Transforming Education With Edtech

The gap between academic preparation and industrial requirements remains a critical bottleneck for India’s growth. In my recent interview for HR ASSOCIATION OF INDIA "Campus to Corporate" edition, I argue that the transition from student to professional must move beyond a simple handoff toward a deeply integrated partnership. We must scale models like the BITS Pilani's "Practice School" across the country to ensure our graduates are not just degree holders, but industry-ready innovators. To leverage our demographic dividend, our institutions must prioritize deep-tech research and agile, credit-based industry training. We need a systemic shift where campuses become breeding grounds for job creators rather than just job seekers.

I spent the day with academicians reflecting on a simple but important question: Are we teaching subjects, or are we shaping thinking? What became clear through the dialogue is that education today needs more than incremental change. It needs reimagination. From curriculum to pedagogy to assessments, every layer must evolve in the context of Design Thinking and AI. Design Thinking, in my view, is not a tool or a course. It is a shift in worldview. When the way we see changes, knowledge reorganises. When knowledge changes, capability evolves. And when capability evolves, outcomes transform. Without this shift in thinking, any change we make will remain superficial. One of the core gaps in our system is this: - We focus on content, but not on purpose - Students learn subjects, but not why they are learning them - Problem solving is taught, but problem framing is not Education must move from content delivery to problem orientation. AI now accelerates this shift. For decades, education has been centred around answers. Today, answers are easily available. What is becoming scarce is the ability to ask the right questions. This changes the role of education fundamentally: - From answers to questions - From memory to thinking - From linear learning to multi-dimensional problem solving It also requires a rethink of assessments. Not just evaluating answers, but evaluating how students frame problems and approach solutions. At the same time, we must be conscious of the risks. Easy access to knowledge can weaken cognitive depth. Foundations matter. Concepts matter. Thinking cannot be outsourced. The balance is clear: - Human for thinking - AI for doing Another important insight is that change in education cannot be driven through isolated interventions. It requires sustained effort, dialogue, and a structured approach to transformation. Institutions will have to identify their friction points, prioritise them, and work through them over time. What encouraged me most was the intent across institutions. There is openness to rethink, to experiment, and to evolve. The opportunity ahead is significant. To move from teaching subjects to shaping thinkers, from solving problems to defining them, and from producing graduates to building agenda setters. That, to me, is the real purpose of education in an AI-first world. Intellect Design Arena Ltd School of Design Thinking Purple Fabric #DesignThinking #8012FinTechDesignCenter #AIinEducation #FutureOfLearning #HigherEducation #ReimagineEducation #Innovation #Learning #Leadership #DigitalTransformation

A ₹30 whiteboard marker quietly built one of India's biggest education companies. When I first looked at PW (PhysicsWallah), I assumed it was another edtech company that benefited from the online learning boom during COVID. The more I researched, the more I realised that wasn't the real story. Long before PhysicsWallah became a $2.8 billion company, Alakh Pandey was uploading free Physics lectures on YouTube. Years before institutional investors believed in the business, millions of students had already placed their trust in the teacher. Today, that trust has turned into one of India's largest education platforms. Here's what that looks like: ✅ 4.6+ crore YouTube subscribers across its learning ecosystem ✅ 5.5+ million paid students learning through PhysicsWallah ✅ ₹1,940 Cr revenue in FY24, growing over 160% year-on-year ✅ 180+ offline centres across 100+ cities, despite being born as an online-first platform ✅ Raised $210 million in 2024 at a $2.8 billion valuation, even as India's edtech sector witnessed one of its toughest funding environments What caught my attention wasn't the valuation. It was the sequence. PhysicsWallah earned trust first. Revenue followed. Investors came much later. While much of the industry competed through aggressive marketing and premium pricing, PhysicsWallah expanded the market itself by making quality coaching dramatically more affordable for millions of students. That decision changed more than pricing. It changed distribution. Students became the marketing engine. Word-of-mouth became the acquisition strategy. Every successful student became proof that the model worked. Here are my biggest takeaways: → Trust is one of the few competitive advantages that compounds over time. → Expanding a market can create far more value than simply competing within it. → The strongest businesses don't always raise capital first. They earn customer conviction first. Looking back, PhysicsWallah didn't just build another edtech company. It built one of India's largest trust-led businesses. Which Indian startup do you think scaled by earning trust before raising capital? #StartupStory #PhysicsWallah #BusinessStrategy #EdTech #Entrepreneurship #InvestmentBanking #CaseStudy #Startups

For over a century, the core of our education system has been built on a simple premise: knowledge transfer.    The teacher has the information, and the student's job is to acquire and retain it.    The age of AI is rendering that model obsolete overnight.   When every student has access to a tool that can instantly summarize complex theories, write elegant prose, and solve difficult equations, the value of simple knowledge retention plummets.    The debate over banning these tools in classrooms completely misses the point. It’s like trying to ban the calculator in the 1980s.   The real, far more urgent question is: What is school for, when the answers to everything are instantaneous?   💡 Critical Thinking & Discernment: The ability to evaluate the information AI provides, spot biases, and separate signal from noise.   💡 Creative Synthesis: The art of connecting disparate ideas in novel ways to create something entirely new.   💡 Ethical Reasoning: The wisdom to wield these powerful tools responsibly and with integrity.   💡 Incisive Questioning: The skill of formulating the perfect prompt or inquiry that unlocks a deeper level of insight.   We are moving from a world that rewards knowing the answer to a world that rewards knowing what question to ask.    Our challenge as leaders and parents is to redesign our educational framework. We must cultivate a generation of critical, creative, and ethical thinkers who see AI as a catalyst for deeper learning and innovation.

The inventor usually doesn't capture the value. The people who own the complementary assets do. That's the depressing truth from economist David Teece's 1986 paper "Profiting from Technological Innovation." When someone invents something new (and especially if the IP is weak), it’s usually the people who own the distribution, the brand, and the customer relationships who get rich. RC Cola pioneered the first major diet cola, Diet Rite, which briefly became a top-selling soda. But once Coke and Pepsi introduced their own diet colas and deployed their superior bottling networks and brand power, they captured most of the profits from the ‘diet cola’ idea while RC faded into the background. Most founders make the same mistake. They think innovation is the moat. It's not. Here's why most tech "innovations" don't create defensible businesses: Innovation 1: Most tech innovations are incremental improvements. You didn't invent a new category. You made something 10% faster or 15% cheaper. That's valuable. But not defensible. Your competitor can copy that in 6-12 months. Sometimes faster. The AI wrapper startups learned this the hard way. They built on OpenAI's API, and watched OpenAI launch the same feature three months later. Innovation alone is usually moatless. Innovation 2: Patents don't protect software like they protect pharmaceuticals. Pharma locks in 20 years of exclusivity. Software can't. You can patent a specific implementation, and your competitor can implement it differently. The technical innovation gets commoditized fast. So, what actually creates moats? Complementary assets. Asset 1: Brand and customer trust. Salesforce wasn't the first CRM. It wasn't even the best. But it owned "cloud CRM" in people's minds. Brand equity compounds. Asset 2: Distribution and customer relationships. Microsoft didn't have the best products in the 90s. Instead, it had every enterprise CIO's phone number. Whether we like it or not, that's a moat. Asset 3: Customer experience and retention mechanics. How easy is it to start? How hard is it to leave? Notion isn't just a note-taking app. It's a workspace your entire team lives in. Switching costs are massive. The product is good, but the lock-in is better. Asset 4: Data and network effects. Every user makes the product better for the next user. LinkedIn isn't valuable because of its interface. That’s mostly average. It's valuable because everyone else is there. That’s why professional networks built on LinkedIn or as a counter to LinkedIn never really took off. The lesson is clear. Stop obsessing over product innovation alone. Start building the complementary assets that let you capture the value your product creates. It's not about who invents first. It's about who builds the distribution, brand, and customer relationships that turn invention into lasting value. The best product doesn't always win. The best system does. #business #entrepreneurship #work

Are universities inefficient—or just measured badly? This article from THE reports on a UNICA conference where higher education leaders pushed back against policymakers’ growing calls for university “efficiency.” They argue that the word itself is unhelpful, imported from microeconomics, ill-suited to a sector where outputs are intangible, long-term, and hard to quantify. Is research output the number of papers, citations, or patents? Should teaching be measured in credit hours or lives changed? This critique is not wrong. But it is also not enough. While “efficiency” may not be the right word, universities do need to become more effective: more capable of turning resources (funding, talent, infrastructure) into outcomes that matter for their societies, economies, and ecosystems. The real issue is not only about language or communication. It’s also about what universities are doing, how they do it, and whether it’s still fit for purpose. Here are four reflections missing from this conversation: 🔹 Universities must measure what matters. Traditional metrics track inputs (funding, staff, student numbers) and outputs (papers, graduates), but rarely outcomes like improved social mobility, health, regional innovation capacity, or climate resilience. We need better tools to assess universities’ public value. 🔹This isn’t just a narrative problem. Yes, universities must communicate better. But messaging without meaningful change is branding. Societies aren’t just confused about what universities deliver, they're unsure that universities are delivering at all. 🔹Internal transformation is essential. Becoming more outcome-effective may require new staffing models, different promotion criteria, rethinking programme portfolios, and reallocating effort away from legacy activities. These are hard questions. We need to ask them. Effectiveness is contextual. A university’s value should be judged not just by global rankings but by what it contributes to its region, industry, and community. Metrics must be sensitive to mission, not just scale. Otherwise, doing well is simply a matter of hiring more, spending more, and graduating more. Ultimately, universities must do more than resist a hostile framing. They must define and demonstrate what success looks like in their own terms, through actions as much as narratives. #HigherEd #UniversityTransformation #PublicValue #EducationPolicy #AcademicImpact #InstitutionalEffectiveness #HEmetrics #FutureOfUniversities https://lnkd.in/gYBqngb6

The best moats are discovered, not built. I've reviewed thousands of startups and noticed a pattern: the most defensible businesses aren't those with the best initial ideas but those solving problems where the path to solution can only be found through iteration. This is why I believe every founder should answer one critical question: "What part of your business involves complexity that can only be mastered through trial and error?" If you can't identify this, you likely don't have a moat. Your competitors can copy your features. They can reverse-engineer your tech. They can outspend you on marketing. But they cannot easily replicate the knowledge you gain from: 1️⃣ The surprising data pattern that emerged after analyzing your 10,000th customer 2️⃣ That counterintuitive product decision that seemed wrong but solved everything 3️⃣ The subtle optimization to your AI training methodology that took 50 experiments to discover 4️⃣ The sales narrative that only worked after you'd had 200 customer conversations These insights aren't in books or blog posts. They can't be explained in investor memos. They live in the scar tissue earned through direct experience. This is especially critical for AI startups. Anyone can access the same open-source models and academic papers, but the configurations, prompts, and workflows that actually work? Those come from methodical experimentation. What protects you isn't what you build, but what you learn while building it. The founders I see winning aren't avoiding complexity—they're leaning into it, knowing that navigating it creates barriers for followers. They're meticulously documenting what works and what doesn't. They're building institutional knowledge that becomes increasingly valuable with each iteration. Defensibility is fundamentally about having earned insights that others must pay the same price to obtain. And that price is time, failures, and the willingness to embrace the complex. #startups #founders #growth #ai

#Transformation in #Education Over the next decade Here’s how this transformation might unfold: 1. #Personalized #Learning: Adaptive Learning Platforms: Education will increasingly leverage AI-driven platforms that tailor lessons, assessments, and feedback to individual student needs, learning styles, and paces. This will allow for more customized learning experiences, where students can progress at their own speed. Data-Driven Insights: Schools will use data analytics to track student progress more effectively and identify areas where each student needs more support or challenge. 2. #Blended and #Hybrid #LearningModels: Flexibility in Learning Environments: The pandemic accelerated the adoption of online and hybrid learning models, and this trend is likely to continue. Students will have more options to learn in a combination of in-person and virtual settings, allowing for greater flexibility and accessibility. Global Classrooms: Technology will enable more cross-cultural and international collaboration, with students participating in global classrooms and working on projects with peers from different parts of the world. 3. Focus on #Skills Over #Content: Shift to Competency-Based Education: There will be a stronger emphasis on developing critical skills like problem-solving, creativity, collaboration, and emotional intelligence rather than merely memorizing content. This shift will prepare students better for the demands of the modern workforce. Lifelong Learning: Education systems will place more emphasis on lifelong learning, encouraging continuous skill development throughout an individual’s career, rather than focusing solely on formal education during the early years. 4. Enhanced Role of #Teachers: Facilitators and Coaches: Teachers' roles will evolve from being content deliverers to facilitators of learning, guiding students in their personalized learning journeys and helping them develop the skills needed to succeed. Professional Development: Continuous professional development for educators will become more critical, with a focus on integrating new technologies and methodologies into their teaching practices. 5. #Equity and #Inclusion: Closing the Digital Divide: Efforts to ensure all students have access to the necessary technology and resources will be a priority, reducing disparities in educational opportunities. Inclusive Curricula: There will be a push for curricula that are more inclusive of diverse perspectives, backgrounds, and cultures, promoting a more equitable and holistic education for all students. 6. Alternative #Credentialing: Micro-Credentials and Badges: Traditional degrees may be supplemented or even replaced by micro-credentials, certificates, and digital badges that recognize specific skills or competencies. Recognition of Informal Learning: More value will be placed on informal and experiential learning, with students able to gain recognition for skills acquired outside of traditional educational settings.

🔥Academia 2.0: Reinvent or Become Obsolete 🚨 The NIH overhead crisis isn’t just about funding—it’s about survival. NIH’s decision exposes a harsh truth: academic research is financially fragile. Institutions lose money on federally funded research, relying on clinical revenue and philanthropy to stay afloat. But survival isn’t a strategy. Academia must transform. 📉 Stop the Growth Obsession—Focus on Productivity Too many institutions measure success by expansion—more faculty, bigger buildings, new institutes—instead of impact. But this model is unsustainable. Academia needs structural changes: 🔹 Flexible research programs—Static departments should be replaced with dynamic, time-limited research programs that sunset unless they deliver impact. 🔹 A smarter division of labor—Does it make sense that top scientists spend 50%+ of their time writing grants? Institutions should find ways to free PIs to focus on research. ⚖️ Change the Culture—Meritocracy 2.0 The traditional academic reward system is outdated. We need to redefine success: 🔹 Rethink publishing—With AI now capable of generating papers, academia should focus on novelty & real-world validation instead of just publication volume and external grant funding. 🔹 Dismantle the Ivory Tower—Public engagement should count toward promotion and tenure, ensuring research benefits society, not just citations. 🔄 Reinvent Academia-Industry Partnerships For too long, academia and industry have operated in separate silos. Instead, we need deep, structural integration: 🔹 Bidirectional mobility between academia and industry is critical. Right now, scientists leave academia for industry and rarely return. 🔹 Public-private R&D hubs— we need integrated research hubs where teams from both sides co-develop solutions from day one. 🔹 Smarter tech transfer & licensing—Many universities underutilize their IP portfolios. They must negotiate stronger equity positions and actively manage spinouts. 👨💼 Rethink Leadership—From Administrators to Innovators Many academic leaders lack experience beyond academia. Institutions need leadership that understands entrepreneurship, industry, and real-world impact. 🔹 Change selection criteria—Favor entrepreneurial experience, external innovation, and industry engagement over purely academic credentials. 🔹 Encourage industry/policy fellowships before taking leadership roles. 💰 Strengthen Financial Resilience—Beyond NIH Dependence NIH funding is critical, but academic institutions need broader revenue strategies to remain sustainable. In addition to industry-funded collaborations, we need: 🔹 University venture funds—Reinvest in high-potential discoveries, ensuring academia shares in long-term success. 🔹Philanthropy-backed research endowments—Create long-term financial stability for high-risk, high-reward science. Academia is at a crossroads. The IDC crisis isn’t just about budgets—it’s about universities reinventing for the future.

🌍 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