Education

Louder for the people at the back 🎤 Many organisations today seem to have shifted from being institutions that develop great talent to those that primarily seek ready-made talent. This trend overlooks the immense value of individuals who, despite lacking experience, possess a great attitude, commitment, and a team-oriented mindset. These qualities often outweigh the drawbacks of hiring experienced individuals with a fixed and toxic mindset. The best organisations attract talent with their best years ahead of them, focusing on potential rather than past achievements. Let’s be clear this is more about mindset and willingness to learn and unlearn as apposed to age. To realise the incredible potential return, organisations must commit to creating an environment where continuous development is possible. This requires a multi-faceted approach: 1. Robust Training Programmes: Employers should invest in comprehensive training programmes that equip employees with the necessary skills for their roles. This includes on-the-job training, mentorship programmes, online courses, and workshops. 2. Redefining Hiring Criteria: Organisations should revise their hiring criteria to focus more on candidates’ potential and willingness to learn rather than solely on prior experience or formal qualifications. Behavioural interviews, aptitude tests, and probationary periods can help assess a candidate's ability to learn and adapt. 3. Partnerships with Educational Institutions: Companies can collaborate with educational institutions to design curricula that align with industry needs. Apprenticeship programmes, internships, and cooperative education can bridge the gap between academic learning and practical job skills. 4. Lifelong Learning Culture: Encouraging a culture of lifelong learning within organisations is crucial. Employers should provide ongoing education opportunities and support for professional development. This includes continuous skills assessment and access to resources for upskilling and reskilling. 5. Inclusive Recruitment Practices: Employers should implement inclusive recruitment practices that remove biases and barriers. Blind recruitment, diversity quotas, and targeted outreach programmes can help ensure that diverse candidates are given a fair chance. By implementing these measures, organisations can develop a workforce that is adaptable, innovative, and resilient, ensuring sustainable success and growth.

𝗔𝟮𝗔 (𝗔𝗴𝗲𝗻𝘁-𝘁𝗼-𝗔𝗴𝗲𝗻𝘁) 𝗮𝗻𝗱 𝗠𝗖𝗣 (𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹) are two emerging protocols designed to facilitate advanced AI agent systems, but they serve distinct roles and are often used together in modern agentic architectures. 𝗛𝗼𝘄 𝗧𝗵𝗲𝘆 𝗪𝗼𝗿𝗸 𝗧𝗼𝗴𝗲𝘁𝗵𝗲𝗿 Rather than being competitors, 𝗔𝟮𝗔 𝗮𝗻𝗱 𝗠𝗖𝗣 𝗮𝗿𝗲 𝗰𝗼𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝗿𝘆 𝗽𝗿𝗼𝘁𝗼𝗰𝗼𝗹𝘀 that address different layers of the agent ecosystem: • 𝗔𝟮𝗔 is about agents collaborating, delegating tasks, and sharing results across a distributed network. For example, an orchestrating agent might delegate subtasks to specialized agents (analytics, HR, finance) via A2A25. • 𝗠𝗖𝗣 is about giving an agent (often an LLM) structured access to external tools and data. Within an agent, MCP is used to invoke functions, fetch documents, or perform computations as needed.    𝗧𝘆𝗽𝗶𝗰𝗮𝗹 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: • A user submits a complex request. • The orchestrating agent uses 𝗔𝟮𝗔 to delegate subtasks to other agents. • One of those agents uses 𝗠𝗖𝗣 internally to access tools or data. • Results are returned via A2A, enabling end-to-end collaboration25.    𝗗𝗶𝘀𝘁𝗶𝗻𝗰𝘁 𝗦𝘁𝗿𝗲𝗻𝗴𝘁𝗵𝘀 • 𝗔𝟮𝗔 𝗲𝘅𝗰𝗲𝗹𝘀 𝗮𝘁:   Multi-agent collaboration and orchestration   Handling complex, multi-domain workflows   Allowing independent scaling and updating of agents   Supporting long-running, asynchronous tasks54 • 𝗠𝗖𝗣 𝗲𝘅𝗰𝗲𝗹𝘀 𝗮𝘁:   Structured tool and data integration for LLMs   Standardizing access to diverse resources   Transparent, auditable execution steps   Single-agent scenarios needing a precise tool    𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗮𝗹 𝗔𝗻𝗮𝗹𝗼𝗴𝘆 • 𝗠𝗖𝗣 is like a 𝘶𝘯𝘪𝘷𝘦𝘳𝘴𝘢𝘭 𝘤𝘰𝘯𝘯𝘦𝘤𝘵𝘰𝘳 (USB-C port) between an agent and its tools/data. • 𝗔𝟮𝗔 is like a 𝘯𝘦𝘵𝘸𝘰𝘳𝘬 𝘤𝘢𝘣𝘭𝘦 connecting multiple agents, enabling them to form a collaborative team.    𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 • 𝗔𝟮𝗔 introduces many endpoints and requires robust authentication and authorization (OAuth2.0, API keys). • 𝗠𝗖𝗣 needs careful sandboxing of tool calls to prevent prompt injection or tool poisoning. Both are built with enterprise security in mind.    𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗔𝗱𝗼𝗽𝘁𝗶𝗼𝗻 • 𝗔𝟮𝗔: Google, Salesforce, SAP, LangChain, Atlassian, Cohere, and others are building A2A-enabled agents. • 𝗠𝗖𝗣: Anthropic (Claude Desktop), Zed, Cursor AI, and tool-based LLM UIs.   Modern agentic systems often combine both: 𝗔𝟮𝗔 𝗳𝗼𝗿 𝗶𝗻𝘁𝗲𝗿-𝗮𝗴𝗲𝗻𝘁 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻, 𝗠𝗖𝗣 𝗳𝗼𝗿 𝗶𝗻𝘁𝗿𝗮-𝗮𝗴𝗲𝗻𝘁 𝘁𝗼𝗼𝗹 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻. This layered approach supports scalable, composable, and secure AI applications.

500 students share one computer in Niger. Yet they're conducting advanced physics experiments that students at elite schools can't access. The secret? WebAR turning basic smartphones into portable STEM labs. Think about that. In Sub-Saharan Africa, fewer than 10% of schools have internet. Student-to-computer ratios hit 500:1. Yet mobile subscriptions jumped from single digits to 80% in a decade. Students already carry the infrastructure—we just weren't using it right. Traditional EdTech Reality: ↳ VR headsets: $300+ per student ↳ Heavy apps requiring 5G speeds ↳ Labs costing millions to build ↳ Rural schools: permanently excluded The WebAR Revolution: ↳ Runs in any browser, optimized for 3G ↳ No app store, minimal storage ↳ Science scores improving 10-15% ↳ Every smartphone becomes a laboratory But here's what grabbed me: A physics teacher in rural South Africa has one broken oscilloscope. No budget. Her students scan printed markers, and electromagnetic fields pulse across their desks. They run experiments infinitely—no equipment damaged, no reagents consumed. One student told her: "Engineering is for people like me now. The lab fits in my pocket." What changes everything: ↳ Mobile-first matches actual connectivity ↳ Browser-based works offline ↳ Teachers need training, not new buildings ↳ Inequality becomes irrelevant The Multiplication Effect: 1 teacher with markers = 30 students experimenting 10 schools sharing content = communities transformed 100 districts adopting = educational equality emerging At scale = STEM education without infrastructure gaps We spent decades waiting for labs that won't arrive. Now any browser becomes one. Because when a student in rural Africa explores the same 3D molecules as someone at MIT—using the phone already in their pocket—you realize: WebAR isn't shiny technology. It's a quiet equaliser making world-class STEM education fit into 3G connections and $50 phones. Follow me, Dr. Martha Boeckenfeld for innovations where accessibility drives transformation. ♻️ Share if you believe quality education shouldn't require perfect infrastructure.

Continuing from last week’s post on the rise of the Voice Stack, there’s an area that today’s voice-based systems often struggle with: Voice Activity Detection (VAD) and the turn-taking paradigm of communication. When communicating with a text-based chatbot, the turns are clear: You write something, then the bot does, then you do, and so on. The success of text-based chatbots with clear turn-taking has influenced the design of voice-based bots, most of which also use the turn-taking paradigm. A key part of building such a system is a VAD component to detect when the user is talking. This allows our software to take the parts of the audio stream in which the user is saying something and pass that to the model for the user’s turn. It also supports interruption in a limited way, whereby if a user insistently interrupts the AI system while it is talking, eventually the VAD system will realize the user is talking, shut off the AI’s output, and let the user take a turn. This works reasonably well in quiet environments. However, VAD systems today struggle with noisy environments, particularly when the background noise is from other human speech. For example, if you are in a noisy cafe speaking with a voice chatbot, VAD — which is usually trained to detect human speech — tends to be inaccurate at figuring out when you, or someone else, is talking. (In comparison, it works much better if you are in a noisy vehicle, since the background noise is more clearly not human speech.) It might think you are interrupting when it was merely someone in the background speaking, or fail to recognize that you’ve stopped talking. This is why today’s speech applications often struggle in noisy environments. Intriguingly, last year, Kyutai Labs published Moshi, a model that had many technical innovations. An important one was enabling persistent bi-direction audio streams from the user to Moshi and from Moshi to the user. If you and I were speaking in person or on the phone, we would constantly be streaming audio to each other (through the air or the phone system), and we’d use social cues to know when to listen and how to politely interrupt if one of us felt the need. Thus, the streams would not need to explicitly model turn-taking. Moshi works like this. It’s listening all the time, and it’s up to the model to decide when to stay silent and when to talk. This means an explicit VAD step is no longer necessary. Just as the architecture of text-only transformers has gone through many evolutions, voice models are going through a lot of architecture explorations. Given the importance of foundation models with voice-in and voice-out capabilities, many large companies right now are investing in developing better voice models. I’m confident we’ll see many more good voice models released this year. [Reached length limit; full text: https://lnkd.in/g9wGsPb2 ]

We are excited to announce the release of our "Guide to Integrating Generative AI for Deeper Literacy Learning" - a collaboration between AI for Education and Student Achievement Partners. We co-developed the guide with SAP, experts in high quality instruction, with an understanding that both the technology and its educational applications are at it's earliest stages. We also know that many teachers, leaders, and students are concerned about the impact the tools will have on learning. We want this guide to act as a jumping off point for educators that are trying to determine if GenAI can positively intersect with high quality instruction in the literacy classroom. The Key Principles of the Guide: •  GenAI tools should support, not circumvent, productive struggle for students •  AI literacy should come before the Integration of GenAI tools •  GenAI should augment educators’ pedagogical expertise, content knowledge, and knowledge of students •  Integration when appropriate should enhance, not replace, proven instructional practices •  Usage should align with students’ developmental readiness and literacy goals Highlights: • A framework for distinguishing productive vs. counterproductive struggle in literacy classrooms • Practical strategies for using AI to enhance student engagement without replacing critical thinking for students •  Best practices for enhancing cognitive lift and what strategies to avoid that offload cognitive lift • Detailed GenAI use cases across foundational skills, knowledge building, and writing instruction • Elementary-specific guidance emphasizing teacher-led AI implementation and modeling • Comprehensive worked examples with Chatbot transcripts that illustrate these practices This is just the beginning, which is why we're actively gathering educator feedback to refine and expand these resources through a survey in the guide. Thank you so much to Carey Swanson and Jasmine Costello, PMP from SAP for being such wonderful partners in this work! You can access the full guide or watch the accompanying webinar in the link in the comments! #ailiteracy #literacy #GenAI #K12

🧠 The Hidden Curriculum: Why Chores Build the Kind of Intelligence Schools Can’t Teach A Harvard study found that children who do daily chores are more likely to succeed later in life. That caught my attention — not because of what it says about children, but what it reveals about how success really forms. I’ve been thinking about this a lot. In my opinion, chores aren’t just a way to teach discipline — they’re a quiet form of systems thinking. I used to think success came from information. Now I think it comes from interaction — with effort, friction, and consequence. When a child folds clothes or washes dishes, they’re not just learning responsibility. They’re learning causality — that their actions can shape their environment. That’s the seed of agency, and in my view, the foundation of all learning. Here’s what this study really points to: → Chores connect effort to outcome. They teach the mind to see progress as earned, not given. → Screens disconnect action from effect. We react constantly, but change nothing. → Causality builds confidence. The more a child sees impact, the more they believe their effort matters. That’s not just psychology — that’s neurology. The brain builds reward loops around whatever it can control. When effort produces order, we feel grounded. When it doesn’t, we drift. Here’s how I think we can bring that loop back 👇 ✅ Reconnect effort with outcome. Let kids see what their actions change — even in small ways. ✅ Reward contribution, not compliance. Praise what they build, not what they finish. ✅ Normalize friction. The hard way isn’t punishment — it’s practice for the real world. In my view, chores don’t just prepare children for life — they simulate it. They teach the one rule that never changes: nothing improves until you act. 💭 So what do you think — are we overeducating kids in knowledge, but undertraining them in consequence? #Parenting #Education #ChildDevelopment #CognitiveGrowth #LifeSkills #FutureOfLearning

Major update on our work: In the last few years, a flood of new research has altered the landscape of the debate around kids, smartphones, and social media. 1️⃣ First, there is now a lot more work revealing a wide range of direct harms caused by social media that extends beyond mental health (e.g., cyberbullying, sextortion, and exposure to algorithmically amplified content promoting suicide, eating-disorders, and self-harm). These direct harms are not correlations; they are harms reported by millions of young people each year. 2️⃣ Second, recent research — including experiments conducted by Meta itself — provides increasingly strong causal evidence linking heavy social media use to depression, anxiety, and other internalizing disorders. (We refer to these as indirect harms because they appear over time rather than right away). Together, these findings allow us to answer the product safety question clearly: 📣 No, social media is not safe for children and adolescents. The evidence is abundant, varied, and damning. We have gathered it and organized it in two related projects which we invite you to read, in this post: https://lnkd.in/eAvfH3aQ

A learning culture is not built by offering more training. It emerges where curiosity, connection, and purpose intersect. Andrew Barry, in The Curious Lion, describes learning culture as a lotus where several forces overlap. I find this framing helpful because it moves the conversation beyond HR programs and into the fabric of the organization. At the individual level, there is curiosity. People must feel invited to ask questions, challenge assumptions, and explore. Without individual curiosity, learning remains compliance. At the organizational level, there is mission. Learning needs direction. When people understand what the company stands for and where it is going, their curiosity becomes focused rather than scattered. At the relational level, there is human connection. Learning accelerates in environments where people feel safe to speak, experiment, and reflect together. The fourth circle is continuous learning. Learning must be ongoing, not episodic. Not a workshop, but a way of operating. Continuous learning ensures that curiosity, mission, and connection reinforce each other over time rather than fading after the latest initiative. When these circles overlap, deeper elements emerge: Shared vision aligns effort. Shared experiences create collective memory. Shared assumptions shape how reality is interpreted. Shared stories transmit meaning across generations. At the center sits what we call learning culture. Not an initiative, but a pattern of how people think, relate, and evolve together. The question for leaders is not, “Do we offer learning opportunities?” It is, “Do curiosity, mission, and connection truly reinforce each other continuously in our organization?” That is where learning becomes cultural rather than occasional.

I graduated from the Wharton MBA a year ago, and here’s what I wish someone had told me before I started about recruiting, mindset, and social life. If you're starting this fall, this is for you! 1️⃣ Business school is a buffet (and you'll get indigestion if you try everything) First few weeks - take stock of everything it has to offer. Internships, fellowships, clubs, accelerators, etc. Then ruthlessly prioritize. Take what serves you and leave the buffet. (You’ll still feel FOMO and take on more than you should because you “paid for it” - that’s all part of the process!) 2️⃣ Pivoting is harder than you think Most people don’t get this until it’s too late - employers don’t recruit from MBA programs because they care that you have an MBA. They do it because MBAs filter for experiences/skillsets they want. You need to show them why you can do the job. The degree just gets you in the door - use the network to get the experience and build the skills. More people than you'd think go back to their old industries. 3️⃣ You'll have your "maybe I should recruit for consulting" moment Or banking. Even if you swore you'd never. We all do. When everyone's doing it, you'll second-guess yourself. That's normal! Just remember why YOU came to school. Stick to your plan, not theirs. (Unless your plan was to recruit for consulting/banking) 4️⃣ The social scene is middle school, except people have money (but it’s not all bleak) Hundreds of type-A personalities in one place and a lot of bankers/consultants who didn’t have enough fun in their 20s = drama, hyper-socialization. Who's dating who, who got the Goldman interview, who wasn't invited to that trip…People will talk about you if you stand out (for good or bad reasons). First semester feels intense, especially if you’re an introvert. By second year, everyone chills out and you find your people. 5️⃣ You'll need to touch grass B-school is a bubble. If you're not careful, you'll think comparing signing bonuses and taking out loans to go on another trip with 20 people you just met are real life. See non-MBA friends. Call your family. There’s a hive-mindedness in business school. Don’t lose yourself in it. 6️⃣ The classes are hit or miss (so be strategic) You’ll keep some class notes for decades to come (If you’re going to Wharton - Negotiations, Legal Aspects of Entrepreneurship, Scaling Operations to name a few). You’ll throw away others before the semester is over. Talk to second-years about which classes are actually worth optimizing to get into. Bonus: Do something unexpected. Join the club you think you'd hate. Take the class outside your comfort zone. I signed up for a week-long backpacking trip in the Andes despite every instinct not to and ended up leading a trip to Antarctica my 2nd year (best MBA memory!). People getting an MBA: What's the one thing you're nervous about as you start your MBA? People who have an MBA: What’s the piece of advice you wish you got before you started?

As Chairman of Student Freedom Initiative (SFI), I’ve seen how limited internet access continues to hold back too many students at our nation’s colleges and universities. This is especially true at our HBCUs — 82% of them reside in broadband deserts, which limits access to opportunity, education and economic mobility. That is why we’ve made closing the digital divide a central pillar of our work. In a recent interview with CNET, Keith S., SFI’s CEO and President and Yvette Thomas, Program Director of Institutional Transformation, spoke with Trisha Jandoc about our work to bring affordable, high-speed broadband to HBCUs and their students. We’re already seeing how real students are benefiting from this work. At Claflin University, students now have access to upgraded infrastructure that supports everything from online coursework to searching for their next job and internship opportunity. Having reliable access to the internet is critical to take part in today’s digital economy and this is so much bigger than any one institution. In addition to providing students with more opportunities, closing the digital divide at HBCUs will drive upwards of $500 billion in U.S. GDP growth. This work will take all of us, and I encourage you all to learn how you can work with SFI to bridge this gap. https://lnkd.in/ewnH_KaK