Case Studies for Teaching Critical Thinking in Class

#NASA & #BOEING Notes Both have experienced significant controversies & tragedies, primarily in their respective fields of aviation and space exploration. B: The Boeing 737 MAX controversy involved critical design flaws in the Maneuvering Characteristics Augmentation System (MCAS), which led to two fatal crashes. N: The Space Shuttle Challenger disaster in 1986 was caused by an O-ring failure in the solid rocket booster, a known design flaw that was critically exacerbated by cold weather. B: Investigations into the 737 MAX incidents revealed lapses in oversight, where Boeing reportedly downplayed the complexity and risks associated with the MCAS to regulators. N: Both the Challenger and the Columbia disaster (2003) were linked to management lapses where warnings from engineers about potential fatal issues were overlooked by higher-ups, pushing ahead with launches under risky conditions. There have been reported instances of a compromised safety culture where economic or political pressures overshadowed safety concerns. Reports and investigations post-disasters pointed to environments where the escalation of safety concerns was discouraged. B: After the 737 MAX crashes, Boeing faced intense scrutiny from the U.S. Congress, the FAA, and other international regulatory bodies, questioning the initial certification processes. N: Each major NASA tragedy led to comprehensive reviews by governmental oversight bodies, leading to significant changes in operational and safety procedures. B: The 737 MAX crashes severely damaged Boeing's reputation, leading to a financial impact, loss of trust among the public and airlines, and a halt in 737 MAX production and deliveries. N: Fatalities and the resulting investigations typically led to temporary halts in space missions, revaluations of protocols, and a long-term impact on the operational practices and safety measures within NASA. Teaching: Analyze specific cases like the 737 MAX & the Challenger disaster. Discuss ethical responsibilities of engineers + management in these scenarios. How decisions were made, including the role of economic pressure & the ethical dilemmas faced by engineers & executives. Examine how pressures for meeting schedules & budgets can compromise safety measures. Discuss strategies for creating a strong safety culture where safety concerns are prioritized and valued. Study the role of the FAA in the Boeing cases & NASA oversight committees in space shuttle disasters. Debate current regulatory practices & suggest potential improvements based on historical shortcomings. Look at the long-term changes implemented to prevent future incidents, such as changes in engineering practices, management approaches, + regulatory. How high-stress environments & high stakes can affect psychology & team dynamics. Evaluate how these incidents affect public trust in institutions. Discuss the importance of transparency and honest reporting in maintaining public trust.

Most classroom decisions are fake. Here’s how to make them real—and teach students what thinking feels like. Welcome to 🧠 Day 1: Judgement aka: the ability to make considered decisions—especially when every option costs something. We say we want students to think critically. But most of what we assign avoids complexity, risk, or tradeoffs. Judgement isn't about being right. It’s about reasoning in public. And AI gives us a new way to do that—with the friction turned back *on*. Here are 3 AI-powered activities that build judgement as a lived process: › 💼 Boss Mode Tradeoffs Present a messy scenario (e.g. your nonprofit has to cut either a staff role or a community program). ⤷ Students ask AI to help them surface possible outcomes, ethical concerns, second-order effects. ⤷ As they talk it out, they ask AI to test their rationale, simulate stakeholder responses, or build a risk matrix. ⤷ Final step: revise the decision based on what surprised them. --- › 🤖 Decision Tree Remix AI generates a full decision path (e.g. Should I approve this medical procedure?). ⤷ Students interrogate the flow: What assumptions are baked in? Where are values driving choices? ⤷ They prompt AI to revise the tree using alternate values (e.g. “optimize for long-term trust” vs “optimize for cost savings”). ⤷ End with a student-AI co-designed decision model. --- › 🗳️ Values-First Sim Students ask AI to solve a dilemma using different ethical systems (utilitarian, feminist ethics, libertarian, religious). ⤷ Then they identify contradictions across responses, ask AI to cross-examine itself, and generate questions it didn’t consider. ⤷ Students co-author a new “hybrid values” approach with AI that reflects their own worldview. --- The goal isn’t to replace judgment with automation. It’s to build judgment through iteration—alongside a partner who never gets tired of your questions. Which scenario would light your students up? ⚡️

Most A/B tests look simple on the surface, two versions, one outcome, run a t-test, done. But what if your entire analysis is built on a faulty assumption? To help my students spot these hidden traps, I created a synthetic dataset (https://lnkd.in/epyqmxTC) that mirrors a common real-world scenario. In this example, we simulate 75 students spread across 10 classes. Each class, not each student, is randomly assigned to either Design A (control) or Design B (treatment) of an educational platform. Students then use their assigned version, and we measure how long they spend on the page as a proxy for engagement. At first glance, Design B appears to outperform Design A. A few of the B-assigned classes show noticeably higher average time on page. This is exactly where things can go wrong. Without proper statistical training, someone might look at this and immediately run Welch’s t-test to compare students in Design A versus Design B. The logic sounds straightforward. There are two conditions, one continuous variable, and Welch’s test even adjusts for unequal variances, so it seems like a safe choice. But it is not the right tool in this case. The issue is that treatment was assigned at the group level. Classes, not individual students, were randomized. That means the data points are not truly independent. Students within a class tend to behave similarly because of shared dynamics such as the same teacher, classroom environment, or peer effects. Welch’s t-test, just like any traditional t-test, assumes each observation is unrelated to the others. When that assumption is violated, the p-values it produces can give a false sense of certainty. In this dataset, Welch’s t-test produced a very small p-value (p = .00007), suggesting a strong and statistically significant effect of Design B. But when we analyzed the same data using a linear mixed-effects model that properly accounted for the fact that students were nested within classes, the result changed. The effect of Design B was no longer statistically significant (p = .109). What seemed like a convincing treatment effect was actually driven by just two of the ten classes. The other classes showed no clear difference. This example has direct consequences for how teams make decisions, and can result in wasted development time, unnecessary marketing costs, and operational effort spent on a change that delivers no real value. While the scenario may seem straightforward, it highlights a deeper issue: without a strong grasp of experimental design and statistical modeling, it’s easy to apply the wrong test, misread the outcome, and move forward with misplaced confidence. Even one misstep can turn into a failed product launch or a missed opportunity. Statistical reasoning isn’t just a technical skill, it’s a critical part of producing research that supports sound, evidence-based decisions. Please learn stats and methods before doing UX research!

Here is another example of young children and critical thinking. One day, a teacher found this fallen apple on the ground near the apple tree and noticed these interesting marks on its flesh. Instead of just throwing it away the teacher brought it to her class and posed these two questions, "I wonder what happened to this apple? Who made these marks?" The theories and questions started coming: Somebody was trying to eat it! It was definitely an animal! A cat did this! Maybe it was a squirrel? Why didn't they eat all of it? The mystery of it all! The children were immediately engaged in thinking and wondering. The teacher set up a table with the apple, magnifying glasses, and a book about apples. That extended the inquiry as interested children came by to offer their own ideas and consider those of their peers. When they didn't agree with an idea they would challenge it and provide their own reasoning. This discussion wasn't after actual answers. What it provided was an opportunity to think and not just be told what to think. We can grow critical thinkers by starting this thinking routine in the early years. The teacher recognized the potential in what would otherwise be considered trash, and the children benefited. #CriticalThinking #ECE #preschool #ChildDevelopment #TeacherDevelopment #21stCenturyLearning #21stCenturySkills

Today, I taught a 45-minute session on AI to High School students. (These students were recently admitted to Berkeley College.) This is what we did. 1️⃣ I handed everyone a sheet of paper and a pen. 2️⃣ I gave them a scenario, and asked them to take notes. Here's the scenario: *** You’re taking a class. You’re about to complete a group assignment, with 3-4 other people. You get assigned Carl. Now, here’s some information about Carl. Carl is a genius. But he’s a specific kind of genius. He spends almost all of his time on the internet, looking for information. Now, he doesn’t read much. But he does remember a lot of it, because he has a photographic memory. When you ask him questions, there’s a 50% chance he’ll regurgitate something he saw online. Sometimes, it’s Wikipedia. Sometimes, it’s another source. Sometimes, he’s not even sure. It just comes to him. Now, about 25% of the time, he says something absolutely brilliant. He combines different sets of information or he says something that really resonates with you. The other 25% of the time? He makes it up. He puts in random pieces of information, or he says something that’s inaccurate. Or he says something that shows his bias. One good thing about Carl: whatever you'll ask him to do, he'll do. Oh, and remember: Carl is kind of a weird dude. Every time you ask him a question, he has a strange tick. In order to think, Carl needs to flick a light switch and dump out a bottle of water. The ritual helps him concentrate. *** 3️⃣ I asked students to flip the sheet of paper over. I had them spend 3 minutes making a plan: what are they going to do with Carl? 4️⃣ I had students pair up and share their ideas with each other. They worked together to create a plan for dealing with Carl. At the end, we had quite a few ideas. Some students wanted to assign a fact-checker. Some students wanted to give Carl a careful set of instructions. Some students wanted to give Carl a "handler." They watch over him. Some students wanted to ignore Carl, and not let him touch anything. Some students wanted to have Carl complete the entire group project. We talked about the approaches, and their pros and cons. This naturally led us to things like: ➢ Voice ➢ Collaboration ➢ How to set clear instructions ➢ Guarding against false information Then, we looked at ChatGPT. We talked about how this technology relates to our plan for Carl. We talked about what sort of mindset help us out when we use AI.

In grad school, we had a professor pose to us a problem. He created a scenario where students in a 6th grade class suddenly performed poorly on tests. He put us in groups and asked us to think about how to solve this issue. It felt like we pitched to him every possible solution. From reducing test taking anxiety to developing better instructional and teaching methods, we mentioned just about everything we could think of. All the while, he kept shaking his head no in disappointment. He finally couldn’t take it anymore and gave us a hint. What would cause this to “suddenly” happen? One of my peers, a teacher, asked if it involved budget cuts. His demeanor began to change, and we knew we were finally on the right track. After a few more educate guesses, we were able to come to the conclusion that the school had cut the breakfast program. The students weren’t performing well because they were hungry. It was an obvious answer that we should’ve asked about first. If the basic needs aren’t met, everything is pointless. I often think about this as an instructional designer. I can create the best learning experiences possible on paper, but in the real world, I can’t account for everything. Learning environments deeply matter. It serves as a reminder that we need to think about every factor for the learning perspective from the obvious basic needs all the way to the finer intricacies of learning.

The Value of a Nurse: ⛔ WARNING - this case study entails poop 💩 💠 A nurse took was taking care of a patient with a new diagnosis of advanced liver cancer. In report, it was noted that he was having frequent diarrhea. C-Diff was negative and he was getting scheduled Imodium. 💠 The patient kept stating (via interpreter) that he had diarrhea. The nurse did not get a chance to see the stool on their shift. 💠 After a couple days off, the nurse was assigned to this patient again. He was still having diarrhea. The nurse was suspicious. His ascites and distention were worse and his electrolytes were WNL. 💠 Upon repeatedly asking for clarification from the patient and seeing the "diarrhea," the nurse discovered it was actually small amounts of leaky stool 👀 👩⚕️ Nurses, we know what this means – He was constipated. And for days he had been taking Imodium, making his constipation worse. 💡 The nurse informed the doctors that the patient was misleading them by insisting he had diarrhea multiple times a day. An abdominal Xray and ultrasound confirmed that he was obstructed. An NG tube was inserted to suction. There were several issues at play here: 1) Culture - The patient was from a modest culture. He was incontinent and embarrassed, and usually cleaned himself before staff had a chance to assist him. 2) Language – Even though a certified video interpreter was used, information was getting lost in translation. 3) Staffing – If the nurses were not ping-ponging from room to room to keep up with demand, perhaps this would have been caught sooner. It takes a long time to interrogate a patient speaking a foreign language (while all your other patients are waiting). 4) Critical thinking – Do it ✅ VALUE: 💙 Bedside nurses are the eyes and ears of the doctors. They spend 12-14 hours a day with their patients, often several days in a row. 💙 They develop a rapport with their patients, fostering trust and improved communication. 💙 Collaboration between the provider and an experienced nurse are key to improving patient outcomes. Was there a time you put your NURSE DETECTIVE hat on to save the day?! ______________________________________________________ Charlotte Goor – 🩺Registered Nurse. ⚖Legal Nurse Consultant. 📧 Charlotte@ExpertCareLNC.com 💻http://ExpertCareLNC.com