How Edtech Improves Educational Outcomes

In 1984, Bloom proved one-on-one tutoring could push kids from the 50th percentile to the 98th. The catch? It was too expensive for most to access. Today, in a school in Nigeria, that dream just got a test run with a free AI tool and a shaky internet connection. Here’s how it worked: 700 high school students from public schools in Benin City, Nigeria, joined after-school English sessions over six weeks. No fancy edtech platform, just Microsoft Copilot powered by GPT-4, and teachers guiding rather than lecturing. The early signs were promising. Students reported clearer writing, fewer spelling errors, and richer vocabulary. Some said the AI helped them feel more confident communicating in English.  After six weeks the results speak for themselves. Students in the treatment group improved their scores by 0.31 SD overall. They also performed significantly better in the end-of-year examinations, which covered broader topics than the ones included in the program. Researchers estimate that a full year of participation could yield 1.2 to 2.2 standard deviations of improvement, based on attendance. Even more striking: the program delivers over 3 years of learning growth per $100 spent, making it a very cost-effective method. These findings highlight both the promise and the precautions necessary with AI in education. My takeaway: of course, AI can hurt learning if it becomes a crutch, encouraging cognitive offloading and shallow engagement. But when paired with sound pedagogy and human oversight, it can accelerate growth. Teacher shortages are worsening. Scalable, affordable tutoring is not a nice-to-have anymore. AI like this could be a part of the solution.

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

Most ed-tech startups chase students one ad at a time. Paper did the opposite. Instead of fighting for individual sign-ups, they asked a better question: What if every student in a district had access to on-demand tutoring — for free? So they built a model schools could say yes to: ▪️Unlimited 24/7 tutoring ▪️Multi-language support ▪️Measurable student progress dashboards ▪️No cost barrier for families And instead of “growth hacks,” they focused on institutional trust and system-level outcomes. The result? ▪️Partnered with 400+ school districts ▪️Supporting millions of students ▪️Raised over $390M+ ▪️Backed by top global funds ▪️Valued north of $1B at peak They didn't scale with noise.   They scaled with impact data: ▪️Student confidence up ▪️Homework completion up ▪️Equity gap closed for low-income districts This wasn’t a marketing play.   It was a mission-distribution-trust flywheel: Impact → Proof → District adoption → Scale → Deeper impact And the insight founders often overlook? Distribution strategy can be your deepest moat. Not features, not buzz — contracts + outcomes. If you’re building in a space where trust beats virality, ask yourself: Are you chasing users or systems? Are you selling features or outcomes? Are you optimizing for hype or proof? Because the startups that get adopted don’t always scream the loudest. They quietly become infrastructure. What market could you win by earning trust at scale instead of chasing clicks? #EdTech #Paper #FounderLessons #ImpactMetrics

🧠 Teaching the Machine to Teach: Ministries, AI, and the Future of Learning by EdTech Hub 📘 This learning brief explores how ministries of education in low- and middle-income countries are harnessing artificial intelligence (AI) to strengthen education service delivery. 🌍 It explains why AI matters—improving efficiency, equity, and data-driven policymaking—and how it’s being applied to automate administration, optimise teacher allocation, predict dropouts, and inform curriculum reform. ⚙️ 🤝 Supported by EdTech Hub, UNESCO, the World Bank, and innovation partners, these efforts demonstrate how AI can transform education governance—if guided by ethical frameworks, inclusive infrastructure, and robust local evidence. 🚀 1. 💡 What roles does AI play in education systems? 🤖 AI streamlines administration, enhances data analysis, predicts risks, and supports curriculum and policy design. It automates routine tasks, strengthens Education Management Information Systems, and enables evidence-based decisions. 2. ⚙️ Why is AI integration important for education ministries? 📊 AI improves operational efficiency, reduces costs, and offers real-time insights into student performance and institutional needs. It enables predictive analytics, optimises resource use, and drives targeted interventions—helping ministries overcome systemic barriers while promoting equitable, evidence-based. 3. 🌍 How are ministries using AI practically?   🏫 Countries use AI for attendance tracking, teacher deployment, and dropout prediction. Emerging tools like digital twins simulate education systems to test policies. 4. 🤝 Who is leading these initiatives?   🧭 Education ministries, with partners and national AI agencies, are leading adoption. Collaborations with research institutions and technology firms support pilot projects, frameworks, and ethical standards—ensuring solutions fit local needs and advance national education priorities responsibly and inclusively. 5. 🚀 What are the future priorities for AI in education?   🔍 Strengthening governance frameworks, investing in digital infrastructure, and generating robust evidence are essential. Ministries must prioritise equitable access, bias mitigation, and teacher training. Challenges ⚠️ 1. 📉 Limited empirical evidence and small-scale pilots hinder informed policy adoption. 2. ⚖️ Algorithmic bias risks reinforcing socioeconomic, gender, and regional inequalities. 3. 🖥️ Weak digital infrastructure limits scalable AI integration in LMICs. 5 policy maker recommendations 🧩 1. 🛡️ Establish ethical frameworks ensuring privacy and accountability. 2. 🌐 Invest in digital infrastructure for equitable AI access nationwide. 3. 👩🏫 Build teacher capacity for AI literacy. 4. 🔄 Promote iterative pilot testing before scaling AI applications. 5. 🤝 Foster public-private partnerships to support sustainable AI innovation. Source: https://lnkd.in/e8fu56N7

����𝐡𝐞 #𝐆𝐞𝐧𝐀𝐈 𝐡𝐲𝐩𝐞 𝐢𝐧 𝐞𝐝𝐮𝐜𝐚𝐭𝐢𝐨𝐧: 𝐀𝐫𝐞 𝐰𝐞 𝐦𝐢𝐬𝐬𝐢𝐧𝐠 𝐭𝐡𝐞 𝐩𝐨𝐢𝐧𝐭? 🧐 Many #EdTech companies are marketing AI tools to educators with a focus on "speed" and "efficiency." But as an educator, I have to ask: 𝑾𝒉𝒆𝒏 𝒅𝒊𝒅 𝒆𝒇𝒇𝒊𝒄𝒊𝒆𝒏𝒄𝒚 𝒃𝒆𝒄𝒐𝒎𝒆 𝒐𝒖𝒓 𝒑𝒓𝒊𝒎𝒂𝒓𝒚 𝒈𝒐𝒂𝒍? In my experience, the true potential of AI in education lies not in saving time, but in enhancing learning outcomes. Let me share an example: Over the past three semesters, I have implemented AI-powered formative feedback tools in my courses. These tools use my assignment rubrics to provide feedback to student before they submit their final work for grading. The goal? Not to cut my grading time, but to empower students to: · Identify strengths and areas for improvement · Attempt to close knowledge gaps independently · Enhance the quality of their work before submission Since using these AI tools for formative feedback, I've noticed that my students plan ahead to allow time for revision and approach me with targeted questions about their work. As a result, I can spend time on more advanced discussions rather than basic corrections of their work. What are your thoughts on the role of AI in education? Are we too focused on efficiency at the expense of effectiveness? #AIinEducation #TeachingInnovation #HigherEd #EdTechTrends

As education technology has come under growing scrutiny, it’s worth paying attention to the anomalies—the cases that don’t fit the dominant narrative. One of the most important, in my view, is Imagine Worldwide – full disclosure, I sit on its board. With nine randomized controlled trials showing positive and significant effects on literacy and numeracy for learners in some of the most challenging contexts in the world, Imagine Worldwide is a reminder that the question is not simply whether edtech “works.” It’s how the technology is designed and what model it is embedded within. A few things stand out: 1️⃣ It avoids one of the biggest problems with edtech: distraction - Many valid critiques of technology in schools start with the reality that internet-connected devices open the door to endless diversions. Imagine Worldwide’s model sidesteps that because its digital curriculum is delivered offline on solar-powered tablets. Students are not being pulled into the broader internet. They are focused on the learning task in front of them. 2️⃣ It solves the “5 percent problem - As Laurence Holt has documented, many digital learning tools show promise in research but fail in practice because most students simply do not use them at the recommended dosage. Imagine Worldwide addresses that by building a clear implementation model: students have a dedicated daily block, a dedicated space, and adults whose role is to keep them on task and making progress. 3️⃣ The technology is not layered on top of the old model - This is the point I keep coming back to. Imagine Worldwide is not just handing out tablets and hoping for transformation. The technology sits inside a tightly designed instructional model with dedicated time, constrained use cases, implementation monitoring, and a clear role for mastery-based progression. 4️⃣ The lesson is bigger than one program - The extensive research behind Imagine Worldwide suggests that the effects of educational technology may depend less on whether learning is “digital” and more on whether the digital environment is designed to support sustained attention, deliberate practice, and real progress. In other words, the most important variable may not be the device. It may be the learning model.

For decades, we've sorted kids into neat little boxes—A, B, C—letting labels define their potential and future. But what if I told you there's no such thing as a "C student"? There are only students with knowledge gaps—and the right technology can close them. Picture this: Two 5th graders tackle fraction division. - Olivia masters it effortlessly, scoring an A - Leo struggles, earning a C What happens next is where our system fundamentally fails. Class marches on while Leo—carrying critical knowledge gaps—falls further behind until he eventually says "I'm just not a math person." Think about that child labeled as "struggling." What if they just needed: • A concept explained differently • Time to recover missed material when sick • Simply more time with the lesson Data tells us that performance on 3rd-grade tests strongly predicts academic outcomes in 10th grade. Students struggling with reading in 3rd grade are 4x less likely to graduate on time—1 in 6 never complete high school. We're essentially determining children's educational destiny before they lose all their baby teeth. Miss a foundation piece, and everything above becomes unstable. This is why so many students decide: "Math isn't for me" "I can't do science" "Maybe college isn't my path" Self-limiting beliefs close doors to university opportunities, scholarships, and future career paths. But it doesn't have to be this way. Stop accepting the myth of "average students." AI-powered educational technology is the great academic equalizer: • Identify knowledge gaps • Develop personalized learning paths • Make learning engaging through personalization • Adjust teaching in real-time based on performance Ready to revolutionize learning outcomes? 1. Embrace the AI paradigm shift. Stanford University research confirms AI chatbots haven't increased student cheating rates. Instead of fearing misuse, imagine students "chatting" with historical figures or receiving bite-sized, gamified lessons instead of slogging through dense textbooks. 2. Become a discerning edtech evaluator. Seek platforms that deliver personalized tutoring, interactive mastery exercises, and data-driven insights that guide intervention. 3. Leverage AI as your educational partner. The path forward combines cutting-edge technology with human guidance—setting ambitious goals, tracking metrics, and fueling motivation. 4. Stay ahead of the curve. The tool that will revolutionize your students' outcomes might be launching tomorrow. Here's what I know to be true: Your child isn't defined by their current grades. They simply need the right tools to fill their knowledge gaps. Every student can excel when given the proper support. The traditional notion of A, B, or C students belongs in education's past. Let's reframe the "C student" as what they really are: a learner who just hasn't mastered the material *yet*. With the right technology and mindset, today's struggling students could become tomorrow's innovators.

Edtech is quietly moving away from a one size fits all model to something far more powerful - learning designed around the individual. With AI embedded into platforms, students are no longer forced into rigid pathways instead, content adapts in real time to their pace, gaps, and context. This shift is not just about convenience, it is exposing how outdated traditional learning structures have been for years. The real breakthrough lies in personalization at scale. From instant doubt resolution to adaptive assessments and AI generated learning formats, the system is becoming responsive rather than prescriptive. It is reducing friction, improving engagement, and making quality support accessible beyond urban centers. But it also raises an important question, if learning can now adjust to the learner, why are institutions still built around standardization? AI will not replace structured education, but it is clearly reshaping how it is delivered and experienced. The winners in this space will be those who blend technology with pedagogy, not compete against it. The future of education is not just digital - it is deeply personalized, continuously evolving, and finally aligned with how humans actually learn. #KYA_LAGTA_HAI #EdTech #ArtificialIntelligence #FutureOfLearning #PersonalizedLearning #EducationInnovation #AIinEducation #DigitalTransformation #LearningReimagined

Edtech, used well, can do two extraordinary things: it sets the floor and it unleashes teacher capacity. Setting the floor means every student gets access to high quality baseline content, even in classrooms where teacher expertise varies widely. It creates consistency, clarity, and a baseline of rigor that too often depends on luck. Unleashing teacher capacity means giving teachers back their most valuable resource: time. Time for small groups. Time for 1:1 support. Time to respond to the huge range of academic needs sitting in front of them every day. These two things matter most in our highest need communities, where staffing challenges are real, academic variance is enormous, and the demands on teachers are relentless. The goal of edtech should never be replacing great teaching. It should be making great teaching possible at scale. While the critiques of edtech have real validity, an overcorrection could do real damage.

What if the act of taking a test was indistinguishable from the act of learning? Why wait weeks for summative scores when multimodal AI can map student performance in real time? How much brilliance goes unnoticed because tests only score final answers? In our newly released case study, "Accessible by Design," my co-author Edward Metz and I explore a necessary paradigm shift. For decades, education has relied on static exams to rank students. This retroactive auditing identifies misconceptions months after the window for effective intervention has closed. We must deemphasize retroactive auditing. Let's build proactive, real-time support systems and erase the boundary between testing and instruction entirely. One future of EdTech is using multimodal AI to capture "learning in motion." Imagine spoken reasoning, applied research, classroom debate, and video game performance becoming learning metrics. By analyzing complex data streams—natural speech, revision of evidence-based writing, open-ended problem-solving—AI has the potential to illuminate a student's thinking as it happens. To ensure these tools serve all students, they must be grounded in Universal Design for Learning (UDL) and Evidence-Centered Design (ECD). These frameworks strip away construct-irrelevant barriers, transforming assessment into an invisible engine delivering personalized scaffolds. Ed and I are incredibly proud to highlight trailblazing companies from the federal ED/IES SBIR portfolio already transforming measurement: 🔬 STEM: OKO, KASI, PocketLab (NotebookAI & G-Force), Water Guardian, INQits, 2 Sigma Schools, StepWise. ✍️ Literacy: LightSide Labs (Turnitin Revision Assistant), Scrible, CG Scholar, Kibeam, Sound Town, Moby.Read, Capti (ETS ReadBasix). 🌱 Support: SownToGrow, Education Modified, PACE AI. I encourage folks to read this piece. Join us in rebuilding assessment to cultivate human potential, not just audit it. 📖 Read the full case study...