Critical Thinking Applications

I recently taught a graduate course on critical thinking, drawing primarily on two frameworks: Ennis (2015) and Paul & Elder (2014) (plus insights from Dewey, 1933). Yes, there are several frameworks to use in this regard but these two stand out. They’re practical, comprehensive, and widely cited in academic research. Why does critical thinking matter now more than ever? One word: AI. Anyone with an internet connection can now produce convincing content in seconds, no expertise, no effort. The result? A flood of misinformation, hallucinated facts, and polished nonsense. It’s what James Paul Gee once warned about: the rise of a culture of amateurism. With Web 2.0, that culture was emerging. With AI, it's becoming the norm. This is why I believe critical thinking is no longer optional. It must be explicitly taught across the curriculum. Students need to analyze, evaluate, and synthesize, not just consume. To support this, I’ve created the visual below, a guide grounded in two seminal frameworks. Use it. Share it. And explore the references to go deeper. We don’t need more content. We need sharper minds. References 1. Dewey, J. (1933). How We Think: A Restatement of the Relation of Reflective Thinking to the Educative Process. D.C. Heath and Company. 2. Ennis, R. H. (2015). Critical thinking: A streamlined conception. In M. Davies & R. Barnett (Eds.), The Palgrave handbook of critical thinking in higher education (pp. 31–47). Palgrave Macmillan. 3. Paul, R., & Elder, L. (2014). The miniature guide to critical thinking concepts and tools (8th ed.) Foundation for Critical Thinking. #CriticalThinking #AIandEducation #MedKharbach #EducatorsTechnology #HigherEd #TeachingWithAI

Yuri Bezmenov, a former KGB informant who defected to Canada, spent much of his life warning the world about the dangers of government propaganda and how it can distort truth in societies. His reflections offer a powerful insight into the mechanics of demoralization—how it strips away the capacity for individuals to perceive the truth, even when presented with irrefutable facts. According to Bezmenov, once a person is demoralized, they become immune to true information, no matter how much evidence is shown to them. This powerful observation sheds light on the pervasive influence of misinformation and the importance of critical thinking in a world filled with conflicting narratives. When people are subjected to manipulative propaganda, they begin to lose their ability to distinguish truth from falsehood, often even rejecting the truth when it is right in front of them. This is a dangerous path for any society, leading to a collective loss of discernment and understanding. Bezmenov’s warning is not just about external propaganda, but also about the internal process of demoralization, which can lead to a collective breakdown in society’s moral and ethical compass. It is a call to remain vigilant, to seek truth with discernment, and to educate ourselves and others to recognize when manipulation and misinformation are at play. In these times, where information flows freely but truth is often elusive, it is more important than ever to hold on to our ability to think critically and maintain a clear moral vision. Without this, societies risk becoming fragmented, with their values eroded and their future uncertain.

During my time as a Navy SEAL, precision and thorough analysis were not just practices but NECESSITIES! The "Five Whys" method exemplifies this approach outside the battlefield, presenting a clear path to problem-solving. Here's how it worked for the Lincoln Memorial's unexpected challenge: 1️⃣ Why is the memorial dirty?Because of bird droppings. 2️⃣ Why are there bird droppings?Birds are attracted to the area. 3️⃣ Why are birds attracted? They eat the spiders there. 4️⃣ Why are there spiders? Spiders eat the insects 5️⃣ Why are there insects? They're attracted to the lights left on at night. The solution? Adjust the lighting to reduce the insects to deter the spiders and birds, directly addressing the root of the cleanliness issue. This method isn't just for maintaining national monuments; it's a powerful tool for any leader or problem-solver in any field. The next time you're faced with a challenge, I urge you to employ the "Five Whys." Get deep. Understand the problem fully before jumping to solutions. By sharing this method, you're not just passing along a problem-solving tool; you're empowering others to think critically and act decisively. Be the one to inspire change, to lead by example.

AI and Critical Thinking: A False Dilemma or a Wake-Up Call? A recent Microsoft and Carnegie Mellon study has gone viral with headlines proclaiming that relying on AI kills critical thinking. The idea that AI could be making us less intellectually agile is an alarming one. But as always, the reality is more nuanced than the headline suggests. The study highlights that users who leaned on AI-generated outputs tended to produce "a less diverse set of outcomes for the same task" compared to those who did not use AI. This has been interpreted as a deterioration of critical thinking. The fear is that AI is shaping human decision-making in a way that reduces independent thought and creative problem-solving. AI is a tool, and like any tool, its impact depends on how we use it. If we blindly accept AI-generated results without questioning or refining them, then yes, our cognitive abilities may atrophy over time. But if we leverage AI as a collaborator—challenging, iterating, and improving upon its suggestions—then it can actually enhance our thinking, not replace it. The key factor here is education and training. If professionals and students are taught to critically assess AI-generated outputs rather than passively accept them, then AI becomes a force multiplier for intelligence, not a replacement for it. - AI as a Thought Partner, Not a Dictator: Diversity in Thinking Comes from Human-AI Collaboration. The Real Danger Lies in Over-Reliance Without Understanding - Diversity in Thinking Comes from Human-AI Collaboration: AI tends to optimize for efficiency, which can sometimes mean converging on common solutions. Humans, however, can inject divergent thinking, cultural insights, and out-of-the-box creativity to balance this tendency. - The Real Danger Lies in Over-Reliance Without Understanding: If AI is treated as a "black box" where results are blindly trusted, then critical thinking erodes. But if it is used as a brainstorming assistant, research tool, or an idea amplifier, then it enhances productivity without diminishing cognitive skills. Rather than debating whether AI kills critical thinking, we should focus on AI literacy. The ability to understand, question, and refine AI outputs will define the winners and losers in the age of automation. Companies, universities, and governments should invest in training professionals not just to use AI, but to think alongside it. The best leaders of tomorrow will be those who know when to trust AI, when to challenge it, and when to override it with human intuition and experience. AI doesn’t inherently make us less intelligent. It amplifies the thinking patterns we already have. If we train ourselves to use AI wisely, it can become a tool that sharpens our intellect rather than dulling it. The challenge is not AI itself—it’s how we integrate it into our workflows, decision-making, and education systems. AI is not the enemy of critical thinking; it is a test of it.

The gap between good decisions and great ones often comes down to the questions we ask ourselves. 31% reduced confirmation bias. 39% improved argument quality. 43% greater hypothesis flexibility. These aren't just statistics. They're evidence of how the right questions can completely reshape your thinking. We're not in an era where critical thinking is optional. We're in a time where it's the difference between leading and following. The most powerful questions aren't complicated. They're precisely targeted to counteract our cognitive blind spots. Here are five backed by research: 🔹 "What would make me wrong about this?" Counteracts confirmation bias by forcing you to seek disconfirming evidence. Journal of Business Research shows this simple question improved decision accuracy by 26%. 🔹 "What's the strongest case against my position?" Develops intellectual empathy by steelmanning opposing views. Stanford University studies found this practice increased persuasiveness by 27%. 🔹 "What information would change my conclusion entirely?" Prevents overconfidence in limited evidence. Princeton University research shows this question improved the incorporation of new evidence by 51%. 🔹 "Whose perspective am I not considering?" Reveals blind spots and prevents echo chamber thinking. MIT Sloan School of Management research found this improved solution quality by 28%. 🔹 "How would I think about this if it weren't my idea?" Creates psychological distance from your own ideas. Organizational Research showed this reduced unhelpful attachment by 47%. The world doesn't just need more information processors. It requires more nuanced thinkers who can navigate complexity with clarity and objectivity. That's the mindset we're helping build - for leaders who want to make decisions they won't regret tomorrow. Coaching can help; let's chat.  Follow Joshua Miller 𝗟𝗶𝗸𝗲 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗿𝗲𝗮𝗱 𝗯𝘂𝘁 𝘄𝗮𝗻𝘁 𝗺𝗼𝗿𝗲? 🚀 Download Your Free E-Book:  “𝟮𝟬 𝗦𝗺𝗮𝗹𝗹 𝗦𝗵𝗶𝗳𝘁𝘀 𝗧𝗵𝗮𝘁 𝗟𝗲𝗮𝗱 𝘁𝗼 𝗕𝗶𝗴 𝗟𝗶𝗳𝗲 𝗖𝗵𝗮𝗻𝗴𝗲𝘀” ↳ https://rb.gy/37y9vi #executivecoaching #criticalthinking #careeradvice

HUGE NEWS EVERYONE: OpenAI just launched ChatGPT Enterprise. This is a significant milestone in the intersection of AI and the corporate world. Marketed as an enterprise-grade solution with advanced security, data protection, and unlimited access to GPT-4 functionalities, it is projected to fundamentally reshape work processes within organisations. However, this technological leap raises nuanced legal issues, particularly in the realms of data protection, intellectual property (IP), and the forthcoming AI Act’s foundation model regulatory obligations. ChatGPT Enterprise assures users of robust data protection, stipulating that the model is not trained on business-specific data and that all conversations are encrypted both in transit and at rest. OpenAI claims the platform's SOC 2 compliance adds an additional layer of trust in its security protocols. However, from a legal perspective, questions arise around data ownership and control. OpenAI promises not to train the model on user-specific data, but what about when a company fine-tunes the model on its own data - what are the data protection considerations then? GDPR imposes stringent requirements on data usage, sharing, and deletion, which businesses employing ChatGPT Enterprise must consider. ChatGPT Enterprise's capability to assist in creative work, coding, and data analysis poses tricky questions in relation to ownership. For example, if the AI generates a piece of written content or code, who owns the copyright? The current legal framework, which traditionally recognises human authorship, may not be fully equipped to navigate the nuances of AI-generated IP. The US District Court last week ruled that AI generated work cannot be copyrighted. What if you as a company are engaging third parties to develop code and other work output - if they are using ChatGPT enterprise to generate the outputs, there may be nothing protected by copyright, and no IP rights to assign to you. How will you address that? Then there’s Article 28b of the forthcoming AI Act which imposes strict regulatory obligations on providers of certain foundation models (like GPT4). If you finetune the model enough, that could potentially make YOU the provider with all the regulatory obligations that could bring. And if it doesn’t, you still may have user obligations. Mass adoption of AI across various sectors could draw scrutiny by competition regulators. Could OpenAI’s ubiquity in over 80% of Fortune 500 companies potentially raise concerns about market competition and behaviour? The debut of ChatGPT Enterprise marks an inflection point in the deployment of AI in enterprise environments. While its promise of improved productivity and robust data protection is enticing, businesses and legal experts must pay heed to the complex legal landscape it interacts with. Comprehensive regulation and judicious legal practice are critical in balancing technological advancement with the protection of individual and corporate rights.

🎯 Why Most Business Problems Remain Unsolved (And How to Fix That) Last week, I had the privilege of facilitating a Problem Solving & Business Acumen workshop for our teams at L'Oréal Indonesia. 💡 The Problem We All Face (But Rarely Talk About) Here's an uncomfortable truth: we're wired to jump to solutions. In business, this looks like: ✔️ Launching promotions without understanding why sales declined ✔️ Hiring more people without diagnosing process inefficiencies ✔️ Copying competitor tactics without validating if they fit our context The cost? Wasted resources, frustrated teams, and recurring problems that never truly go away. According to the World Economic Forum's Future of Jobs Report 2023, analytical and critical thinking are the #1 and #2 most important skills for workers. Yet, most of us were never formally taught how to think critically or solve problems systematically. 🛠️ The Problem-Solving Process: A Step-by-Step Guide Step 1: Define the Problem (Don't Jump to Judgment!) 📝 Craft a Problem Statement with 6 components: "How can [responsible party] improve/reduce [reality] to meet [expectation] within [timeline] without [anti-goals], in order to fulfill [reason]?" Example: "How can the product team launch a new product on time in Q4 2024 without sacrificing key processes, in order to meet the sales target?" Step 2: Find Alternatives (Issue Tree + MECE) Once the problem is clear, break it down using an Issue Tree. For instance, if mascara sales dropped -14% YoY: 📦 Placement → Gondola compliance, visibility, signage 🎁 Promotion → BOGO mechanics, POS materials 💰 Price → Elasticity, perceived value 🎨 Product Claims → Content freshness, reviews 🔥 Competition → Share of voice, endcap presence ✅ Ensure hypotheses are MECE (Mutually Exclusive, Collectively Exhaustive)—no overlaps, no gaps. Step 3: Test Your Hypotheses Don't fall in love with your first idea. Run quick tests: 📊 For a skincare serum declining in pharmacies, we tested: ✔️ Hypothesis A: Reduced pharmacist advocacy is the issue → Micro-detailing pilot in 10 stores ✔️ Hypothesis B: Cold chain OOS drives lost sales → Warehouse SOP audit + temperature logs ✔️ Hypothesis C: Execution gaps suppress promo ROI → Endcap compliance audit Each hypothesis had clear KPIs and timelines—no guessing, just data. Step 4: Make the Decision (Impact vs. Effort Matrix) Not all solutions are equal. Prioritize: 🟩 Quick wins—do this! 🟦 Strategic bets 🟨 Fill-ins 🟥 Avoid Focus on low effort, high impact moves first. Build momentum, then tackle the big bets. 🚨 What Happens When We Skip These Steps? A mascara brand saw sales drop -14% YoY. The reaction? "Let's run a BOGO promo!" The result? Sales stayed flat. Why? Because the real issues were: ❌ Poor gondola compliance (only 68% correct facings) ❌ Weak influencer share of voice ❌ Competitor secured prime endcap space The lesson: Solutions applied to the wrong problem = wasted budget and missed targets.

“Manage your AI investments like a portfolio” sounds sensible. Almost too sensible. Diversify risk. Balance horizons. Stage capital. Reduce exposure to failure. All the right words. And that’s precisely the problem. Portfolio thinking is a financial logic applied to what is fundamentally a power shift. It frames AI as a capital allocation challenge, when in reality it is a responsibility allocation problem. Most organizations don’t struggle with where to invest in AI. They struggle with deciding what they are willing to let go of once AI works better than expected. That’s why portfolios quietly fill up with pilots. Experiments that prove value but never replace anything. Systems that assist but never decide. Tools that generate insight but never carry accountability. The portfolio grows. The organization remains unchanged. Activity increases. Impact plateaus. Portfolio logic makes this feel acceptable. Losses are expected. Some bets won’t pay off. Responsibility is spread thin — no single initiative matters too much because “it’s just one of many.” That mindset is fine when you’re buying equities. It’s reckless when you’re delegating judgment to machines. AI doesn’t behave like a financial asset. It rewires decision rights, compresses hierarchies, and exposes unresolved questions about trust, authority, and ownership. When something goes wrong, you can’t blame volatility or market cycles. An AI system acted because someone allowed it to. Or because no one had the courage to decide who should own it. So yes — run AI initiatives as a portfolio if you must. But govern them like infrastructure, not like venture bets. Every AI system that touches real decisions needs a named human who stands behind its outcomes. Not just the upside. The consequences. The companies that will pull ahead are not the ones with the most balanced AI portfolios. They are the ones willing to say: this decision is no longer human-led, this process is retired, this machine now carries responsibility — and so do we. Most AI strategies fail quietly, not loudly. Not because the technology didn’t work — but because nobody was willing to be responsible when it did. https://lnkd.in/eaFQEQWE #AI #Strategy #Transformation #Change

AI replaced a large part of coding. But it did not replace engineering. Generating code is becoming easier. Deciding what to build, how systems should work, where risks may appear, and what happens after deployment still requires human judgment. Here are 7 skills developers need to master: → 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗮𝗹 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 Turn requirements and constraints into scalable, reliable designs while balancing cost, speed, and trade-offs. → 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 Understand how APIs, services, databases, caches, queues, and monitoring work together in production. → 𝗖𝗼𝗱𝗲 𝗥𝗲𝘃𝗶𝗲𝘄 & 𝗗𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴 Check AI-generated code for wrong assumptions, missing conditions, edge cases, performance issues, and production risks. → 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 & 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 Connect user problems and business goals to feature scope, technical decisions, and measurable outcomes. → 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗔𝘄𝗮𝗿𝗲𝗻𝗲𝘀𝘀 Review permissions, data protection, prompt injection risks, insecure coding, and hidden vulnerabilities before release. → 𝗔𝗜 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 𝗦𝗸𝗶𝗹𝗹𝘀 Give clear context, define tasks well, review outputs, refine prompts, validate results, and reuse proven patterns. → 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗢𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽 Monitor logs and metrics, detect incidents, fix root causes, document learnings, and continuously improve the system. AI can generate code quickly. Engineers are still responsible for judgment, reliability, security, architecture, and long-term maintenance. The future belongs to developers who can direct AI and own the outcome. Save this if you are preparing for the next era of software engineering.

Don’t just hear the words, listen between the lines… Language is a powerful tool that enables us to communicate, connect, and build relationships. However, it can also be a source of confusion, conflict, and division. Many of the world's problems stem from linguistic mistakes and simple misunderstandings, where words are misinterpreted, misused, or taken out of context. When we communicate, we're not just exchanging words; we're also conveying emotions, intentions, and nuances. However, these nuances can be lost in translation, leading to misunderstandings and miscommunications. A single word or phrase can have different meanings to different people, and the context in which it's used can greatly impact its interpretation. When we take words at face value, we risk making assumptions about what the other person means. We might assume that we understand their perspective, or that we're being misunderstood. These assumptions can lead to conflict, resentment, and hurt feelings. So, how can we avoid these misunderstandings? By clarifying, asking questions, and seeking to understand the other person's perspective. We can ask for explanations to ensure that we're on the same page. By doing so, we can build trust, resolve conflicts, and deepen our relationships. Don't ever take words at face value. Instead, approach communication with curiosity, empathy, and an open mind. Recognize that words are just one part of the communication process, and that tone, context, and intention are just as important. By being more mindful and intentional in our communication, we can avoid misunderstandings, build stronger relationships, and create a more harmonious world. In a world where words can be both powerful and problematic, it's essential to approach communication with care and attention. By being aware of the potential for misunderstandings and taking steps to clarify and seek understanding, we can create a more compassionate and connected world. So, let's choose to communicate with intention, empathy, and understanding, and see the positive impact it can have on our relationships and our world.