’ I paused, smiled, and said, "Not teachers—but it will change how we teach forever." As an educator and entrepreneur, I’ve witnessed every shift in the education industry, from chalkboards to digital classrooms. But nothing has intrigued me more than the rise of AI in education. A few months ago, a student in my class struggled with understanding rotational mechanics. Despite multiple attempts, he couldn’t grasp the concept. So, I experimented. I used an AI tool to create personalized simulations of real-life scenarios he could relate to. Within 30 minutes, the light bulb went off—he finally got it. That’s the power of AI. It’s not here to replace teachers; it’s here to empower us. How I See AI Shaping the Future of Education: → Personalized Learning Every student learns differently. AI allows us to create customized learning paths based on strengths, weaknesses, and pace. Imagine a classroom where no one feels left behind. → Better Access to Quality Education AI-powered tools can bring the best teachers and resources to even the most remote corners of the world, bridging the education gap like never before. → Liberating Teachers AI can take over repetitive tasks—grading, administrative work—so teachers can focus on what truly matters: teaching, mentoring, and inspiring. AI is a tool, not a solution. The magic of education lies in the human connection—a teacher understanding a student’s unspoken hesitation or cheering their smallest victories. At Motion Education Pvt Ltd, we’re already exploring how to integrate AI into our teaching methodologies without losing that human touch. Because the future of education isn’t man vs. machine—it’s man with machine. So, to my students: Don’t fear AI. Embrace it. Use it to amplify your learning. And to my fellow educators: Let’s lead this revolution together. The classrooms of tomorrow are in our hands. What do you think? Will AI transform education for the better, or is there more to consider? Let’s discuss. #AI #AIinEducation #EdTech #NVSir