Continuous Learning Practices

People are suffering—yet many still deny that hours with ChatGPT reshape how we focus, create and critique. A new MIT study, “Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay-Writing,” offers clear neurological evidence that the denial is misplaced. Read the study (lengthy but far more enjoyable than a conventional manuscript, with a dedicated TL;DR and a summarizing table for the LLM): https://lnkd.in/g6PBVwVe 🧠 What the researchers did - Fifty-four students wrote SAT-style essays across four sessions while high-density EEG tracked information flow among 32 brain regions. - Three tools were compared: no aid (“Brain-only”), Google search, and GPT-4o. - In Session 4 the groups were flipped: students who had written unaided now rewrote with GPT (Brain→LLM), while habitual GPT users had to write solo (LLM→Brain). ⚡ Key findings - Creativity offloaded, networks dimmed. Pure GPT use produced the weakest fronto-parietal and temporal connectivity of all conditions, signalling lighter executive control and shallower semantic processing. - Order matters. When students first wrestled with ideas on their own and then revised with GPT, brain-wide connectivity surged and exceeded every earlier GPT session. Conversely, writers who began with GPT and later worked without it showed the lowest coordination and leaned on GPT-favoured vocabulary, making their essays linguistically bland despite high grades. - Memory and ownership collapse. In their very first GPT session, none of the AI-assisted writers could quote a sentence they had just penned, whereas almost every solo writer could; the deficit persisted even after practice. - Cognitive debt accumulates. Repeated GPT use narrowed topic exploration and diversity; when AI crutches were removed, writers struggled to recover the breadth and depth of earlier human-only work. 🌱 So what? The study frames this tradeoff as cognitive debt: convenience today taxes our ability to learn, remember, and think later. Critically, the order of tool use matters. Starting with one’s ideas and then layering AI support can keep neural circuits firing on all cylinders, while starting with AI may stunt the networks that make creativity and critical reasoning uniquely human. 🤔 Where does that leave creativity? If AI drafts faster than we can think, our value shifts from typing first passes to deciding which ideas matter, why they matter, and when to switch the autopilot off. Hybrid routines—alternate tools-free phases with AI phases—may give us the best of both worlds: speed without surrendering cognitive agency. Further reading: Lively discussion (debate) between neuroethicist Nita Farahany and CEO of The Atlantic, Nicholas Thompson, “The Most Interesting Thing in AI” podcast. The big (and maybe the final) question for us is: What is humanity when AI takes over all the creative processes? Podcast link: https://lnkd.in/emeQkcK6

Most careers stall for 1 reason: People stop learning. They wait for the company to invest in them. Or for their manager to set up training. High performers, on the other hand, don't wait. They treat learning as part of the job - Even when the workday ends. Not endless study, Just small, repeatable habits - that compound. Here are 11 that make lifelong learning automatic: 1. Keep a "Questions" Note on Your Phone ↳Anytime you wonder about something, jot it down. Research one nightly 2. Replace the Doomscroll ↳Replace 30 minutes of dead scroll time with a course or podcast 3. Teach What You Learn ↳Write a short post, Loom, or explain it to a peer 4. Reverse Engineer Great Work ↳Take an article, pitch, or deck you admire and break down why it works 5. Shadow Someone 2 Steps Ahead ↳Don't ask for mentorship - just observe 6. Then, DO Ask for Mentorship ↳Say: "I admire how well you do X - would you mind coaching me on that?" 7. Run Tiny Experiments ↳Pick one skill and test it live this week 8. Force Repetitions by Tracking ↳For writing, word count. For sales, calls made. Progress is fuel 9. Do "Learning Sprints" ↳One focused topic for 30 days, then switch 10. Revisit Old Material ↳The second read often hits deeper than the first 11. End Your Day with Reflection ↳One line: "What did I learn today?" The compounding effect is real. Small reps + every day = Mastery. Agree? --- ♻️ Share this to inspire other life-long learners. And follow me George Stern for more personal growth content.

Innovation is the lifeblood of progress, but it doesn’t happen by chance. It’s cultivated in environments where team members feel safe to share ideas and challenge the status quo. Creating a culture of innovation means nurturing an environment where bold ideas can flourish. It’s about openness, diverse perspectives, and the freedom to experiment. When people feel empowered to speak up, creativity thrives, and true innovation follows. So, how do you create such a culture? 1️⃣ Embed a Growth Mindset: Encourage continuous learning and development across all levels of the organization. Provide resources for professional growth and celebrate learning milestones, fostering an environment where knowledge and skills are constantly evolving. 2️⃣ Facilitate Cross-Functional Collaboration: Break down silos and encourage teams from different departments to work together. Cross-functional projects can bring fresh perspectives and spur innovative solutions that wouldn’t emerge in isolation. 3️⃣ Implement Structured Feedback Mechanisms: Establish regular feedback processes focused on constructive criticism and actionable insights. Ensure psychological safety so team members feel secure, viewing feedback as an opportunity for growth rather than critique. 4️⃣ Encourage Calculated Risks: Promote a culture where calculated risks are welcomed. Empower your team to explore new ideas and approaches without fear of failure. Recognize and reward innovative efforts, even when they don’t result in immediate success. By embedding these principles into your organizational culture, you can pave the way for continuous growth and success. Let’s create spaces where innovation is not just an aspiration but a tangible reality. #Leadership #Innovation #FutureOfWork

Cognitive Surrender is how engineers quietly accumulate comprehension debt My latest free deep-dive: https://lnkd.in/gzwBNDXh ✍ When we use AI coding agents and orchestrate, the line between delegating and surrendering moves under our feet daily. Based on recent research, the distinction is critical for anyone shipping code: - Cognitive Offloading: You hand off the how and keep the what. You judge whether the result is sensible and intervene when it isn't. - Cognitive Surrender: You stop constructing the answer entirely. The AI's output becomes "your" output, and you inherit its confidence without doing the underlying reasoning. In software engineering, surrender is the mechanism by which comprehension debt accumulates. Every 600-line PR we casually approve, or every complex stack trace we let the agent fix without understanding the root cause—these are tiny, compounding loans. The codebase grows, but our mental model of the system shrinks. Surface correctness is not systemic correctness. To resist surrender, we have to build friction and calibration into our workflows. Here are a few heuristics I use: 1. Construct an expectation first: Before running the agent, decide what the answer should roughly look like. If it doesn't match, you have a real choice to make. 2. Read the diff like a junior wrote it: "Seems right" is not a code review. The job hasn't changed but the author has. 3. Ask the model to argue against itself: This breaks the borrowed-confidence effect and forces you to evaluate the tradeoffs. 4. Solo time at the keyboard: Write code without the agent weekly. It's the ultimate calibration exercise to ensure offloading hasn't become surrender. The goal isn't to stop using AI tools - I use them every day to ship faster. The goal is mutual amplification, where the agent acts as the second engineer in the room, not the only one. #ai #programming #softwareengineering

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

Most L&D professionals learned the Kirkpatrick Model early on. Fewer have seen it applied beyond Level 1. Here's what each level can actually look like when you put it into practice, not just the textbook definition. ✨ Level 1: Reaction 🔹 Textbook version: Did learners find the training engaging and worth their time? ✅ In practice: Instead of "Did you enjoy this session?", ask "Was this relevant to the work you do?" and "Could you apply this right away?" ✅ Metric to track: Relevance and applicability ratings, not just satisfaction scores. ✨ Level 2: Learning 🔹 Textbook version: Did learners gain the intended knowledge or skills? ✅ In practice: Replace recall-based quizzes with scenario-based checks. Can the learner apply the concept to a situation they'd actually face? ✅ Metric to track: Pre/post assessment scores on scenario-based questions, not just "did you pass the quiz." ✨ Level 3: Behavior 🔹 Textbook version: Are learners applying what they learned on the job? ✅ In practice: 30/60/90-day check-ins, manager observations, or peer feedback on whether the new behavior is showing up in real work. ✅ Metric to track: % of participants demonstrating the target behavior, based on manager or peer input, not self-reported confidence. ✨ Level 4: Results 🔹 Textbook version: Did the training impact business outcomes? ✅ In practice: Pick one business metric the program was meant to influence, before you build it, not after, and track the change. ✅ Metric to track: Movement in that specific KPI (error rates, time-to-productivity, conversion rates, retention) compared to a baseline. Most programs are measured thoroughly at Level 1 and barely at all beyond it. But Levels 3 and 4 are where the "did this actually matter" conversation happens, and they are also where L&D earns a seat at the table. Which level does your organisation measure consistently, and which one do you wish you could measure better? #LearningAndDevelopment #LnD #KirkpatrickModel #TrainingEvaluation #InstructionalDesign #LearningMeasurement #TrainingAndDevelopment #LnDStrategy

More mistakes = more lessons The more mistakes you make, the more lessons you gain. 

It's important not to rely solely on theory or focus only on your successes. 

Embrace your failures and view them as valuable learning opportunities. 

Each mistake provides practical insights that theory alone cannot offer and helps you grow both personally and professionally. 

By analysing and understanding your missteps, you can develop better strategies, avoid repeating the same errors, and ultimately achieve greater success. 

Expanding your focus beyond successes ensures a well-rounded approach to growth and continuous improvement. 1. Reflect on Mistakes: - Daily or Weekly Reviews: Set aside time to review your actions and decisions regularly. Reflect on what went wrong and why. - Journaling: Keep a journal of your mistakes and the lessons learned from each one.
 2. Seek Feedback: - Peer Reviews: Ask colleagues or mentors for constructive feedback on your work and decisions. - Open Communication: Create an environment where team members feel comfortable discussing their mistakes and what they learned.
 3. Analyse and Document: - Root Cause Analysis: Use techniques like the "5 Whys" or fishbone diagrams to understand the root causes of mistakes. - Documentation: Document mistakes and their causes in a shared repository for future reference and team learning.
 4. Create a Learning Culture: - Celebrate Learning: Recognise and reward employees who identify mistakes and share their lessons. - Encourage Experimentation: Foster an environment where calculated risks and experimentation are encouraged, even if they result in

Be someone who never stops learning. 7 ways to stay relevant in your career: It's humbling to start over, to learn new things, to suck at something again (I did a lot of that this year). Do it anyway. Your career depends on thinking like a beginner. Here's how to make learning your competitive advantage: 1. Block time for learning → Schedule 30 minutes every morning → Treat it like a client meeting → Never let urgent overtake important Make learning a routine you can stick to. 2. Learn from people outside your niche → Follow experts from different industries → Read books that "aren't for you" → Have coffee with people who think differently The fastest way to learn, is to surround yourself with new ideas. 3. Ask the dumb questions → Seriously, it's fine. → Say "I don't understand" without shame → As a manager, I love these questions. The smartest people ask the most questions. 4. Build learning systems not goals → Create a learning roadmap → Track what you learn, not just consume → Share your learning to deepen your knowledge Systems beat motivation every time. 5. Focus on skills that transfer → Don't learn "ChatGPT". Learn LLMs. → Principles are timeless → Learn to learn Tactics change. Principles don't. 6. Make failure your teacher → Reflect on what isn't working → Be willing to experiment → Try again with new knowledge One of my favorite phrases is: "learning experiments". 7. Stay professionally paranoid → Assume your job will change → Don't get complacement → Don't allow ego to hold you back Ego keeps you stuck. Humility sets you free. PS: What do you want to learn more about this year? I'd love to know👇 --- Enjoy this? ♻️ Repost it to your network and follow Owain Lewis for more

A friend sent me this graph today that at first made me laugh. It compared professional athletes to people in sales: 🟦 Athletes: Train. Perform. Train. Perform. Train. Perform, over and over. 🟥 Sales: Perform. Perform. Perform. Perform...oh, and that one training day. Then I realized, this isn't just a sales problem. It's a workplace problem. Think about it: • A surgeon spends years in residency before operating independently • An NFL quarterback watches hours of film for every hour on the field • A musician practices scales they've known for decades Yet in most organizations, across every function, every role, every level, we hire people, onboard them in a few weeks, and then just...expect peak performance. Indefinitely. With minimal ongoing investment in their growth. We wouldn't ask an athlete to compete without training. Why do we ask our people to? Continuous learning isn't a perk. It isn't a line item to cut when budgets get tight. It's the foundation of sustainable performance, for individuals and for organizations. The best companies I've seen, or read about, treat development the same way great coaches treat practice: structured, intentional, and never optional. What would it look like if our teams trained like professionals? #Learning #ProfessionalDevelopment #Leadership #Talent #GrowthMindset

Unpopular opinion: Microlearning is making your workforce dumber. There. I said it. And the neuroscience backs me up. For the past 5 years, L&D has been obsessed with bite-sized content. “Learners are busy!” “Attention spans are shrinking!” “Make it 3 minutes or less!” But here’s what we’re ignoring: The brain doesn’t learn through accumulation of facts. It learns through pattern recognition, contextual embedding, and effortful retrieval. When you fragment complex skills into 90-second modules, you’re triggering three cognitive failures: 1. The Illusion of Mastery Short bursts feel productive. You get that dopamine hit of completion. But fluency ≠ competence. Neuroscience shows that difficult, sustained engagement—not easy wins—creates durable learning. 2. Context Collapse The brain stores information in rich networks of association. When you strip away context to hit a 3-minute target, you’re removing the very scaffolding that makes knowledge transferable to real work situations. 3. No Desirable Difficulty Bjork’s research is clear: learning should be hard. Microlearning optimizes for convenience, not for the cognitive struggle that rewires neural pathways. The Real Problem: We’re confusing information access with capability building. Yes, give people microlearning for just-in-time answers—“How do I format this PowerPoint?” But stop pretending 47 three-minute videos will develop strategic thinking, leadership presence, or complex problem-solving. What Actually Works: ∙ Spaced repetition over weeks (not days) ∙ Interleaving different concepts to force discrimination ∙ Extended practice with feedback loops ∙ Cognitive struggle followed by consolidation Deloitte runs thousands of learning programs annually. The ones that measurably change behavior? They’re never the short ones. Maybe it’s time we stopped optimizing for completion rates and started designing for neural change. What’s your experience? Are we sacrificing deep learning on the altar of engagement metrics? *** The brain comparison imagery is a conceptual illustration, but the science behind it is solid. Decades of research on spacing effects (Cepeda et al.), desirable difficulties (Bjork), and neural consolidation (McClelland et al.) consistently show that sustained, effortful learning creates more robust neural changes than fragmented exposure.