Faculty Development Plans

Reimagining the 🏛️ Classroom: Imagine a classroom where students drive the lesson, and the teacher offers constructive feedback at regular intervals, prompting learners to think beyond the textbook through deep, open-ended questions and organizing tasks that connect mathematical concepts to everyday life. Sounds fantastic, right? But do we observe this in every classroom? If yes, we’re approaching our 🦄 unicorn moment, a rare but ideal educational experience. If not, the question becomes: How do we cultivate such classrooms? It all begins with teacher training and the instructional model adopted by the institution. Let’s explore three popular models of teacher training: 1. 🧑🏫 Craft Model (Wallace, 1991) In this model, the trainee teacher works closely with an expert, learning by emulating their teaching techniques. Pitfall: The trainee is primarily exposed to the strategies of a single expert, which may limit innovation and adaptability. 2. 📚 Applied Science Model Trainees acquire scientific knowledge and pedagogical theories, then apply them in the classroom. Pitfall: A disconnect often exists between theorists and practitioners, creating barriers in translating theory into effective practice. 3. 🤔 Reflective Model Trainees integrate theoretical knowledge with prior experience, apply it in practice, and reflect on their teaching. This reflection informs future planning and instructional decisions. Strength: Though non-linear, this model encourages problem-solving and continuous growth. 🏅 The Ideal Approach: A Thoughtful Blend Personally, a hybrid model offers the most effective results. Trainee teachers: -Study pedagogical theories, -Observe expert practitioners, -Design and implement their own teaching strategies, -Receive mentorship and constructive feedback from experienced educators. This approach fosters autonomy, creativity, and continuous improvement, ultimately driving classrooms where students are active participants in their learning journey. #teacher #educator #teachertraining, #trainingmodel #  

What if an online course didn’t just teach innovation but operated like a product studio? That’s the design ethos behind New Product Development, a fully asynchronous course I am developing within the Master of Business Management at the University of Auckland. It’s not just about learning innovation theory. It’s about practising innovation as a way of learning and doing so in a way that fits the realities of working professionals. The course unfolds through six studio sprints, each aligned with a real-world product development stage: 🔹Framing opportunities 🔹Discovering unmet needs 🔹Designing value 🔹Building prototypes 🔹Go to market strategy 🔹a final innovation portfolio and pitch Every sprint includes hands-on toolkits, reflection prompts, and optional peer critique. Assessments are artefacts: opportunity maps, personas, low-fidelity prototypes, validation plans, and strategic pitches. These artefacts mirror what students might produce in a product team, innovation unit, or consultancy. But what makes this possible online? I’ve reconceived Canvas LMS not as a content repository but as a virtual studio: 🔹Sprint dashboards replace linear modules. 🔹Toolkits and templates scaffold creative work. 🔹Discussions become “crit walls” for sharing work-in-progress. 🔹Reflection journals trace how students make decisions in uncertain contexts. The pedagogy draws from studio-based learning, design thinking, and agile methodologies but adapted for asynchronous learners. This means no Zoom fatigue, no live workshops, and no assuming everyone’s working on the same schedule. Instead, students build momentum through iterative, flexible engagement directly tied to their own industries, roles, and contexts. Why does this matter? Because the students in this course are not full-time students—they are full-time professionals. Product managers, consultants, public servants, engineers, and social innovators. For them, learning must integrate into the flow of work, not interrupt it. Studio pedagogy allows that. It invites them to explore workplace-relevant challenges, use generative AI ethically and creatively, and produce outputs that can feed back into their own projects. It’s one thing to talk about lifelong learning. It’s another to build courses that make it practical, applied, and meaningful. That’s the promise of studio-based, asynchronous design. I believe it’s a model with broad relevance, far beyond product development. #OnlineLearning #StudioPedagogy #LearningDesign #CanvasLMS #InnovationEducation #ProductManagement #HigherEducation #WorkIntegratedLearning #AsynchronousLearning #EdTech #AIinEducation #Universities

An innovative approach to teaching economics that puts student learning first! As an economics educator, I've redesigned my ECO 100-semester project to embrace Universal Design for Learning (UDL) principles, offering students multiple pathways to demonstrate their understanding of macroeconomics. I was encouraged by Jeni Al Bahrani and her summer project on UDL as part of her doctorate degree to bring back this project. Students can choose from three engaging options: * Create a physical art piece interpreting economic concepts through visual expression * Compile an "EconSelfie" diary connecting real-world observations to classroom theories * Write a critical book review analyzing contemporary economic literature. This semester’s choices were Kyla Scanlon’s “In this Economy?” or Scott Galloway‘ “Algebra of Wealth”. This flexible approach: * Accommodates different interests * Empowers student choice and engagement * Maintains academic rigor while fostering creativity * Makes economics accessible and relevant to everyday life The results? Students are more engaged, demonstrate deeper understanding, and develop unique perspectives on economic principles. Seeing how different creative approaches can unlock complex economic concepts is amazing! I'm particularly proud of how this project breaks down barriers in economics education while maintaining high academic standards. Each option requires critical thinking and authentic application of course concepts. I am appreciate the innovative business education environment we have created at NKU Haile College of Business What innovative teaching methods have you implemented in your classroom? Let's share best practices! You can read more about my research in this area in my newsletter. Link in comments. #TeachEcon #UniversalDesign #HigherEducation #TeachingInnovation #StudentSuccess #UDL #EconomicLiteracy

Learning flourishes when students are exposed to a rich tapestry of strategies that activate different parts of the brain and heart. Beyond memorization and review, innovative approaches like peer teaching, role-playing, project-based learning, and multisensory exploration allow learners to engage deeply and authentically. For example, when students teach a concept to classmates, they strengthen their communication, metacognition, and confidence. Role-playing historical events or scientific processes builds empathy, critical thinking, and problem-solving. Project-based learning such as designing a community garden or creating a presentation fosters collaboration, creativity, and real-world application. Multisensory strategies like using manipulatives, visuals, movement, and sound especially benefit neurodiverse learners, enhancing retention, focus, and emotional connection to content. These methods don’t just improve academic outcomes they cultivate lifelong skills like adaptability, initiative, and resilience. When teachers intentionally layer strategies that match students’ strengths and needs, they create classrooms that are inclusive, dynamic, and deeply empowering. #LearningInEveryWay

AI decouples confidence from competence. And our pedagogies are not mature enough yet to deal with it. Shaw and Nave's new paper (2026) found that AI access inflated participant confidence by over 10%, even when the AI gave wrong answers. Participants only overrode incorrect AI answers 20% of the time and ‘confidence did not significantly decline as the number of faulty trials increased.’ This is strange for anyone who designs learning. Traditionally, confidence tracks competence through a feedback loop. You try something, you see the result, you adjust your self-assessment. Get it wrong a few times, confidence drops. You adjust, get better, confidence rises. The loop is slow and painful, but it keeps the two roughly aligned. The paper identifies a sequence they call ‘cognitive surrender’: a student encounters a question, consults AI, adopts output, moves on. The stages of thinking collapse into a single step: convenient, passive acceptance, impervious to prior experience. Effective pedagogy has to interrupt this sequence somewhere. The question is where, and what each interruption point produces. ➜ Interruption 1 (Before AI Consultation): Pedagogy of Pre-Commitment. Students generate their own response before seeing AI’s. Without pre-commitment, students have no baseline to compare against. With it, the comparison between “what I thought” and “what AI produced” concretely exposes an investigative gap. ➜ Interruption 2 (During AI Consultation). Pedagogy of Directed Skepticism. Students consult AI with a specific evaluative task, not an open question. Instead of "explain X to me," the prompt becomes "here is my explanation of X, find the weaknesses." This changes the student's cognitive posture from receiver to evaluator. ➜ Interruption 3 (After AI Consultation). Pedagogy of Divergence Documentation. Students document where their thinking diverged from AI's. What did AI suggest? Where did you agree? Where did you disagree? What did you change? When participants had a reason to care about accuracy, override rates doubled. Making divergence documentation part of the grade creates an incentive. ➜ Interruption 4 (At The Point of Self-Assessment): Pedagogy of Calibration. The paper's confidence inflation finding means AI compromises self-assessment. The pedagogical response is not to abandon self-assessment but to make it more empirical and objective. Students predict, test, measure the gap. They track their predictions, score their accuracy, identify their systematic biases. ➜ Interruption 5 (At The Social Level): Pedagogy of Accountability. The paper tested individuals in isolation. It did not test what happens in dialogue. When a student has to explain their AI-assisted work to a peer or instructor who asks follow-up questions, cognitive surrender becomes visible and costly. The pedagogical move is to build oral accountability into AI-assisted work, not solely  as a policing mechanism, but as a site where learning intensifies.

🌱 “𝐈 𝐝𝐨𝐧’𝐭 𝐟𝐨𝐫𝐜𝐞 𝐭𝐡𝐞𝐦 𝐭𝐨 𝐠𝐫𝐨𝐰. 𝐈 𝐫𝐞𝐦𝐨𝐯𝐞 𝐰𝐡𝐚𝐭 𝐬𝐭𝐨𝐩𝐬 𝐭𝐡𝐞𝐦.” This line hit me hard—because that’s what great teaching truly is. I once had a student who struggled not with ability, but with fear—fear of making mistakes, of raising their hand, of being wrong. Traditional instruction kept nudging them to “speak up more.” But what actually worked? Giving them a safe space to think quietly, letting them submit reflections anonymously, then slowly offering low-stakes speaking opportunities. They bloomed—on their own terms. 🔍 This is what barrier-free learning looks like. Not pushing students harder, but asking: What’s in their way—and how do I remove it? Some powerful methodologies that support this mindset: ✅ Inquiry-Based Learning – Let curiosity drive the lesson. ✅ Scaffolded Instruction – Support step-by-step until confidence builds. ✅ Metacognitive Reflection – Teach students to know how they learn. ✅ Growth-Oriented Assessment – Focus on progress, not just performance. 🌿 Students don’t need force. They need conditions to thrive. #LearnerCentered #Pedagogy #InquiryBasedLearning #GrowthMindset #TeachingStrategies #HolisticEducation #Scaffolding #ReflectivePractice #BarrierFreeLearning

My hot take for the day is that the best thing to do in response to genAI in the classroom has nothing to do with genAI. Instead, we should use any disruption to double down on building classroom communities full of trust and an embrace of the frictionful state of learning. 1. Learn students’ names: perhaps one of the highest ROI things you can do to create a foundation for community. 2. Foster metacognitive habits: help student reflect on what they're learning and how. You want to build independent, active learners instead of passive receivers of information. 3. Teach with transparency: don't hide the ball. Put your motivations and pedagogical decisions on the table. 4. Communicate explicit learning objectives: tell them the point of every assignment and what they're supposed to get out of it. 5. Make communication policies clear: tell them how to get a hold of you and set expectations for when they can expect a response. h/t to Robert Talbert for this one. 6. Create frameworks for feedback: help them understand how to give and receive feedback. I really like @kimballscott's framework of Radical Candor for this. 7. Double down on active learning: get them engage in the work of learning. This is fun and often looks a lot like play! Don't just talk at them but get them talking to you and to each other. 8. Encourage experimentation: iterative improvement and failure is the way. 9. Cultivate community: help them fully leverage the rich relational web that is in the background of every classroom. This is so often untapped. 10. Connect individually with each student: it might be challenging, but do your best to get to know each student as an individual person. Feeling like you're seen and that you belong matters. 11. Build shared responsibility for learning: teacher and student both have to bring something to the table for learning in the classroom to happen. Call this out explicitly and have a conversation about what everyone is bringing. 12. Get alongside students: try to avoid being in front all the time but get beside your students so that they see you are on their side and wanting them to succeed. 13. Model vulnerability: when you mess up, and you will, own it. Much easier for them to do it if they see it from you. 14. Reframe from "have to" to "get to": everybody has some level of agency in their choice to be in the classroom. Remind everyone of the opportunity and privilege it is to be in a classroom. 15. Trust your students: what if you gave your students the benefit of the doubt and trusted them until they gave you a reason to do otherwise. 16. Offer opportunities for failure and retries: learning happens when we try, fail, reflect, and try again. 17. Embrace friction: learning, like any worthwhile activity, is hard work. Instead of looking for a frictionless experience where we accomplish things without effort, encourage students to dig into the worthwhile challenge of learning something new and growing.

AI Chatbots Boost Learning When Paired with the Right Pedagogy! This new study by Zhang et al. (2026) proves what we have been saying here over the last few years: Effective AI integration starts with pedagogy. And one of the strongest points to start with in this pedagogy-led AI integration is the backward design framework. McTighe and Wiggins' (2012) Understanding by Design model asks teachers to plan in three deliberate steps: identify the desired learning outcomes, determine the evidence students need to produce, and only then choose the activities and tools. AI belongs in step three, not step one. Zhang et al.'s (2026) findings line up with this logic. The chatbot-assisted approaches with the largest effects (inquiry, situated learning, problem-based learning) all start from a clear pedagogical purpose and use the chatbot to serve that purpose. The weakest effects, game-based learning, often come from designs where the tool is bolted on without alignment between the chatbot's affordances and the learning goals. The lesson for teachers is simple. Don't open ChatGPT and ask "what can I do with this?" Open your unit plan and ask "what do I want students to learn? What evidence do I need? What role can a chatbot play in producing that evidence?" The chatbot earns its place in the lesson only when the design has already decided what the lesson is for. Pedagogy comes first. Tools come second. Link in the first comment! #AIinEducation #AIPedagogy #BackwardDesign #EdTech #AILiteracy #TeachingWithAI References McTighe, J., & Wiggins, G. (2012). Understanding by Design framework. ASCD. Zhang, Q., Zhang, N., & Lu, C. (2026). How do pedagogical approaches affect the impact of chatbots on learning performance? A meta-analysis and research synthesis. Educational Research Review, 51, Article 100783.

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.

If our students passively absorb info, we failed them. They need active, meaningful, enduring learning. We do that by increasing conceptual friction (nod to Jason Gulya). Students need challenges and complexities to increase Critical thinking, problem-solving, deeper understanding. ✅ 𝗧𝗶𝗽𝘀 𝘁𝗼 𝗹𝗲𝘃𝗲𝗿𝗮𝗴𝗲 #AI 𝗳𝗼𝗿 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗶𝗻𝗴 𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝘂𝗮𝗹 𝗳𝗿𝗶𝗰𝘁𝗶𝗼𝗻 ➡️ Structured academic controversy Assign students different stances on an issue. Use AI to generate arguments for each side. ➡️ Predict-observe-explain (POE) activities Students predict outcomes, observe results, and explain observations. Use AI to simulate physical phenomena or historical events. Students test predictions and refine their understanding. ➡️ AI-generated prompts for critical thinking Generate complex, open-ended questions. Require students to apply knowledge in new ways. (Use Ruben Hassid Prompt Maker GPT to improve prompts.) ➡️ Interactive simulations and scenarios Create interactive simulations that mimic real-world scenarios. In a physics class, AI can simulate different frictional forces and their effects on motion, allowing students to experiment and observe outcomes in a controlled environment. ➡️ Analyzing AI responses Ask AI to write an essay or solve a problem. Students analyze and critique the AI responses. Identify errors, biases, and areas for improvement. ➡️ AI as a debate partner Use AI to simulate a debate partner. Help students practice argumentation skills. They respond to AI-generated counterarguments in real-time. ➡️ Scaffolded assignments Students use AI tools at different stages of their work. Brainstorm ideas, draft an outline, and refine final product. ➡️ Role-playing and simulations Simulate negotiations or market analysis. Provide a dynamic, interactive learning experience. Students and AI take on different roles in a simulated environment. ➡️ Feedback and revision cycles Provide instant feedback on student work. Encourage multiple revision cycles. ➡️ Ethical and societal implications Explore ethical and societal implications of decisions. Simulate the impact of different policies on society. ✅ 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 𝗳𝗼𝗿 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 ➡️ Co-create expectations With students, define appropriate use and how AI should be cited. ➡️ Encourage reflection After using AI, students reflect on their experiences: How they'll use AI differently in the future. How AI influenced their thinking. What they learned. ➡️ Provide support and resources Tutorials, help sessions, online resources. Explain how to use AI effectively and ethically. ------------------------- Thoughtfully integrate AI into your classroom to ⬆️ conceptual friction. Challenge students. Promote critical thinking. Prepare them for an AI-infused future. ------------------------- ♻️ 𝗿𝗲𝗽𝗼𝘀𝘁 𝘁𝗼 𝘀𝗵𝗮𝗿𝗲 𝘄𝗶𝘁𝗵 𝘆𝗼𝘂𝗿 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 𝘀𝗼 𝘄𝗲 𝗰𝗮𝗻 𝗹𝗲𝗮𝗿𝗻 𝘁𝗼𝗴𝗲𝘁𝗵𝗲𝗿