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Have you ever tried quickly looking something up on Wikipediajust because youre curious or maybe for workonly to, a half an hour later, wonder why youre reading about the history of the European Space Agency? In my opinion, Wikipedia is one of the last good websites on the internet. Outside of the occasional fundraiser, there are no ads, no dark patterns, and no clickbaitits just information. Which leaves no doubt in my mind that falling into a Wikipedia rabbit hole is healthier than scrolling on social media. Even so, it can be addictive, and links are the reason why. Every Wikipedia article is jam packed with links to other Wikipedia articles, which is exactly why you end up down rabbit holes. Often, though, you dont understand how you wound up where you didso what if you could visualize exactly that? This tip originally appeared in the free Cool Tools newsletter from The Intelligence. Get the next issue in your inbox and get ready to discover all sorts of awesome tech treasures! Your new digital cork board To create a visualization of how you got from point A to point B on Wikipedia, head to Wikiboard. Wikiboard creates a mind mapwhich allows you to see how various concepts are connectedas you browse Wikipedia. You can start browsing immediately. To get started, open Wikiboard and enter your search termit will pull up the corresponding Wikipedia page. The first article you select opens in its own box on Wikiboard, and as you browse, the site creates new boxes for every link you click, drawing lines from one article to another as they open. Once your board is created, you can scroll and zoom as much as you like. You can also rearrange the boxes and add sticky notes, allowing you to organize and add a bit of context to everything as you browse. ~wikiboard.pngThis isn’t your father’s Wikipedia.~ One note: Wikiboard is currently only for larger screens, so you wont be able to use it on your phone. This could be a useful research tool, enabling you to see how concepts relate to each other as youre learning. You can even save separate boards in your browser so you to come back to them later. And while I could spin this as purely a research tool, its also just plain fun. Theres something amazing about visualizing your Wikipedia rabbit holes. Next time you feel like going on a deep dive, give Wikiboard a go. Going back and seeing the steps you took on your Wikipedia rabbit hole is endless entertaining, and can teach you a lot about your interests as well. You can open Wikiboard in your browser on any desktop-sized device. Wikiboard is free to use. You can opt to make a donation to support the developer if you like, but its not required. You dont need to create an account to use Wikiboard, and the site has no ads. Treat yourself to all sorts of brain-boosting goodies like this with the free Cool Tools newsletterstarting with an instant introduction to an incredible audio app thatll tune up your days in truly delightful ways.
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E-Commerce
In the Star Trek universe, the audience occasionally gets a glimpse inside schools on the planet Vulcan. Young children stand alone in pods surrounded by 360-degree digital screens. Adults wander among the pods but do not talk to the students. Instead, each child interacts only with a sophisticated artificial intelligence, which peppers them with questions about everything from mathematics to philosophy. This is not the reality in todays classrooms on Earth. For many technology leaders building modern AI, however, a vision of AI-driven personalized learning holds considerable appeal. Outspoken venture capitalist Marc Andreessen, for example, imagines that the AI tutor will be by each childs side every step of their development. Years ago, I studied computer science and interned in Silicon Valley. Later, as a public school teacher, I was often the first to bring technology into my classroom. I was dazzled by the promise of a digital future in education. Now, as a social scientist who studies how people learn, I believe K-12 schools need to question predominant visions of AI for education. Individualized learning has its place. But decades of educational research are also clear that learning is a social endeavor at its core. Classrooms that privilege personalized AI chatbots overlook that fact. School districts under pressure Generative AI is coming to K-12 classrooms. Some of the largest school districts in the country, such as Houston and Miami, have signed expensive contracts to bring AI to thousands of students. Amid declining enrollment, perhaps AI offers a way for districts to both cut costs and seem cutting edge. Pressure is also coming from both industry and the federal government. Tech companies have spent billions of dollars building generative AI and see a potential market in public schools. Republican and Democratic administrations have been enthusiastic about AIs potential for education. Decades ago, educators promoted the benefits of One Laptop per Child. Today, it seems we may be on the cusp of one chatbot per child. What does educational research tell us about what this model could mean for childrens learning and well-being? Learning is a social process During much of the 20th century, learning was understood mainly as a matter of individual cognition. In contrast, the latest science on learning paints a more multidimensional picture. Scientists now understand that seemingly individual processessuch as building new knowledgeare actually deeply rooted in social interactions with the world around us. Neuroscience research has shown that even from a young age, peoples social relationships influence which of our genes turn on and off. This matters because gene expression affects how our brains develop and our capacity to learn. In classrooms, this suggests that opportunities for social interactionfor instance, children listening to their classmates ideas and haggling over what is true and whycan support brain health and academic learning. Research in the social sciences has long since proved the value of high-quality classroom discourse. For example, in a well-cited 1991 study involving over 1,000 middle school students across more than 50 English classrooms, researchers Martin Nystrand and Adam Gamoran found that children performed significantly better in classes exhibiting more uptake, more authenticity of questions, more contiguity of reading, and more discussion time. In short, research tells us that rich learning happens when students have opportunities to interact with other people in meaningful ways. AI in classrooms lacks research evidence What does all of this mean for AI in education? Introducing any new technology into a classroom, especially one as alien as generative AI, is a major change. It seems reasonable that high-stakes decisions should be based on solid research evidence. But theres one problem: The studies that school leaders need just arent there yet. No one really knows how generative AI in K-12 classrooms will affect childrens learning and social development. Current research on generative AIs impact on student learning is limited, inconclusive, and tends to focus on older studentsnot K-12 children. Studies of AI use thus far have tended to focus on either learning outcomes or individual cognitive activity. Although standardized test scores and critical thinking skills matter, they represent a small piece of the educational experience. It is also important to understand generative AIs real-life impact on students. For example: How does it feel to learn from a chatbot, day after day? What is the longer-term impact on childrens mental health? How does AI use affect childrens relationships with each other and with their teachers? What kinds of relationships might children form with the chatbots themselves? What will AI mean for educational inequities related to social forces such as race and disability? More broadly, I think now is the time to ask: What is the purpose of K-12 education? What do we, as a society, actually want children to learn? Of course, every child should learn how to write essays and do basic arithmetic. But beyond academic outcomes, I believe schools can also teach students how to become thoughtful citizens in their communities. To prepare young people to grapple with complex societal issues, the National Academy of Education has called for classrooms where students learn to engage in civic discourse across subject areas. That kind of learning happens best through messy discussions with people who dont think alike. To be clear, not everything in a classroom needs to involve discussions among classmates. And research does indicate that individualized instruction can also enhance social forms of learning. So I dont want to rule out the possibility that classroom-based generative AI might augment learning or the quality of students social interactions. However, the tech industrys deep investments in individualized forms of AIas well as the disappointing history of technology in classroomsshould give schools pause. Good teaching blends social and individual processes. My concern about personalized AI tutors is how they might crowd out already infrequent opportunities for social interaction, further isolating children in classrooms. Center childrens learning and development Education is a relational enterprise. Technology may play a role, but as students spend more and more class time on laptops and tablets, I dont think screens should displace the human-to-human interactions at the heart of education. I see the beneficial application of any new technology in the classroomAI or otherwiseas a way to build upon the social fabric of human learning. At its best, it facilitates, rather than impedes, childrens development as people. As schools consider how and whether to use generative AI, the years of research on how children learn offer a way to move forward. Niral Shah is an associate professor of learning sciences & human development at the University of Washington. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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E-Commerce
In todays corporate landscape, optics often precede outcomes, especially in technology-led transformations. Announcements of new platforms, AI-powered strategies, or digital-first pledges frequently come long before the underlying infrastructure to support them. That was Teds reality as the chief growth officer at a global bank when his CEO unveiled a high-profile AI-Powered Growth Strategy positioned as a bold leap forward. The announcement made headlines and thrilled investors, but behind the scenes, the organization wasnt prepared. Ted was given a skeletal team of two direct reports, a patchwork of third-party tools, and the mandate to partner with five global banking divisions serving more than 500 employees. He was expected to turn the AI vision into reality with little structural support. This tension is commonand survivable. Leaders who maintain credibility dont scrap such pledges or decry them. Instead, they manage the gap between promise and proof. A well-intentioned CEO may launch an initiative to signal innovation, but when systems or skills lag, ambition can outpace execution. WeJenny, as an executive adviser and learning & development expert, and Kathryn, as an executive coach and keynote speakerhave identified five strategies to help executive teams navigate these moments with integrity and strategic foresight, especially when the initiative is more symbolic than substantive in its early stages. 1. Balance bold aspiration with candid honesty In the early stages of transformation, perception often outpaces progress. Stakeholders want visible proof that change is real. McKinsey found that 70% of digital transformations fail to meet their intended outcomes because senior executives either overpromise or disengage when early wins dont materialize. Those charged with execution must balance bold aspiration with candid honesty, communicating both the vision (Heres where were heading) and gap (Heres what it will take to get there) to maintain trust and momentum. Behind the scenes, Ted allocated 20% of the budget to data cleanup and capability-building, unseen but essential work such as strengthening data quality and governance, building the pipelines and quality controls that support mission-critical AI, and elevating the organizations baseline AI literacy. Within a year, three pilots validated the transformation narrative and quieted early skeptics. Edelmans Trust Barometer shows that stakeholders extend grace when leaders communicate with clarity and consistency, not performative certainty. Credibility, not charisma, sustains momentum through uncertainty. Try this: Balance vision with transparency. Use confident yet realistic language, such as Were learning in real time or This is a multi-year capability build. 2. Map Whats Performative vs. Whats Possible Not every element of a high-visibility initiative will yield immediate results. The key is distinguishing symbolic actions that signal intent from those that build lasting capability. Theresa, chief digital officer at a consumer goods firm, launched a public digital transformation week with town halls and press coverage. She brought in her AI agency partners and major retail customers to show alignment and signal momentum, partnership, and focus. The event created attention, but she knew the real work would happen out of sight. She used a short-horizon/long-horizon approach. The short horizon created urgency and rallied stakeholders, while the longer horizon anchored on execution. She reassigned 30% of her team to integrate legacy systems, clean priority datasets, and run joint sprints with her AI partners. That groundwork created a technical foundation strong enough to support advanced modeling. Within nine months, they delivered a demand-forecasting model that reduced inventory outages by 18%, transforming a performative launch into measurable operational value. When mapping an initiative, clarify two horizons: Short horizon (06 months): What signals matter? (e.g., visible executive sponsorship, internal messaging, external storytelling) Mid / long horizon (624+ months): What structural enablers must be built? (e.g., data platforms, technology partnerships, governance, skills) Visibility matters, but only when its paired with substance. Try this: Separate the symbolic from the structural. Create a two-horizon map to test balance: Which actions build momentum? and Which build capability? Then ensure both are visible. 3. Leverage Visibility as Currency When a high-profile initiative captures attention, use that spotlight to build political capital and secure future resources. Leaders who link early symbolic wins to longer-term learning sustain engagement and trust. Julie, a chief marketing officer we advised, leveraged her companys Digital Reinvention campaign to secure additional funding for employee upskilling, positioning it as the bridge between aspiration and execution. Try this: Treat visibility not as validation, but as leverage. Ask, What can this attention buy us: credibility, talent, or momentum? That perspective turns optics from vanity to value. 4. Build Small Wins that Prove Real Value Symbolic gestures lose power without substance. Once the spotlight fades, stakeholders want proof. Anchor your narrative in small, visible wins: projects, pilots, or behaviors that validate early promises. Start with pilots that address real pain points: automate a reporting process, improve data access for a critical team, or integrate AI into a single workflow. For Ted, that meant delivering credible proof pointsan AI-powered lead scoring model that lifted conversion rates by 12%, a unified customer insights dashboard, and a monthly What Were Learning series to build internal momentum. Small, visible progress converts skepticism into trust and gradually shifts perception from Its all optics to Its starting to work. Try this: Start small, but make progress visible. Choose one pilot that solves a visible pain point within 90 days. Publicize lessons learned, not just the result, to show that momentum is real, even if imperfect. 5. Reframe the Narrative: From Optics to Opportunity The best leaders dont deny the optics, they reframe them as stepping stones to a larger transformation. Gary, a nonprofit CEO we coached, introduced his first AI pilot as symbolic but necessary. It wasnt yet transformative, but it sparked a mindset shift: leaders began talking about data ethics, digital fluency, and decision-making transparency. As he put it, The project wasnt about the tool. It was about changing how we think. Reframing is essential. Deloitte and BCG both show that real value emerges when strategy, technology, and human systems align. Symbolic gestures only matter if they lead to lasting capability and behavior change. When leaders treat optics as openings rather than distractions, they turn visibility into belief. Stakeholders who see learning, transparency, and follow-through extend trust, and grant the runway needed for real transformation. Try this: Name the signal and the shift. Say, This initiative signals where were headed. Then ask, What new conversations or capabilities did this open up? In complex transformations, optics are not the enemy. Theyre a catalyst for belief. What matters is how leaders use those moments to align teams, secure investment, and guide the narrative from promise to proof. Integrity isnt about rejecting optics; its about ensuring they serve a larger purpose. The most effective leaders turn visibility into accountability and symbolic beginnings into lasting systems.
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E-Commerce
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