Key Considerations for Edtech Leadership

🌍 UNESCO’s Pillars Framework for Digital Transformation in Education offers a roadmap for leaders, educators, and tech partners to work together and bridge the digital divide. This framework is about more than just tech—it’s about supporting communities and keeping education a public good. 💡 When implementing EdTech, policymakers should pay special attention to these critical aspects to ensure that technology meaningfully enhances education without introducing unintended issues:  🚸1. Equity and Access Policymakers need to prioritize closing the digital divide by providing affordable internet, reliable devices, and offline options where connectivity is limited. Without equitable access, EdTech can worsen existing educational inequalities.  💻2. Data Privacy and Security Implementing strong data privacy laws and secure platforms is essential to build trust. Policymakers must ensure compliance with data protection standards and implement safeguards against data breaches, especially in systems that involve sensitive information.  🚌3. Pedagogical Alignment and Quality of Content Digital tools and content should be high-quality, curriculum-aligned, and support real learning needs. Policymakers should involve educators in selecting and shaping EdTech tools that align with proven pedagogical practices.  🌍4. Sustainable Funding and Cost Management To avoid financial strain, policymakers should develop sustainable, long-term funding models and evaluate the total cost of ownership, including infrastructure, updates, and training. Balancing costs with impact is key to sustaining EdTech programs.  🦺5. Capacity Building and Professional Development Training is essential for teachers to integrate EdTech into their teaching practices confidently. Policymakers need to provide robust, ongoing professional development and peer-support systems, so educators feel empowered rather than overwhelmed by new tools. 👓 6. Monitoring, Evaluation, and Continuous Improvement Policymakers should establish monitoring and evaluation processes to track progress and understand what works. This includes using data to refine strategies, ensure goals are met, and avoid wasted resources on ineffective solutions. 🧑🚒 7. Cultural and Social Adaptation Cultural sensitivity is crucial, especially in communities less familiar with digital learning. Policymakers should promote a growth mindset and address resistance through community engagement and awareness campaigns that highlight the educational value of EdTech. 🥸 8. Environmental Sustainability Policymakers should integrate green practices, like using energy-efficient devices and recycling programs, to reduce EdTech’s carbon footprint. Sustainable practices can also help keep costs manageable over time. 🔥Download: UNESCO. (2024). Six pillars for the digital transformation of education. UNESCO. https://lnkd.in/eYgr922n  #DigitalTransformation #EducationInnovation #GlobalEducation

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.

🪴 Leading in education through the AI transition requires more than tool adoption. It requires judgment, systems thinking, and the courage to redesign how decisions are made. This article is a timely reminder that artificial intelligence in education is not a future conversation. It is already shaping curriculum design, workforce readiness, instructional models, and how institutions measure impact. The leaders who will succeed are the ones who understand AI as an operating shift, not a side initiative. Three ideas that stood out for education and innovation leaders: 🪴 AI literacy is now a leadership responsibility. Understanding how AI influences learning, assessment, and access is no longer optional for superintendents, presidents, and policymakers. 🪴 Equity must be designed into AI strategy. Digital equity, data governance, and responsible deployment need to be addressed at the system level, not after implementation. 🪴 Education strategy and workforce strategy are converging. Preparing students for an AI-enabled economy requires tighter alignment between education, industry, and public sector leadership. If you are leading in education, technology, or public service, this is essential reading as we navigate the next phase of digital transformation together. ♻️ Repost if this resonates 🔗 Follow Dr. Kiesha King or visit thedrking.com for more on education strategy, AI, innovation, and leadership. Views are my own and do not represent the official positions or opinions of any organization with which I am affiliated.

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

Can AI revolutionize education without compromising pedagogy or ethics? Yaacoub et al. (2025) research says yes—if we design it wisely. Synthesizing four interrelated studies, the authors present a three-phase framework that elevates AI-generated educational content from mere automation to a powerful, student-centered learning tool. Key recommendations: 1) Cognitive Alignment: Embed established frameworks (Bloom’s and SOLO taxonomies) into AI tools to ensure that generated content targets appropriate learning depths—from basic recall to abstract thinking. 2) Linguistic Feedback Optimization: Use linguistic analysis to improve AI-generated feedback for clarity, tone, and engagement. Metrics like readability and sentiment help personalize responses, enhancing student comprehension and motivation. 3) Ethical Safeguards: Implement bias detection, explainable AI, and human oversight to ensure AI respects fairness, transparency, and inclusivity—protecting learners from systemic harm. For Decision-Makers: Investing in AI for education isn't just a tech upgrade—it's a strategic move that requires pedagogical integrity and ethical accountability. This framework offers a ready-to-implement roadmap to create scalable, inclusive, and cognitively rich learning experiences. #AILeadership #EdTechStrategy #ResponsibleInnovation #FutureOfLearning #InclusiveEducation #LeaderTech 📬 Vous aimez ce type de contenu ? Je partage chaque mois une newsletter (gratuite et indépendante) dédiée aux décideurs éducatifs, avec cas concrets, outils et analyses stratégiques : → [LeaderTech: https://lnkd.in/eNm2F9Ec ] Version anglaise disponible ici: → [EdTech Research Insights https://lnkd.in/gvHqj7jR ]

Over the past few years at the American International School of Guangzhou, one of the biggest shifts in our thinking around educational technology has been realising that we no longer need to force our workflows, pedagogy, and school priorities to fit within disconnected external platforms. Instead of asking, “Which tool should we purchase?”, we started asking, “What are we actually trying to achieve, and importantly, how might we design systems around our people, our vision, and our context?” That mindset has led us to develop a growing ecosystem of bespoke solutions from supporting classroom observations, professional learning, leadership reflection, to goal setting, and AI-supported workflows — all intentionally connected to our school improvement priorities and continuously refined through user feedback. Importantly, this is not a finished product or a one-time initiative. It is an ongoing process of listening, refining, redesigning, and evolving our systems each year to ensure they remain aligned to who we are as a school and what we value most in teaching and learning. What has been most exciting is realising how accessible this work has become. With the rise of low-code platforms, AI-assisted development, and cloud-based tools, schools now have far greater agency to design systems that truly reflect their culture and priorities rather than adapting themselves to generic workflows. This article reflects on that ongoing journey at AISG, the importance of reverse engineering from end-user needs, and why the future of edtech may belong less to schools that buy the most tools — and more to schools that build the most coherent systems.

Research tracking actual edtech usage across K–12 districts shows that 60–70% of purchased ed-tech licenses go unused. Nationally, that adds up to $1+ billion every year in underutilized or unused software. That’s not a technology failure. That's an adoption and oversight failure. The good news: districts that address this intentionally can claw back both dollars and instructional focus. What works: • Designate a clear instructional owner for every tool. No owner, no renewal • Right-size licenses annually based on real usage, not enrollment • Simplify portfolios with fewer tools, deeper implementation • Build adoption benchmarks into contracts and renewal decisions • Invest in training for teachers with ongoing support, not one-time PD • Require vendors to provide transparent usage and impact data • Sunset unused tools regularly, make stopping just as normal as starting The hidden cost of edtech isn’t the license. It’s the clutter, confusion, and lost time when tools don’t earn their place. The next phase of edtech isn’t about buying smarter tools. It’s about managing them better. If school districts did this consistently, the budget conversation would shift from “we need more” to “we’re finally getting value.” #edtech #education #edbudget #edleadership #teachers #schools #edreform

I recently "sat" down with Scott Elliott at EdWeek Market Brief to discuss a hard truth: the edtech industry is at a critical inflection point regarding our "social license" to operate. A major catalyst for this conversation was the release of the InnovateEDU and Instructure 2026 Evidence Report, as well as the growing edtech pushback from within the ecosystem, including educators and parent advocacy groups. The findings are a wake-up call for the entire ecosystem. While we’ve seen a massive surge in tool adoption, the data shows a staggering research gap: The Reality Check: Only 40% of purpose-built edtech tools have identifiable evidence aligned to ESSA standards. For general consumer tools used in classrooms, that number drops to a mere 2%. The "Level IV" Trap: Even among tools with evidence, many are sitting at Level IV (demonstrating a "rationale"). While this is a starting point for innovation, it cannot be the finish line. As I shared with Scott, we are moving into an era of dual determination of financial necessity and a new ROI - what is the edtech's return on instruction? With pandemic-era funding winding down, districts are no longer asking what a tool can do—they are demanding proof of what it actually does for student and educator outcomes. For edtech companies, evidence-building is no longer a differentiator; it is a baseline requirement. To earn back the trust of parents and educators, we must: Move Beyond Sleek Brochures: Prioritize rigorous, third-party validation over marketing claims. Commit to Transparency: Be radical about sharing not just where a tool succeeds, but for whom and why. Design for Impact: Align product development with the learning sciences from day one. The "funky" politics of screen time and AI skepticism won't be solved with better PR—they’ll be solved with better proof. Let’s shift the signal from simple adoption to a shared commitment to high-quality, impactful innovation. Read the full interview and dive into the report here: https://lnkd.in/gP3Rujhe #EdTech #EvidenceBased #ESSA #InnovateEDU #Instructure #EducationPolicy #AILiteracy #ImpactData

📊 How can we use data science to truly improve schools? For over 50 years, education leaders have been urged to leverage data for decision-making. Yet despite massive investments in dashboards and analytics systems, research shows that the link between data use and actual improvements in student outcomes is often weak. In my new paper, “Data Science in Education Administration, Policy, and Practice”, I argue that education data science should be understood as a third core methodology in education research, alongside quantitative and qualitative traditions. Open Access Preprint: https://lnkd.in/eKYTr3i3 Key insights: 🔹 Beyond dashboards: Data science is more than reporting — it involves machine learning, visualization, and exploratory data analysis to support evidence-based improvement cycles. 🔹 Prediction matters: School leaders need accurate predictions, not just statistical model fit. Accuracy should stand alongside theory in informing decisions. 🔹 Algorithms in education must be Accurate, Accessible, Actionable, and Accountable (the “4As”). 🔹 Capacity building: We need to train educational data scientists who can both analyze data and communicate findings to policymakers, teachers, and communities. In effect, we must train people who can talk to people and talk to machines. 👉 The goal is not to replace theory, but to balance explanation with prediction — and to center human judgment, ethics, and collaboration in the process. 🔑 Key Takeaways for the Field For Practice: Schools and districts should embed data science partnerships — not just dashboards — into leadership and improvement cycles. Joint sensemaking between analysts and leaders is essential. For Research: We must expand beyond model fitting to systematically test prediction accuracy and build open, reproducible workflows that connect theory, and application. For Training: Graduate programs in education leadership and policy need roadmaps for education data science capacity building — equipping future leaders to understand, question, and apply advanced analytics responsibly. A key practice for training from Data Science is the Common Task Framework which focuses on: (a) open large-scale real-world deidentified datasets, (b) a shared culture of shared code for shared research, (c) public and open evaluation of algorithms. I’d love to hear from colleagues! Let me know what you think! Open Access Preprint: https://lnkd.in/eKYTr3i3 #EducationResearch #DataScience #EducationPolicy #SchoolLeadership #LearningAnalytics #EdTech

Edtech is often criticised for poor quality, misuse of student data and limited learning impact (I’ve voiced those concerns myself several times). But we can’t hold systems accountable without first showing what good or exceptional performance looks like. Once that’s clear, we can create competitive pressure and drive improvement.  ⬇️ Excited to finally share our paper in HSCC Springer Nature that outlines key benchmark criteria for high-quality EdTech. The paper summarises the work our research group has been doing over the past three years. It focuses on educational impact and edtech’s added value for students’ learning. 📚 After an extensive literature review and cross-sector consultations, we’ve developed a multidimensional framework grounded in the “5Es” — efficacy, effectiveness, ethics, equity, and environment.  Efficacy and Effectiveness combine experimental evidence with process-focused metrics and pedagogical implementation studies. Broader metrics focus on ethical data processing, inclusive and equitable approaches and edtech’s environmental impact. 👇 The fifteen tiered impact indicators already guide a comprehensive and flexible evaluation process of international policymakers, educators, EdTech developers and certification bodies (see EduEvidence - The International Certification of Evidence of Impact in Education and our case studies). 🙏 Huge thanks to all who contributed, especially through our participatory Delphi process. Your insights were invaluable! Nicola Pitchford Anna Lindroos Cermakova Olav Schewe Janine Campbell /Rhys Spence Jakub Labun Samuel Kembou, PhD Tal Havivi/ Ayça Atabey Dr. Yenda Prado Sofia Shengjergji, PhD Parker Van Nostrand David Dockterman Stephen Cory Robinson Andra Siibak Petra Vackova Stef Mills Michael H. Levine  #EdTech #ImpactMeasurement #5Es #EdTechQuality #EdTechStandards 👇 Read here or download from: