Innovative Teaching Methods Using AI

Over the summer, like many of you, I have been playing intensely with how AI can be integrated into our teaching and learning in a meaningful way. So, I would like to share a relatively recent development from OpenAI called Study Mode. Study mode is a built-in ChatGPT mode that turns the assistant into a tutor. Instead of just giving answers, it guides you step-by-step with Socratic questions, scaffolded explanations, and formative assessments that adapt to your goals and level (using memory from the conversation). Study mode represents a deliberate move toward aligning AI with evidence-based learning science. By using scaffolded, interactive guidance rather than direct answer delivery, study mode fosters active engagement, metacognition, and self-regulated learning. AI tools have often been criticized for enabling passive “answer retrieval” rather than fostering deep learning. Study mode applies principles from How People Learn (Bransford, Brown, & Cocking, 2000), the ICAP engagement framework (Chi & Wylie, 2014), and cognitive load theory (Sweller, Ayres, & Kalyuga, 2011) to create a more purposeful, student-centered interaction using a stepwise scaffold approach. Step-by-Step Scaffold Establish Baseline Understanding. Elicit Prior Knowledge Expand the Solution Space Refine Through Critical Inquiry Synthesize a Combined Approach Integrate Applied Consideration Implications for Teaching and Learning with AI Study mode illustrates how AI can operationalize decades of learning science research: Supports constructivist learning by building on the student’s prior knowledge. Encourages cognitive apprenticeship through guided practice in expert reasoning. Fosters self-regulation by prompting learners to make decisions and justify them. Bridges theory and practice by requiring learners to apply domain concepts to authentic, complex scenarios. Study mode offers an instructional design pattern that mirrors the best practices of human tutoring: diagnosing needs, scaffolding knowledge, eliciting active engagement, and gradually handing over cognitive control to the learner. When paired with sound pedagogy, AI can support not just knowledge acquisition but the higher-order reasoning, adaptability, and reflective judgment that education strives to cultivate. References Bransford, J. D., Brown, A., & Cocking, R. R. (Eds.). (2000). How people learn: Brain, mind, experience, and school(Expanded ed.). Washington, DC: National Academies Press. Chi, M. T. H., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist, 49(4), 219–243. Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. Springer Science & Business Media. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.

Why aren't I hearing more about AI helping teacher education? Not developing slides or activities better, but giving teachers better ways to practice skills that are experientially learned? Here's a paper I found about using AI to evaluate how tutors teach math, but that's just scratching the surface. Consider these game-changing applications: • Virtual Classroom Management: Practice handling disruptions and fostering positive learning environments in a safe, AI-driven space. • Diverse Student Avatars: Hone your differentiation skills by interacting with AI students representing various learning styles and abilities. • Parent-Teacher Conference Simulator: Rehearse difficult conversations with AI-generated parent personas. • Real-time Instructional Coaching: Receive immediate feedback on your teaching strategies from an AI that understands diverse pedagogical approaches. • Cultural Competency Training: Engage with AI-simulated scenarios to improve your handling of multicultural classroom dynamics. These tools won't replace real-world experience, but they could offer unprecedented opportunities for deliberate practice and rapid skill development in areas traditionally learned through years of on-the-job experience. As we integrate AI into education, let's not forget its potential to shape not just student learning, but accelerate teacher growth as well. #teacherdevelopment #teachingcollege #teacher #aiineducation #ailiteracy https://lnkd.in/epz2AWVF

At Tulane, I encourage my students to use ChatGPT. Why? Because if my job as a professor is to prepare them for real-world careers — and AI isn’t going anywhere — then not teaching them how to use it responsibly would be a disservice. If you're an educator still telling students not to use AI, maybe the question isn’t “How do I stop them?” Maybe the question is, “How do I need to evolve as a teacher?” Don’t want students using AI to write papers? Have them submit the paper however they want, then require a 5-minute oral presentation defending their ideas. Two more ways professors can embrace AI: 1. Process-based grading. Ask students to share their drafts, prompts, and iterations in a version-controlled doc (like Google Docs). Grade the thinking and evolution, not just the polished output. 2. AI collaboration analysis. Assign students to compare their own work with what ChatGPT generates, then have them reflect on where AI helped, hindered, or fell short. AI isn’t cheating. AI is a calculator for cognitive work. The job now isn’t to block it — it’s to teach mastery in the age of it. Let's stop fearing the future. Let’s teach it. #HigherEd #AIinEducation #ChatGPT #FutureReady #Tulane #EdTech #DisruptionInEducation #TeachTheFuture #AcademicInnovation #AItools

One of the clearest and most immediate applications of AI is tutoring. It’s not hypothetical. It works right now. And it’s going to change how we all learn. Here’s how to start using AI as a personal tutor: 1. Treat It Like a Thought Partner, Not Just a Search Engine Don’t just ask for definitions. Ask for explanations in your learning style: • “Explain it to me like I’m 12.” • “Walk me through this step by step like a Socratic dialogue.” • “Give me a visual metaphor.” Good AI tutors adapt to your pace, not the other way around. 2. Use AI to Simulate Worlds and Scenarios Want to practice your French with a Parisian? Want to debate the Federalist Papers with Hamilton? Want to be coached on logic puzzles or business cases? You can simulate all of these—with roleplay, feedback, and infinite patience. This changes what’s possible. You can now study with a cast of characters who don’t get tired or bored. Example prompt: “Pretend you’re a brilliant but eccentric Oxford professor teaching me game theory. Make it interactive and quiz me along the way.” 3. Ask It to Diagnose Your Learning Gaps The best tutors don’t just teach—they diagnose. Try asking: • “What am I not understanding here?” • “Where do students usually get confused with this concept?” • “Based on my questions so far, what should I review?” AI is good at spotting what you’re missing—even better when you give it your notes or answers to evaluate. 4. Learn by Teaching the AI Try this: “I’m going to explain this concept to you. Interrupt me if I make a mistake or leave out an important step.” Teaching is a powerful way to solidify understanding. AI can help you sharpen your thinking by gently pointing out gaps or fuzzy logic. 5. Use It to Build a Daily Learning Habit Set up a recurring prompt: “Quiz me on something new every day. Alternate between history, science, philosophy, and critical thinking. Keep it short, but challenge me.” Or build a personalized GPT that tracks what you’ve learned, your strengths, and your interests. Learning is a long game. The key is consistency, not intensity. Final Thought Everyone should have access to a great tutor. AI makes that possible—for the first time in history. And this is just the beginning. If you use these tools seriously, you can learn anything.

A look at how CS50 has incorporated artificial intelligence (AI), including its new-and-improved rubber duck debugger, and how it has impacted the course already. 🦆 https://lnkd.in/eb-8SAiw In Summer 2023, we developed and integrated a suite of AI-based software tools into CS50 at Harvard University. These tools were initially available to approximately 70 summer students, then to thousands of students online, and finally to several hundred on campus during Fall 2023. Per the course's own policy, we encouraged students to use these course-specific tools and limited the use of commercial AI software such as ChatGPT, GitHub Copilot, and the new Bing. Our goal was to approximate a 1:1 teacher-to-student ratio through software, thereby equipping students with a pedagogically-minded subject-matter expert by their side at all times, designed to guide students toward solutions rather than offer them outright. The tools were received positively by students, who noted that they felt like they had "a personal tutor." Our findings suggest that integrating AI thoughtfully into educational settings enhances the learning experience by providing continuous, customized support and enabling human educators to address more complex pedagogical issues. In this paper, we detail how AI tools have augmented teaching and learning in CS50, specifically in explaining code snippets, improving code style, and accurately responding to curricular and administrative queries on the course's discussion forum. Additionally, we present our methodological approach, implementation details, and guidance for those considering using these tools or AI generally in education. Paper at https://lnkd.in/eZF4JeiG. Slides at https://lnkd.in/eDunMSyx. #education #community #ai #duck

How will generative AI shape the future of learning in business schools? In our newly published article, “Generative AI as a Copilot for Teaching Cases in Business Schools” (published in #AdvancesinGlobalLeadership), Mark Mendenhall, Anthony Mendenhall and I explore how tools like ChatGPT are transforming the way students engage with the case method—one of the cornerstones of business education. Using the example of the Computex Corporation case (many thanks to Martin Hilb for allowing us to use the case!) we simulated how three archetypical student profiles—lazy survivors, good faith learners, and overachievers—might use AI to prepare for a global leadership class. The differences were striking. While AI could be used as a shortcut, it can also be engaged as a springboard for deeper insight and critical thinking. 💡 Our key insight? Generative AI has a dual nature: it can be a powerful tool for intellectual growth, but it also runs the risk of becoming a crutch that short-circuits learning when misused. 🧠 But here’s the twist: The very act of prompting and experimenting with AI—when done thoughtfully—can itself be a source of learning. The process encourages reflection, synthesis, and deeper engagement with course material. In this way, AI becomes not just an answer machine, but a thinking partner. This presents educators with both a challenge and an opportunity: to rethink teaching design, adapt our in-class facilitation, and guide students in using AI not to replace thinking, but to enhance it. 💬 We’d love to hear how others are navigating this terrain in their own teaching. Are you seeing similar dynamics with your students? 🔗 Read the full chapter (Emerald Publishing): https://lnkd.in/d_xTqFSK #AIinEducation #CaseMethod #BusinessEducation #HigherEd #FutureOfLearning #GlobalLeadership

Embracing the future of Artificial Intelligence in the classroom: the relevance of AI literacy, prompt engineering, and critical thinking in modern education (published in International Journal of Educational Technology in Higher Education by Springer Nature Group) The present discussion examines the transformative impact of Artificial Intelligence (AI) in educational settings, focusing on the necessity for AI literacy, prompt engineering proficiency, and enhanced critical thinking skills. AI literacy is identified as crucial, encompassing an understanding of AI technologies and their broader societal impacts. Prompt engineering is highlighted as a key skill for eliciting specific responses from AI systems, thereby enriching educational experiences and promoting critical thinking. This is discussed through a case-study based on a Swiss university and a narrative literature review, followed by practical suggestions of how to implement AI in the classroom. 💡 Key Ideas: 1. #AILiteracy is crucial for students and teachers to understand AI capabilities, limitations, and societal impacts. This knowledge enables responsible and effective use of AI in education. 2. #Prompt engineering skills allow educators to strategically design prompts that elicit desired behaviors and critical thinking from AI systems. This transforms AI into an interactive pedagogical tool. 3. #Fostering #CriticalThinking skills through AI use is vital, enabling analysis of information, evaluation of perspectives, and reasoned arguments within AI environments. This prepares students for an AI-driven world. 4. #Continuous AI #training and support for teachers is essential as rapid advancements can otherwise outpace educator knowledge, causing classroom management issues. Keeping teachers updated enables successful AI integration. 5. Addressing #AI #bias through diverse and inclusive training data is important to prevent inequities. Educator training in recognizing biases is also necessary to avoid perpetuating prejudices. 🔧 Recommendations: 1. Develop comprehensive AI literacy courses and integrate AI ethics discussions across subjects to promote responsible use. 2. Provide regular AI training workshops for teachers on prompt engineering, bias recognition, and pedagogical integration to close knowledge gaps. 3. Fund programs that increase equitable access to AI education tools, targeting underprivileged schools and diverse learners. 4. Encourage critical analysis of real-world AI case studies to highlight societal impacts and ethical considerations. 5. Foster an institutional culture of open AI communication through forums and collaborations. This enables continuous learning and innovation. https://lnkd.in/e4xhDdg2

Here's a fascinating bit of history: the United States Military Academy at West Point has been using "AI" since the 1800s (although not the kind you may be thinking of).  "Additional Instruction" (AI) has been a cornerstone of cadet education, offering personalized 1:1 mentoring to those students struggling with complex subjects. Now, a forward-thinking West Point accounting professor has created "AI4AI" - ingeniously merging traditional Additional Instruction with modern artificial intelligence. 🔄Here's how AI4AI works: 1. Cadets must first consult an AI Tutor to explore their questions 2. They submit their AI conversation logs when requesting Additional Instruction from a professor 3. The professor analyzes the submitted AI dialogue before meeting the student 🌟 Why This Approach Is Innovative: This approach aligns perfectly with BoodleBox's three pillars of AI readiness: 1. Domain Expertise: - Students must actively wrestle with concepts using AI before getting Additional Instruction - This "productive struggle" builds deeper understanding 2. AI Enablement: - Students get hands-on experience learning when and how to use AI effectively - This develops critical AI enablement skills for future leaders 3. Human Excellence: - Student-professor interactions become laser-focused on advanced concepts, with AI handling foundational questions beforehand. - By reviewing the student’s AI interactions first, professors can focus their valuable time on what matters most: providing targeted mentorship, sharing deep insights, and building meaningful connections with students. 💡 Why AI4AI Resonates with Modern Education: - It keeps the “Professor in the Loop” ... AI is used as part of a collaboration not as a replacement - It maximizes instructor impact: the Professor can focus on deep engagement and transformative teaching moments - It creates a scalable model for personalized learning support: a professor can reach more students without sacrificing individual attention - It empowers student autonomy while reinforcing that they can and should reach out for guidance when needed 🚀 For Fellow AI in Education Innovators: This aligns well with the innovative approaches to responsible AI in education that we're seeing from over 10,000 faculty and students using BoodleBox: - It's a great example of teaching with AI (to create domain expertise) and teaching about AI (to develop AI enablement), while crucially maintaining the irreplaceable role of human educators - this isn't about AI replacing professors (teaching by AI), but rather empowering faculty to be even more effective and impactful while also being efficient. This innovative approach maintains West Point's tradition of educational excellence while readying cadets for an AI-powered future. It shows how historical teaching methods can be thoughtfully adapted with technology for the modern era. Totally Not Genuine AI Generated Photo Credit: Flux

Case Method and GenAI The case method occupies a foundational place in MBA pedagogy as it immerses students in real-world situations marked by incomplete information, competing interests, and complex managerial trade-offs. Rather than serving as a lecture, it relies on guided inquiry through which instructors pose open-ended questions that compel students to justify arguments, test assumptions, and connect insights to theory. This approach develops judgment under uncertainty, persuasive communication, and leadership within collaborative settings. Students’ preparation—through rigorous analysis and position formation—ensures classroom engagement centers on debate rather than information recall. The process sharpens analytical rigor, critical thinking, and teamwork skills that extend far beyond business school. Generative AI has recently complicated this landscape. These tools can produce polished case analyses within minutes, but uncritical use risks undermining the intellectual struggle central to the case method. If students rely on AI outputs instead of reasoning through ambiguity, key learning outcomes—judgment, ethics, and creativity—may erode. I often recall the tale of a young teacher who asked, “How do you afford to repeat the same questions every year?” The older teacher replied, “I change the answers.” In education, the questions—the cases—may remain fixed, but the quality of reasoning must continuously evolve. AI is simply another variable in this evolution: neither replacement nor threat, but context. It can expand how students frame problems, provided its use is responsible and transparent. The pedagogical challenge is to acknowledge AI’s presence while preserving intellectual integrity. Instructors must set boundaries for acceptable use, redesign cases to emphasize justification over conclusion, and add dynamic elements—new data, counterarguments, or unexpected twists—to prevent reliance on pre-generated responses. Within such a framework, GenAI functions as an enhancer of learning: accelerating research, diversifying perspectives, and freeing time for reflection and discourse. The classroom remains a forum to test and refine managerial judgment, not to retrieve fixed answers. This evolution also redefines the instructor’s role—from content deliverer to mentor in ethical reasoning, critical thinking, and digital fluency. Institutions must therefore innovate curricula that integrate AI’s potential while curbing its shortcuts. Used wisely, GenAI can augment creativity, collaboration, and metacognition rather than replacing effort or curiosity. The enduring power of the case method lies in shaping discernment, empathy, and decision-making under uncertainty. In that journey, GenAI becomes not a disruptor but a companion—one more instrument for questioning, challenging, and enriching the formation of managerial judgment.

Most AI conversations in education are stuck in one lane: policing. But the real question I wrote this book for is: What does learning look like when AI is present—everywhere—and we still want students to think for themselves? My book, Students First, AI Second: Practical Thinking Tasks for Every Classroom, is built around a simple principle: AI should never replace student thinking. It should reveal it, stretch it, and make it visible. Here are 3 practical shifts from the book that change the way AI fits into a lesson: 1) From “answers” → to “thinking traces.” Don’t grade the final product only. Grade the path: decisions, revisions, reasoning, evidence, reflection. 2) From “tool use” → to “task design.” AI doesn’t improve learning by existing in the room. Learning improves when teachers design tasks that require judgment, tradeoffs, and explanation. 3) From “Got it / didn’t get it” → to “show me your process.” Even a weak answer becomes valuable when students can explain why they chose it, what they ruled out, and what they’d improve. Here’s a ready-to-use classroom task (works with or without AI): ✅ The “Better Question” Task (10 minutes) • Give students a topic (or a reading / problem / image). • Ask them to write one “bad” question (too broad, yes/no, obvious). • Then rewrite it into three better questions: 1. a clarifying question 2. a challenging question 3. an application question • Students choose the best one and justify why. If AI is allowed, students can consult it—but they must also write: “What did the AI miss or oversimplify?” and “What question is still unanswered?” That’s the whole point of Students First, AI Second: use AI to increase student thinking—not reduce it. If you’re a teacher, trainer, or school leader and you want practical, repeatable tasks that protect student voice and modernize learning, this book is for you. #AIinEducation #Teaching #CriticalThinking #Assessment #TeacherDevelopment #HigherEd #ESL #ClassroomPractice