Classroom Technology Updates

🚀 AI Agents in Education can bring a much needed change in how we learn One of the most important aspects of learning is understanding that every student learns differently. Some prefer reading, others learn through visuals, and some excel through hands-on experience. Unfortunately, most of our current educational material assumes a one-size-fits-all approach, leaving little room for personalisation. I am someone who prefers visuals and hand-on learning and there have been many scenarios where I wished the books that I was reading had a way to elaborate the topic through an interactive video or practical exercise. 🎯 This is where AI Agents will make a big difference. Unlike traditional systems, AI agents can adapt to a student’s unique learning style by analyzing past interactions and tailoring content accordingly. For example: 📍 If a student learns better through visuals, the AI agent can generate diagrams or videos. 📍 For a student who thrives on hands-on learning, the agent can design interactive exercises or simulations. By doing this, AI agents act less like tools and more like personalized teachers, understanding what a student knows, what they struggle with, and how they prefer to learn. I am hopeful that in 2025 and beyond, we can expect AI agents to be widely used in classrooms and educational platforms. They’ll help teachers save time by automating administrative tasks and providing personalized support to students. Teachers can leverage AI agents in creating custom lesson plans and can answer student queries in a way that promotes deeper understanding, much like a skilled teacher does. While these agents are not yet perfect, they are improving fast. In the coming years, they will likely become a key part of education, making learning more engaging, accessible, and effective. 🚀 The impact of AI agents will go beyond the classroom, transforming how students and teachers interact with technology. Education has long sought to personalize learning, and AI agents can finally making this vision possible. I write about #artificialintelligence | #Technology | #Startups | #Mentoring | #Leadership Vignesh Kumar

Neha wants to learn AI. She’s ambitious. Watches a few YouTube tutorials. Buys a top-rated course. Bookmarks 10 blog posts. Downloads ChatGPT and Midjourney to “explore on her own.” Fast forward 3 weeks— Zero progress. Just a bunch of half-finished modules and rising self-doubt. And she’s not the exception. A study published in Heliyon (2023) found that over 90% of learners drop out of MOOCs before completion. And most never return. Why? Too much content. Too little clarity. We thought making learning accessible meant flooding the internet with tutorials, certifications, and micro-courses. But in this sea of abundance, people are drowning in indecision. Everyone's a creator. Every platform's a catalog. But very few are curators. That’s the shift we’re headed toward. From aggregation to personalisation. From volume to velocity. From watch more to learn better. Imagine this: You start a course. Within minutes, the platform knows you prefer visuals over text. It adapts the pace to your attention span. Realizes you’re great at theory but struggling with real-world AI applications. Re-routes your learning path. Reinforces your weak points. And speaks to you like a mentor, not a menu. Because learning shouldn’t feel like finding a needle in a haystack. It should feel like the needle finds you. This isn’t some future vision. The tools already exist — from fine-tuned LLMs to adaptive learning engines. But the real question is: Who will use them to make learning feel truly personal again? In an age of infinite options, clarity is the competitive edge. And the next generation of education products will win not by offering more, But by offering exactly what matters. Curated. Contextual. Conversational. --- What would it take for your learning journey to feel like it was built just for you?

Can Software Double Learning? Reflections on the Andhra Pradesh PAL Study A major evaluation in Andhra Pradesh’s government schools has made global headlines. A team led by Nobel laureate Michael Kremer finds that Personalised Adaptive Learning (PAL) software doubled measured learning rates for 14,000 students across 1,200 schools. For Class 6, this meant the equivalent of two years of progress in just one year. This is an important result. For decades, Indian classrooms have struggled with overcrowding and diverse learning levels. PAL addresses this by tailoring practice questions to each child’s ability, something a single teacher with 40–60 students cannot easily do. The Andhra trial confirms what earlier experiments in India and Kenya (Muralidharan, Singh, & Ganimian, 2019; Banerjee et al., 2016) had shown: adaptive technology can deliver real improvements in maths and language learning. Yet the story is more complex. Learning Beyond Test Scores The “doubling” claim rests on test outcomes. While foundational skills are vital, education is not reducible to exams. Creativity, empathy, higher-order thinking skills, critical thinking, and cultural understanding remain invisible to the software. Narrowing education to what algorithms can track risks shrinking the purpose of schooling. Unequal Gains The study found boys gained more than girls. This gap reflects entrenched inequities in digital access and social norms, not just software design. Andhra’s classrooms remain stratified and resource-divided. Without deliberate safeguards, technology will mirror and even reinforce these inequalities rather than correct them. The Politics of EdTech The trial is significant because it is publicly funded, unlike many private EdTech apps. But key questions persist: Will PAL support teachers or erode their authority? Who owns the vast learning data generated? Are public schools becoming sites for global EdTech experiments? As research on EdTech warns (Williamson & Hogan, 2020; Selwyn, 2022), technology can bring surveillance, privatisation, and market logics into public education. A Way Forward The Andhra study matters because it shows that personalised learning works. But scale-up must be careful: (i) Keep teachers central and build their professional capacity. (ii) Address gender, community, and rural divides in access and outcomes. (iii) Measure learning more holistically, beyond maths and language scores. (iv) Ensure local ownership of data and curriculum. Adaptive software can accelerate test outcomes, but education’s task is far larger: shaping thoughtful, ethical, and culturally rooted/critical human beings. That remains beyond the reach of any algorithm. Critical EdTech India (CETI) #EducationResearch #EdTech #PublicPolicy #LearningOutcomes #AdaptiveLearning #GlobalEducation #CriticalEdTech #EquityInEducation #DigitalLearning #EdTechForGood #LearningEquity #PolicyAndPractice #IndianEducation #GovtSchools #PAL #EducationReform

Amazing! This is the present and the future of learning experience creation. I now have a fully working system that automatically personalizes learning based on learner data, data from the business, and learner actions. The cafe scenario based learning experience I created is supposed to mimick logging into a fake Point of Sale System (POS) and launching training alongside the POS. I created a system on the back end that pulls in data on who the cafe lead is, their store, scans multiple stores reviews to pull the matching data on their specific store reviews, generates a scenario tailored just to them with OpenAI, and sends it straight into my scenario template. The learning experience they load on their screen updates almost instantly. This means no more manually creating learning experiences for different audiences. I can now automatically create a dynamic, data driven learning experience that adapts itself the second the learner enters the system. Now that this is working, the next steps are to limit the scenarios to pull only from data in a specific time period. If current data is missing, the system will fall back to other priorities like safety goals or incidents at nearby stores that could happen here. I also need to update the visuals so the images match whatever scenario is generated or remove them when they are not needed. This is the type of system I deeply care about building. It uses learning sciences, automation, and AI to create scalable experiences that support business needs. What possibilities do you see when learning experiences can adjust immediately based on data and actions? #LearningDesign #VibeCoding #LearningSciences #GenerativeAI #AIinLearning #n8n #LearningEcosystems #EdTech #WorkplaceLearning #InstructionalDesign #PersonalizedLearning #FutureOfLearning #eLearning

Gen Alpha students are learning with AI tutors while your workforce still sits through PowerPoint presentations The learning divide is creating a talent transformation crisis. Today we tracked how AI-powered education is reshaping Gen Alpha and Gen Z, and the implications for CXOs are staggering. The New Learning DNA: → Personalized Learning Paths: Squirrel Ai Learning and ALEKS Corporation adapt to individual learning styles, creating custom curricula for each student ↳ Workforce Impact: Gen Alpha expects hyper-personalized development plans, not generic training modules → Instant AI Feedback: Khan Academy's Khanmigo provides real-time learning adjustments based on student performance ↳ CXO Reality: New hires expect immediate, contextual feedback - traditional annual reviews feel archaic → Virtual Experimentation: AI-powered virtual labs let students run risk-free experiments and simulations ↳ Business Implication: This generation thrives on trial-and-error learning, demanding safe spaces to innovate and fail fast → Micro-Learning Mastery: Students consume knowledge in bite-sized, AI-curated chunks optimized for retention ↳ Leadership Challenge: Long-form training sessions are becoming obsolete as attention spans adapt to micro-content The data is clear - students using AI learning tools show 70% faster skill acquisition and 85% better knowledge retention compared to traditional methods. But here's the kicker: they're entering workforces still operating on industrial-age learning models. Bridging the Learning Gap → Redesign Onboarding for AI-Native Minds: Create interactive, personalized learning journeys that mirror their educational experience → Implement Real-Time Learning Systems: Move from scheduled training to on-demand, AI-supported skill development → Build Experimentation Cultures: Establish safe-to-fail environments that match their virtual lab experiences → Adopt Micro-Learning Architectures: Break complex skills into digestible, immediately applicable modules Gen Alpha and Gen Z aren't just digitally native - they're AI-learning native. The companies that adapt to their learning DNA will capture the best talent. Those that don't will struggle with engagement, retention, and innovation. At PeopleAtom, we're building the future of workforce development where AI meets human potential. If you're a CXO or People Leader ready to transform how your organization learns and grows, join our waitlist to be part of this revolution. Love and generational bridges, Joe #FutureOfWork #GenAlpha #AILearning #WorkforceTransformation #PeopleStrategy

Becoming a Lifelong Learning Experience Designer: How Generative AI Will Transform Business Education We are entering a pivotal era in which generative AI doesn’t simply enhance education—it reimagines it. Business schools have the opportunity and the responsibility to evolve into dynamic, personalized, and globally accessible learning ecosystems. This transformation calls for a new kind of educator: not only a subject-matter expert but also a Lifelong Learning Experience Designer—someone who shapes learning journeys, collaborates with AI engineers, and safeguards human values in digital pedagogy. Here’s my vision of what’s coming: 1. Hyper-personalized and simultaneously multilingual education Every learner will engage with an AI tutor capable of fluent, real-time dialogue in English, Japanese, Mandarin, Spanish, and more. Class discussions will become multilingual by design, removing language barriers and expanding access to top-tier education globally. 2. AI-curated, lifelong learning journeys Generative AI will serve as a lifelong academic advisor—monitoring learners’ competencies, interests, and career paths to suggest just-in-time modules, simulations, and certifications. Business education will shift from a fixed curriculum to an evolving, intelligent learning ecosystem. 3. Co-creation of knowledge with learners Education will transition from content delivery to content co-creation. Faculty and students will partner with AI to generate new frameworks, business cases, and research—making learning more agile, relevant, and responsive to emerging global challenges. 4. AI-augmented learning communities and synthetic peers Hybrid classrooms will include students and professors and AI-generated avatars simulating diverse sectors, cultural mindsets, and leadership styles. These synthetic peers will enhance dialogue, challenge assumptions, and promote inclusivity of thought. 5. Redefining the role of faculty—and enabling global scalability The educator’s role will expand from delivering content to becoming a designer of ethical, transformative, human-AI learning experiences. Faculty will: • Supervise and direct generative AI as co-creators of pedagogy, • Collaborate with AI engineers to design adaptive platforms, • Act as guardians of ethics, inclusion, and intellectual rigor. This approach will also allow business schools to scale their programs across geographies while maintaining cultural relevance and educational excellence. AI is not replacing educators—it’s amplifying their mission. It empowers us to reimagine education as a lifelong, personalized, multilingual, co-created journey. As educators, we are becoming architects of human-AI learning ecosystems, responsible for what students learn and how, with whom, and why. This is not a trend. It’s a transformation into a new civilization. GLOBIS University - Graduate School of Management | GLOBIS Corporation Esade | Esade Executive Education | Esade Alumni

Traditional learning feels like old-school GPS: • You follow a fixed path, whether it works for you or not. • Feedback is limited to basic right or wrong turns. • You rely on standard apps and platforms, with little room for personalization. Adaptive learning works like Waze: • It reroutes based on your unique learning needs and goals. • It fills knowledge gaps in real-time, optimizing your path as you go. • It offers efficient, tailored navigation for: 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀 (𝗗𝘂𝗼𝗹𝗶𝗻𝗴𝗼): • Adjusts lesson difficulty based on how you're doing. • Ideal for shorter attention spans, offering bite-sized, targeted practice. 𝗔𝗰𝗮𝗱𝗲𝗺𝗶𝗰 𝗦𝘂𝗯𝗷𝗲𝗰𝘁𝘀 (𝗞𝗵𝗮𝗻 𝗔𝗰𝗮𝗱𝗲𝗺𝘆): • Provides personalized practice and progress dashboards. • Adapts exercises to your skill level, filling gaps to ensure mastery. 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹 𝗦𝗸𝗶𝗹𝗹𝘀 (𝗥𝗮𝗽𝗟): • Adapts to your peak performance times with mobile-first microlearning. • Turns complex training into manageable, actionable steps. 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 (𝗖𝗼𝗱𝗲𝗰𝗮𝗱𝗲𝗺𝘆): • Offers interactive coding lessons with instant feedback and hints. • Adapts content to match your progress and guides you through new concepts.    𝗖𝘂𝘀𝘁𝗼𝗺𝗶𝘇𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (𝗖𝗵𝗮𝘁𝗚𝗣𝗧): • Acts like your AI co-pilot, reframing explanations until they click. • Adjusts its approach to match your comprehension style. Adaptive learning isn’t about forcing a fixed route. It’s about navigating a path that works for you. Video Description: A black screen with a yellow text box reading Part 5 - Adaptive Learning fades to a woman sitting in front of a brown woven screen.  Wearing a navy blazer and red blouse, she has medium-length brown hair and red glasses.

Building Agentic Graph Systems That Learn and Adapt to Each User 🛜 Graph-based systems represent a significant advancement in creating truly personalized and agentic AI systems by enabling sophisticated patterns of memory, recommendation, and contextual awareness to work together seamlessly. The integration of graph structures allows AI agents to maintain complex webs of relationships while actively learning and adapting to individual users' needs and preferences. First, graph structures provide a natural foundation for building memory systems that can evolve into sophisticated recommendation engines. The ability to traverse and weight relationships between entities enables systems to transform from passive storage into active agents that can anticipate needs and suggest relevant actions. This is particularly powerful because the graph structure captures not just individual pieces of information, but also their context, outcomes, and interrelationships. Second, graph-based systems excel at incorporating multi-dimensional pattern recognition. Unlike traditional recommendation systems that might focus on simple similarity metrics, graph structures can simultaneously process temporal patterns, contextual relationships, user behaviors, and outcome patterns. This multi-faceted analysis enables recommendations that are both more accurate and more nuanced than conventional approaches. Third, the adaptive learning capabilities of graph-based systems create a powerful feedback loop for personalization. When users respond to suggestions, their feedback modifies the weights of relevant connections in the graph. This creates a self-improving system where successful patterns naturally strengthen while less helpful ones fade. The adaptation works at both individual and aggregate levels, enabling systems to balance personalized learning with broader pattern recognition. Fourth, graph structures provide elegant solutions to common challenges in personalization systems, particularly the cold start problem. Even with limited initial information about a new user, the system can leverage indirect relationships and partial matches to make meaningful recommendations. As more interactions occur, these initial connections rapidly refine through feedback and pattern matching. Fifth, graph-based systems offer sophisticated privacy controls while maintaining high levels of personalization. This architectural approach enables highly personalized experiences while maintaining appropriate privacy protections. The integration of these capabilities has profound implications for AI system design. The graph structure serves as a unified framework where memory, learning, and recommendation capabilities can seamlessly interact. This enables increasingly sophisticated agents that can not only store and retrieve information but actively predict and suggest relevant knowledge and actions based on deep contextual understanding.

The one-size-fits-all model just doesn’t work in education, and I admit that most EdTech companies (including us at Airtribe) are not solving this problem. In my experience, true learning is driven by curiosity, and that curiosity varies from person to person. The learning path isn’t linear for everyone. Some prefer diving deep like a DFS, exploring every detail in-depth, while others prefer a broad overview like a BFS, covering multiple concepts quickly to get the bigger picture. At Airtribe, while we offer extensive knowledge transfer through live sessions, we realized this is super useful but isn’t the most effective approach for every learner because everyone has a different starting point. So, we started exploring how to make learning more personalized, and Generative AI emerged as the perfect solution. Over the past few months, we’ve developed features to enhance the learning experience. One of the major additions is interactive reading components — a blend of text, code, videos, and quizzes designed to create a more engaging learning environment. But the 10x improvement is our new AI-driven nudges. These nudges prompt learners to explore more about a topic in a way that suits their learning style. If you’re curious, the AI will guide you to dive deeper and learn in a way that feels natural to you. We’re currently testing this with a small cohort, and the results are looking great. It's still early, but I believe this will significantly improve the way people learn on our platform. — Here’s an example of how someone (like me who prefers more examples) can learn about North Star Metrics while going through the reading content. 👇🏻

Two recent studies, one from OpenAI's analysis of 2.5 billion daily ChatGPT messages and the other from Google's controlled trial of AI-augmented textbooks, provide converging evidence of a fundamental shift in how people learn. ChatGPT, with 700 million weekly users, sees 10% of all messages dedicated to tutoring, predominantly from users aged 18-25. Surprisingly, students primarily use AI to deepen understanding rather than complete tasks: 49% of interactions seek explanations and comprehension, not ready-made answers. This organic adoption shows students creating personalized learning experiences that traditional one-size-fits-all textbooks cannot provide. Google's Learn Your Way validates this approach experimentally. By personalizing textbook content to student interests and reading levels, explaining physics through basketball or economics through music, the system improved test scores by 13 percentage points. Both studies show AI transforms passive reading into active engagement through questions, multiple content representations, and immediate feedback. The gender gap in usage has closed, and adoption is accelerating in lower-income countries, though educated professionals still dominate work-related usage. The convergence is becoming more clear: millions of students aren't waiting for institutions to provide AI learning tools, they're already using GenAI as a personalized tutor. The data suggests GenAI works best as a learning companion that enhances understanding rather than replacing formal education. As we move forward, the question isn't whether AI will transform education, that transformation is already underway, driven by millions of students who have discovered that AI can provide something traditional educational materials cannot: personalized, patient, always-available support for learning. The question is how educational institutions, policymakers, and technology developers will respond to and shape this transformation to ensure it enhances rather than undermines human learning and development. https://lnkd.in/gpAxJrfF