Real-World Problems to Enhance Critical Thinking

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? ⚡️

We go through tons of math and physics problems in STEM education. And yet, we fail to leverage the most important learning: modeling. A typical engineering problem might look like this: "Consider an elephant of mass X at the top of an inclined plane of height Z and angle Y. The elephant slips and rolls down. Determine how long it will take to reach the bottom." Then comes the killer note: "Assume the elephant is a perfect sphere with uniform density and no friction." At this point, the problem is no longer engineering, it’s just math. The student plugs in a formula, solves for time, and moves on. But real engineering isn’t about solving equations. It’s about making decisions and solving problems. A better approach? Remove the note. Now, the student must: ✅ Define assumptions. Is friction negligible? Is a perfect sphere reasonable? Probably not, but making that assumption gives a lower bound for time. ✅ Question the real-world implications. How does shape affect motion? How does friction change the problem? ✅ Recognize uncertainty. Maybe the elephant gets stuck. Maybe it never reaches the bottom. Now, the student is forced to reason about bounding the problem rather than just computing a single number. This is engineering. Not just applying formulas, but thinking critically, defining problems, and managing uncertainty. Too often, we strip engineering problems of the complexity that makes them worth solving. But real-world engineers don’t get neat assumptions, they get messy, ambiguous, imperfect systems. We should teach students to think like engineers, not calculators. How can we improve problem modeling in engineering education?

This is my 4th year teaching at DePaul, and each time, I learn more about myself and the importance of being a constant practitioner. This group of students was particularly committed, driven, and passionate; reinforcing my optimism about people's desire to improve themselves and the world around them. This class explored core concepts like sensemaking, small data, biomimicry, nature-based solutions, Indigenous wisdom, doughnut economics, and social enterprise. These lenses are crucial for understanding and addressing real-world issues. One of the best quotes from Christian Madsbjerg that we all must reconsider is that we must "go to the savannah and not the zoo." This is a reminder to spend time with our end users in their element and not assume we have all the answers within a simulated environment. Our class dove into three final projects using the UNLEASH design thinking method.  Below is a short snippet of the high-level insights. 1. Pulaski Pedestrian Safety Project Problem: Addressing the alarming pedestrian safety issues on Pulaski Street in Chicago. Challenges: Resistance from the Illinois Department of Transportation (IDOT) and increasing pedestrian accidents. Solutions: Community engagement, increased enforcement, and advocating for infrastructural changes. Impact: Highlighted the critical role of grassroots efforts and the power of community voices in pushing for change. 2. Combating Food Deserts in Englewood Problem: Tackling food scarcity and poor nutrition in the Englewood area. Challenges: Limited access to fresh produce and educational resources. Solutions: Planting fruit trees, educational programs on healthy eating, and creating urban gardens utilizing church lands. Impact: A holistic approach that combines education with practical solutions, aiming to improve the community's physical and mental well-being. 3. Electric Vehicle (EV) Feasibility Study Problem: Enhancing the adoption of electric vehicles by addressing battery life, range, and charging infrastructure. Challenges: High costs, long charging times, and limited charging stations. Solutions: Implementing battery swapping technology to reduce charging times and improve convenience. Impact: Promoting environmental sustainability and making EVs more accessible to middle and low-income consumers through innovative technology and public-private partnerships. I am grateful to all my DePaul students over the years! Please let me know if I can ever be of assistance. Onward ;) Bruce Leech Emily Doyle Alyssa Westring Melissa Rountree Maija Renko Zoharia Drizin Joona Mikkola Gretchen Shuler Lisa Gundry Katie Morris Amy Marie Amaon Al Parkalob Dan Morgan Marzena Fiolek, MBA Caitlin Fuller Makayla Read, MSSM Brady Furleigh Kathia Hernandez Ryan H. George Brigandi Nicole Laumer Jean-Stéphane Naas Joseph Knight Alejandra Pineda Hajarah Ashraf Michael Wiencek Virginia Head Preeti Iqbal Natalie Probstein David Townsend, MBA Rachel Habegger Olawale Babatunde (MS)