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I’m closing out the school year by slowing down to actually look back and make sense of what happened. It’s a metacognitive act that I, for one, could certainly do more of.In this article, I’m inviting a few guests from the podcast episode “How Did It Actually Go?” (link below) to guest-write an article. Each of them agreed to go a little deeper in writing, reflecting on how process-based learning with AI actually played out in their schools.In this article, Leon Lam reflects on building an AI chatbot for process-based learning and what he’d do differently next time. Reflecting back, what he found is at the center of this piece, so I won’t spoil it. But here’s a takeaway I want to reinforce: he assumed students would follow the process without ever explaining why. A process that remains invisible asks students to comply rather than learn. Adolescents need both to understand why we do something and how it helps them grow.Let’s check in with Leon to hear his thoughts.IntroLeon Lam here, guest posting on Alex’s Substack. This will be an extension of what I discussed on the podcast episode “How Did It Actually Go?” I want to dive deeper into what I learned building an AI chatbot for process-based learning, and what I would do differently next time.A lot of teachers use custom chatbots in their classrooms. I went a step further and built an entire platform for creating custom chatbots, mostly for analytics and data. I wanted to know whether or not the AI chatbots were making an impact. My hope is that what I learned can help you decide whether to invite an AI tutor into your classroom or to keep it outside.What I BuiltAs mentioned in the podcast episode, my Socratic essay-writing bot coached students through Cambridge AS Economics 12-mark essays. It was structured around stages: question analysis, planning, and paragraph coaching.In my bot, Alex’s Think, Generate, Edit process was built into each stage. Students had to think through the questions the AI gave, craft their own responses, which the AI gave feedback on according to preset criteria, and then they had to edit their work until it matched the criteria. The stages were important because they allowed students to plan first instead of jumping into the writing immediately.What I ObservedI categorized student behavior into three patterns. The first group of students spent hours with the bot. They exchanged hundreds of messages. They followed the bot’s strictly enforced reply format. They complied with it for as long as it took to complete the essay on the platform. It looked like they were fully engaged, but upon deeper digging, I discovered that the chatbot I made was needlessly ruthless, and that the learning could’ve happened much more quickly if I had built in human touchpoints or relaxed the restrictions. Students had reported how cumbersome it was to get the exact answer the AI wanted.The second group of students tried to game it for answers. The bot was designed to ask them questions, so they worked at getting around that. These students weren’t really learning anything. They tried prompt injection, off-topic detours, anything to extract an answer. They mostly failed, but time was wasted on trying to manipulate an AI instead of learning.The third group of students did not engage with it at all. That was just not how they wanted to learn. In the end, the bot was just a chat interface. They wanted a teacher, and I saw their eyes light up when I took back the reins in the classroom.Performance on summative assessments did not change since using the bot, either. Students who did well before continued to do well. Students who struggled before kept struggling. What did change was my ability to see the process. I could zoom in on student artifacts and query the data with an LLM, and that visibility was genuinely useful. So in that way, you might say the bot served as an assessment, which provided data that in many ways reinforced what I already knew about the students.I suspect the issues came from two main reasons:* I did not name the thinking processes out loud with the students. I built it into the bot and assumed they would just follow along. Thinking back, I should have made them aware of the process so they know why I chose to set up the assignment this way. My thinking is that it would help the hacker group and the disengaged group to want to use the bot meaningfully.* I removed too much of myself from the process. In theory, the assignment should have worked. In practice, my students handed the entire feedback process over to an AI, and the efficiency I was chasing became the flaw. Aimée had named it before I did. She’d been a guest on the same podcast; when I heard her portion of the episode, her idea of “gates” mapped exactly onto what I’d been considering ever since I put the bot in front of students.What I would do differentlyI do believe that process-based learning is the way to go in the age of AI, but my next iteration of this assignment will definitely be different. Here’s how I will pivot moving forward:I will reinsert myself in the feedback loop. AI should not give feedback on its own, no matter how well it is trained. Giving feedback to students is what builds trust and rapport; that’s the teacher’s job. There’s still a real role for AI, though. An AI can be trained to spot specific writing weaknesses and tag them to feedback I’ve already written, or to extend a comment of mine by pointing to a resource. The condition is that everything passes under my eyes before it reaches the student. The main point is that AI-use should reinforce things that we are learning and want to support in class.I’m going to teach the process out loud. I will explicitly teach my students Think, Generate, Edit, and other processes, or co-create a process with them in class that suits the task. This time, they got a tool that already knew the answer to that question, and they were left to comply with it. However, if I had printed our process on paper and written underneath each step where and how AI was used, and why, my students would have understood the design from the inside and hopefully have been engaged with every step.I’m changing how I grade. Because some students will focus too much on the final output, even if I ask for process artifacts, I won’t accept a finished essay unless the artifacts back it up. The artifacts will be graded, too, but the bulk of the grade for the final product will be awarded only if the final output was the natural product of the process.I’m magnifying authentic, in-person assessment without neglecting AI literacy. I want students to share opinions that are actually theirs, in front of other humans, with their screens closed. That is often uncomfortable for them, and that is the point. My ideal classroom is one that maximizes original thought, critical thinking, and other capacities needed to interact effectively with AI, which I am making a focus outside of the classroom. Students will be instructed to interact with AI without my supervision. This means I will need to teach AI literacy, so my students can remain thoughtful and responsible.I’m still building, but smarter. I built a platform that made students learn through Socratic chatbots, a workflow that’s still new and unproven in their minds, piled on top of everything else they already had to do. So the next iteration starts from what students already do instead of inventing something foreign. I rewrote the entire textbook with AI for accessibility, same flow through the topics, but with simpler, more direct wording. I put MCQs inline for formative checks, and digitized over 3,500 past-paper MCQs organized by topic so students can set up their own mock tests, complete with explanations for the wrong answers. Because I own the platform, I’ll know exactly which topics a class struggled with. So I will keep building my own platform and iterate on the processes students already go through, improving their learning and my teaching at the same time.ConclusionThinking back to my own time in schooling, I don’t remember which teachers had the coolest PowerPoints or used the latest gadgets. I remember the chance to question alongside other students, the jokes, the moments of wonder and care. I miss watching students struggle through the thinking, take pride in what they made, and own their learning. With the changes I’ve shared, that’s the classroom I want to build in this AI-enabled world.Monday Ready ResourcesOne of the most important realizations from this experience is that I need to talk to students along the way about the process or even co-design it with them, as well as teach AI literacy. Here are three ways you can get started with your students to make a process or build AI literacy:1. Go to Alex’s AI Enhanced Process generator and take a crack at making your own process on your own or with your class.2. Take Anthropic’s free course on AI Fluency: Framework & Foundations. This can also be done on your own or with your students.3. Use my Process-Based AI Use Scale which you can post around the room, or use with students to determine how much AI should be used in each specific step of the process.AI DisclosureI wrote this whole article, and then Claude was consulted on for wording, structure and flow. Some of Claude’s suggestions made it to the final version. Some suggestions inspired other original changes. Ultimately, the words are entirely my own and represent my opinion. Aside from the screenshots of my application, I gave ChatGPT the final version of this article, asked for image suggestions, and asked it to craft the prompt that generated the images you see in the article.Images generated by ChatGPT and Gemini. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit aienhancedprocesses.com

IntroI’m closing out the school year by slowing down to actually look back and make sense of what happened. It’s a metacognitive act that I, for one, could certainly do more of. Over the next few weeks, I’m inviting a few guests from the podcast episode “How Did It Actually Go?” to guest-write an article. Each of them agreed to go a little deeper in writing, reflecting on how process-based learning with AI actually played out in their schools. Writing is both output and thinking. Writing is the actual process of figuring out what you believe. I’ve found this idea to be true on this Substack, and I think my guests have too. There’s a depth in that kind of deliberate slowing down that I haven’t always experienced with AI-generated text. Personally, I can’t help but wonder whether that reflective habit is at risk. The world is moving fast, and every week there’s a new exciting model. Deliberate slowing could look inefficient in that context and anti-zeitgeisty.With that, I want to thank Aimée Skidmore for this week’s post, which sits at the center of this discussion. Aimée teaches Grade 12 in Geneva and thinks hard about when GenAI is in the room, how we can maintain student agency and effortful thinking, as they are prone to wanting to move too fast in the name of completion. To get students to deliberately slow down, she created something that she calls gates that serve as pauses, or deliberate moments in a thought process, where students have to show what they are actually thinking before they earn the right to move on. The idea came from the game Dungeons and Dragons, which tells you something about how Aimée thinks. She’s practical, a little playful, and genuinely curious about the tension between structure and ownership in a classroom where AI can skip the messy middle entirely.In this piece, she walks through two iterations of the same project, what she noticed between them, and the questions she’s still thinking about. The Monday-Ready resource at the end is concrete and immediately usable, with a checklist of things we can do with gates in a process.Make sure to follow Aimée on Substack. Enjoy!From AiméeWhen students use GenAI, the worry is that they’ll outsource the final product. But the bigger risk is that they outsource the messy middle: the testing, rejecting, revising, deciding, and explaining.So many of us avoid this issue by designing around GenAI. And I used to spend a lot of time wrangling with how to do this with some of my lessons and projects. Now, I spend less time doing that and more time engineering moments where students have to show what they are thinking before they move on.My Grade 12 students were working in pairs to build a chatbot to help another student practice a certain habit of mind, like persistence or thinking flexibly. I wanted them to work through a Design Thinking process of empathy, define, ideate, prototype, test. Some of these steps involved getting support from GenAI, and some were not. I wanted them to be balanced in their use of tech.Alex McMillan’s AI Enhanced Process Generator was a key tool in helping me decide and communicate on which steps students might use AI to help and where I wanted them to work on their own. Full product scrolling screenshot below.At first glance, it could have looked like a dream GenAI project. Students were using AI, building something for a real purpose. They seemed to be in the flow and moving quickly. Maybe a little too quickly.I started noticing that students were at their computers, starting to build the chatbots, pretty early on. Some were even submitting the link to their final product in one class period. I felt a little panic and then decided to walk around and ask how things were going. What I found was disappointing: I couldn’t get to every student, there were some who couldn’t answer my questions about their process, and there were some who didn’t accept my suggestions to slow down and have another look at the first steps.So I went back to the drawing board to rethink the approach and rebuild it for the next cohort. How could I get them to slow down and go through all the steps of design thinking? I was trying to find out how I could get them to hand in a ‘rough draft’, like we do with essay writing, but I was more interested in checking their process than their product. I didn’t really care so much about whether the chatbot was 100% functional. It was only one small piece of the project rubric.Iterating with GatesOn my second iteration of this project, I decided to add some proficiency checkpoints: a pause and check that students have to take before they move to the next stage of the work. I called them gates because I had this image of a DnD player facing an important decision where they need to slow down, check equipment and consult with their party before going through.Here are the two I built:Here’s what happened:The pace slowed. Students appeared to be more thoughtful in their choices. They had to sit through the struggle and check their own work before asking me.The talk changed. I was able to have short conversations with each student when they called me over to sign off. Over time, our talk became less about me checking their work and more about “Tell me where you are now.” “What do you like about this tool so far?”Students started explaining choices. “What led you to that decision?” I was able to redirect them when I saw they were not thinking deeply enough and ask them some questions that made my coach’s heart flutter. “What was challenging here for you? And what else?”They noticed problems earlier. Before they handed it in, they were able to make improvements because they could see those changes would make the final product stronger. The project became less about “my chatbot works” and more about “my chatbot is designed for a real learner.”This felt like a real win. The gates did what I hoped they would do. They slowed the project down in the right places. They made the process more visible and gave students a reason to explain their choices before rushing ahead.And this is the part I’m still thinking about. I feel a tension here about how much of the process I should define for them.When I create something, I do not move through the work in a straight line. I start in one place, jump to building, get stuck, jump somewhere else, come back, revise, test, rethink, and slowly find my way through. That movement feels natural to me now, but it took years to build. Students are still learning what that kind of process feels like.So the questions I’m sitting with now are: how do we give students enough structure to support their thinking, without turning the process into another set of steps they simply complete for us? How do I avoid a heavy process that will lead to more paperwork and overfunctioning for me?Because if I build too many gates, or if every gate depends on my approval, I risk creating the very thing I’m trying to move away from: students waiting for me to tell them if they are doing it right, if they are allowed to continue.So, the next version of this project might have students deciding where the gates go. It might involve more student self-checks, more peer testing, and more room for students to say, “This is what we tried. This is what we changed. This is why we’re moving forward.”And probably more modeling from me, too. Not modeling the perfect process, but showing what it looks like to get stuck, change direction, reject an idea, return to an earlier version, and keep working. That feels important because students do not learn ownership by being dropped into total freedom. They learn it by practicing responsibility within a structure that helps them keep going.The gate is not the point. The pause is the point. And what students do inside that pause is where the learning lives. That, to me, is one of the real design challenges with GenAI in the classroom. Yes, the tool can make the work move quickly. My job is to help students slow down enough to notice what they are doing, make real choices, and stay awake inside the process.Monday-Ready ResourcesResource #1 - Checklist when Using GatesSeparate the gate from the grade. If students associate checkpoints with judgment, they’ll perform readiness rather than demonstrate it. Frame the gate as a conversation. “Walk me through your thinking” lands differently than “let me check your work.”Unpack the steps before students take them. When you introduce a process, explain why each stage exists. Human psychology is consistent on this: we do not expend effort on things that feel arbitrary. If students understand why the empathy phase comes before the prototype phase, they’re more likely to take it seriously.Use a student-facing checklist, then release some gates over time.Before students call you over, they should be able to say yes to two or three concrete criteria. This shifts the first layer of accountability to them and changes what the teacher conversation is actually for. Over time, some gates can become peer-checked or self-certified. Early on, every checkpoint might involve the teacher. Once students show they understand the process, they can take on more of the checking themselves. This builds toward ownership without dropping them into total freedom before they’re ready. You can see how I built this into Step 5. Test on the Project Worksheet. (link below)Create a process journal and build in feedback before moving on. Ask students to document their thinking at each stage before they call you over. The journal becomes evidence of the work, not just the product. A peer can respond first; the teacher becomes the second reader. You will see how I did this through a Project Worksheet. (link below)Practice the process more than once. Research on habit formation and classroom routines suggests it takes roughly three iterations before a process becomes something students internalize and implement with any real fidelity. The first run is orientation. The second is where it starts to click. The third is where it becomes routine.Go public with stuck moments. Model a process where you make a wrong choice, back up, and explain why you changed direction. Do this more than once. If the only process students ever see is the polished version, the messy middle feels like a mistake instead of a sign the work is actually happening.Resource #2 - Printable ChecklistHere’s a PDF you can print out, along with look-fors, for using a gate in your processes with students.AI DisclosureChatGPT was used to help me code some pieces for the Teacher and Student Project Brief. It also helped check the project for safeguarding issues, and gave the idea to have students think about and build in guardrails. I used it to generate the Project Rubric.Image of the gate created with ChatGPT to represent the idea of a gate: a pause where students make their thinking visible before moving on.Elements of the article leveraged AI as a supporter of language clarity, not idea generation. All conceptual content of this article was created either by Aimée or Alex, or both in partnership.The Project LinksCheck out the project here referenced in the article.* TEACHER version - https://buildhomassistantteacher.netlify.app/* STUDENT version - https://buildhomassistantstudent.netlify.app/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit aienhancedprocesses.com

It's time to finish up the year with one last podcast episode. I decided that I wanted to have a reflection and talk to people about how process-based learning has been going inside their schools or classrooms. I talked to a range of educators and asked them several different questions, and this episode is a series of highlights from those conversations. So, over these 20 minutes, you're going to hear a series of short recordings in which we look at process-based learning with AI from several angles. Below are notes about each of the guests with links to their websites and social media. Thank you all for contributing to this episode!Aimée Skidmore | Teaching and Learning Coach | GenevaAimée works with experienced teachers who are tired of being the engine in the room. Her focus is student ownership: structures where students start, think, revise, and take responsibility without the teacher carrying it all. She appears twice in this episode. First, she describes what process-based AI use looks like from inside her classroom. In her second segment, she explains how deliberate checkpoint gates changed the outcome of a chatbot-building project.Aimée offers a six-week Student Ownership Sprint for secondary teachers. She also hosts the International Teacher Staffroom podcast.LinkedIn | TeachSparkAimée wrote a companion piece to go along with this episode. After you listen, make sure to read her more in-depth write up about “gates” below.Jay Goodman, Ed.D. | PBL Consultant | CanadaJay has spent nearly two decades designing problem-based learning programs. His Ed.D. focused on PBL program design. He co-developed the Innovation Institute, an award-winning interdisciplinary PBL program in Shanghai.In this episode, he describes mentor bots: teacher-designed AI personas built around specific domains of expertise. Students identify a knowledge gap, do initial research, and then bring that thinking into a structured conversation with a field-specific model. It solves a real PBL logistics problem without replacing the thinking students need to do first.LinkedIn | Goodman Learning PartnersVamshi Mugatha | Director of Technology | American School of BrasiliaVamshi brings in a leadership perspective as an admin. Vamshi describes a familiar challenge for many schools around the implementation side of a policy. What he realized was that the missing piece was expectations. When teachers weren’t setting them, students were using AI without disclosing it. The gap between the two created tension that the policy alone couldn’t resolve.LinkedInLeon Lam | A-Level Head of Humanities | Beijing National Day SchoolLeon teaches A-Level economics and leads Humanities at Beijing National Day School. Last year, he vibe-coded a Socratic essay coaching chatbot designed to slow students down and move them through idea generation, outlining, and drafting as distinct stages. He’s candid about what happened. Some students engaged deeply. Others focused entirely on getting the chatbot to advance to the next stage, treating compliance as the goal. He reflects on what he’d do differently next time. His biggest takeaway is that co-designing a process with students can be a powerful way to make the process less performative and more purposeful in supporting their work. LinkedIn This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit aienhancedprocesses.com

Scaffolding is one of those practices most educators have been trained to use, talk about as a part of daily planning, but might need to reconsider now that we live in the age of AI. We’ve been using it for a long time: breaking down a complex task, modeling a thinking move, offering a hint when a student gets stuck, then stepping back as they find their footing. But knowing what scaffolding is and implementing it with fidelity in an AI-enhanced classroom are two different things. When the support is too tight, too scripted, or never fades, scaffolding can stop being supportive of student learning and growth. In a classroom where AI is available, a student oriented toward completion rather than understanding is one click away from outsourcing the whole thing.So this article is about one question with two parts: what does strong scaffolding look like when AI is in the room, and how do we design for it deliberately? The research on effective scaffolding gives us the foundation. AI gives us both a powerful new tool and a new set of risks. Understanding both is what makes the difference between AI enhancing student thinking and replacing it.DefinitionsI find that Universal Design for Learning (UDL) and differentiated instruction (DI) are often used interchangeably with scaffolding, so I want to take a minute to explore the three of them in relationship to one another before we move forward. I imagine that the conflation comes from the fact that each involves a teacher adjusting something for a learner to be successful. But there are some nuances between the three, and to make it more interesting, all three could technically exist at the same time. Defining Universal Design for Learning (UDL)CAST, the organization that developed Universal Design for Learning, describes UDL as a framework for designing curriculum so it works for all learners from the outset. Before the unit exists, a UDL-informed teacher is asking: Why are we learning this? How will I present it in multiple ways? How will students engage with it? How will they show what they know? The three principles (engagement, representation, and expression) aren't checkboxes. They're what Katie Novak might call design orientations. Many teachers and school systems treat UDL as a synonym for accommodation: extra time, modified texts, assistive technology. Those things matter, but they aren't UDL. UDL isn't retrofitting the curriculum for students who can't access it. It's designing the curriculum so the barriers don't exist in the first place. Defining Differentiated Instruction (DI)Carol Ann Tomlinson, who is arguably a leader in differentiation, has stated for decades that differentiation is a proactive mindset that a teacher brings to planning. Before the lesson begins, a differentiated teacher is asking: Who are my learners? What do I know about where they’re starting? What pathways and options can I build in so the learning reaches all of them? Multiple approaches to content, process, and product; not different destinations, but different routes to the same one. Many teachers have accepted a version of differentiation that means reducing the task for students who struggle. Fewer requirements or something like that. Tomlinson calls this myth out directly: differentiation is more qualitative than quantitative. It isn’t giving some students less of the same assignment. It’s rethinking the nature of the assignment so it fits the learner while keeping the learning objectives intact.Defining ScaffoldingScaffolding is what happens once learners are in the room, and you can see what’s actually happening. Pauline Gibbons puts it precisely in her book Scaffolding Language, Scaffolding Learning. She writes: “Scaffolding is not simply another word for help. It is a special kind of help that assists learners in moving toward new skills, concepts, or levels of understanding. It is future-oriented and aimed at increasing a learner’s autonomy. As Vygotsky has said, what a child can do with support today, she or he can do alone tomorrow.”In practice, scaffolding might look like a sentence frame that gives a student the language structure so their thinking can do the real work. It looks like a worked example that shows the process, not just the product. It looks like gradual release: I do, we do, you do. A think-aloud where a teacher makes their invisible reasoning visible. A guiding question that narrows the cognitive load just enough to get a student unstuck without removing the challenge. Notice what all of these have in common: none of them lower the intellectual demand. Second, with scaffolding, the aim is that students gain a level of independence to implement learning strategies on their own or with peers rather than relying on the teacher. Bringing UDL, DI, and Scaffolding TogetherHere’s a Claude Artifact (screenshot also below) of my understanding between UDL, Differentiated Instruction (DI), and Scaffolding as supported by several texts and AIs.All three of these practices can occupy the same classroom, the same lesson, even the same moment. Consider a history teacher designing a unit on the civil rights movement. Before the unit begins, she thinks about how students will access primary sources, how they will engage with the material, and how they will show understanding through writing, discussion, or a visual product. That’s UDL doing its work at the design stage. Within the unit, she notices some students need more time with the documents while others are ready to move into analysis. For students still wrestling with the sources, she designs a close-reading process. For students ready to push further, she moves them into a comparison and argument-building process. Different actions, same destination. That’s differentiation. Then on a Tuesday, she opens a language scaffold bot she built in advance; one that knows the sentence starters, knows the argument structure, and knows its job is to practice with students until they can do it alone. A student who can’t connect evidence to a claim works through three or four cycles with the bot; it offers a starter, the student completes it, the bot pushes back gently, and the student tries again. By the end, the student has written the sentence. The bot didn’t write it. The teacher didn’t write it either, though she designed the whole condition that made it possible. The scaffold existed for six minutes. The student is ready to meet the standard and gains a sense of independence.Over-Scaffolding and The Learning PitWhen a teacher manages every step of a lesson, students follow the path but never make sense of the terrain. Ready-made answers lead students to reuse solutions rather than build reasoning. Frey, Fisher, and Almarode put it plainly in How Scaffolding Works: without sufficient fading, students develop a dependency on the supports provided and fail to reach independence. It's a little counterintuitive, but teachers need to allow students to sit in what James Nottingham calls "the learning pit"; that uncomfortable space of not yet knowing, which is where the real thinking happens. Tolerating that discomfort long enough for the thinking to happen isn't cruelty. It's the whole mechanism.Monday Ready Resource: Prompt for Learning Pit Coach When students get stuck, this bot helps them sit with the discomfort long enough to work through it rather than around it. A great addition to a “ask three before me” approach.COPY AND PASTE INTO AN AI BOT FOR STUDENTS: You are a coach for students who are stuck and frustrated. Your first job is not to ask a question. It is to acknowledge what the student is feeling. Tell them directly that being stuck is not a sign that something has gone wrong; it is a sign that they are in the middle of real learning. Be warm and specific: the discomfort they feel right now is the learning pit, and every person who has ever learned something hard has felt exactly this. Only after that acknowledgment ask them one question: what is one small thing you could try right now, even if you are not sure it will work? If they say they don’t know, ask them to describe what they have already tried. If they say nothing, ask them to try one thing, anything, and come back and tell you what happened. Do not offer solutions. Do not explain the concept. Do not tell them what to try. Your job is to help the student stay in the pit long enough to find their own way out. Normalize the struggle. Trust the student.Impactful scaffolding is responsive to the students in the classroom, their cultures, and their needs.Studies across math, literacy, and language education confirm this: scaffolds built around one cognitive tradition can exclude learners who don’t share it. Erin Meyer’s research in The Culture Map helps explain the mechanism. Low-context cultures like the United States expect meaning to be spelled out explicitly; the task, the steps, the expected outcome, all stated directly upfront. High-context cultures like Japan, China, and much of the Arab world expect meaning to be inferred, relationships to be honored before instructions arrive, and the whole to be understood before the parts are named. A scaffold designed around low-context assumptions doesn’t just feel unfamiliar to a high-context learner. It can feel disrespectful, as if the teacher is being too direct or blunt.And yet multilingual students don’t operate as fixed cultural types. Over a career working with international learners, I’ve seen students shift their communication norms depending on their language fluency, who else is in the group, and what they think is expected of them. As a supportive teacher, the best move is a genuine investment in knowing your students, paired with a process that keeps expectations the same while letting expression vary. The destination doesn’t change. Every student is working toward the same learning goal. What can look different is how they show the journey; one student writes a personal narrative, another builds a structured argument, a third talks it through before anything hits the page. The thinking and expectations beneath all of them are the same. This is where process-based learning has a real advantage: a framework like Think, then Generate, then Edit names categories of thinking rather than prescribing a single path or output. It doesn’t tell students precisely what to think, or how to think it, or how to show it. Instead, it offers students a structure for thinking that they can internalize and repeat. And because each stage is named, teachers can be deliberate about when and how AI fits into the process, and when it doesn’t. The class can move through the same process together, while individual students work with different levels of support depending on where they are.That same principle applies when AI enters the feedback conversation. A bot that opens by asking how a student prefers to receive feedback, before offering any observations at all, is doing something most fixed feedback rubrics never do: honoring the learner’s communication style before the content of the feedback even arrives.Monday Ready Resource: Prompt for Feedback CoachThis bot guides students through seeking, making sense of, and acting on feedback using the Acquire, Analyze, Act process. The prompt below can help scaffold independence by gradually returning decision-making to the student at each stage. The goal isn't fluency with the bot; it's fluency with the process, practiced first with AI support and eventually carried into peer feedback cycles without it.COPY AND PASTE INTO AN AI BOT FOR STUDENTS:You are a feedback coach working with a student through three stages. Before you begin, ask how they prefer to receive feedback; some want to hear what is working first, others want to get straight to what needs improving. Honor their preference throughout. In the Acquire stage, ask what they most want to learn from this feedback and what success looks like to them. Then ask them to share the feedback they received, or offer to give feedback yourself. In the Analyze stage, share one observation framed around their intention, then ask them to interpret it: what do they notice, what surprises them, what feels actionable? Do not tell them what to do. If they respond briefly, follow their lead and give them space. In the Act stage, ask them to name one concrete revision, make it, then reflect on what shifted and what they would seek feedback on next time. At every stage, the decisions stay with the student. Your job is to ask the question that helps them think one level deeper than they would alone.Metacognition is one of the best scaffolds.Metacognitive scaffolds that build in planning, monitoring, and reflecting produce stronger outcomes than those focused only on task completion. David Rock calls one planning move “prioritize prioritizing” in Your Brain at Work: deciding what matters most is itself a cognitive task that deserves deliberate attention before the work begins. When students know what steps to take and in what order, cognitive load drops. And as Frey, Fisher, and Almarode note in How Scaffolding Works, as students engage in deliberate practice with scaffolds and feedback, they develop habits that endure across time; what researchers call automaticity. The goal is for the process to become so familiar that students stop spending mental energy on understanding the instructions and start spending it where it belongs: on the thinking the task actually requires.Linking metacognition back to the “learning pit”, when we practice metacognition before a learning task, we can anticipate the mindset, the strategies, and the places that we can get stuck, which makes it feel like it’s not a surprise, and students will know how to respond to said challenges as they happen. Without foresight into those realms, students will react impulsively to a frustrating situation, and the last thing they will want to hear is “that’s just part of learning! Get used to it!” In that situation, your learners are going to want an answer, and AI might be one click away to help them out. So again, the path to independence is to practice metacognition ahead of a challenging task and ask students to anticipate pitfalls and strategies to overcome them. Monday Ready Resource: Article on MetacognitionI have an article about three AI-enhanced processes related to metacognition that connect to these ideas of planning, monitoring, and reflecting with AI that I mentioned above. Check it out below.Monday Ready Resource: Prompt for Foresight CoachThis bot helps students plan before they begin using the Foresight stage of the Hindsight, Oversight, Foresight process. The goal isn't fluency with the bot; it's fluency with the planning moves, practiced first with AI support and eventually carried into any challenging task without it. Teachers can scaffold with a responsive approach by adjusting how students interact with the bot as they gain fluency; more guidance early, more independence later. Students who internalize the process start guiding their own metacognition in new contexts, perhaps without their AI coach and instead talking through obstacles with a peer, or working through the same questions in a journal before a task begins.COPY AND PASTE INTO AN AI BOT FOR STUDENTS: You are a thinking coach helping a student prepare for a learning task. Do not help them complete the task. Begin by inviting the student to share anything about how they like to think through new tasks; some students like to see the whole picture first before breaking it into steps, others prefer to start with one concrete thing and build from there. Acknowledge their preference before moving forward. Then ask them three questions, one at a time: What is this task asking you to do? Where do you think you might get stuck, and why? What strategies do you already know that could help you work through those moments? After they answer all three, summarize their thinking back to them in a way that reflects how they described it, not just what they said. Ask if they want to adjust their plan before they begin. Always keep the decisions with the student.Opportunities and pitfalls of scaffolding with AIScaffolding is a way of supporting students. The key to this form of support is that we intentionally plan its fade over time. So, it would be a misnomer to say that AI is a scaffold. Well, no. The way in which we deliberately use AI with a plan over time to support learning is a scaffold. The point being that it’s entirely in how AI is used, and the best way to get there is a well-designed, clear process with a plan.Frey, Fisher, and Almarode describe distributed scaffolding in How Scaffolding Works as the in-the-moment support teachers provide while students are actively working; the nudges, questions, and hints that respond to where a learner actually is rather than what was planned in advance. They recommend a sequence: start with a question to check understanding, move to a prompt if that doesn’t unlock the thinking, then a cue, and only then a direct explanation as a last resort. That sequence is designed to keep the cognitive work with the student as long as possible.This sequence lives within what researchers call the Gradual Release of Responsibility; the movement from explicit instruction (I Do) to guided practice (We Do) to independent work (You Do). Clark is clear that it’s not linear; teachers move back and forth between stages depending on what students actually need.When a teacher tells a student, “AI is fine on this task,” the bot might help them skip the entire sequence and go straight to direct explanation. Seeking the path of least resistance is a human psychological trait. The move that works is specific: when you are brainstorming, you may use AI to push your thinking further. Not “AI is allowed.” AI supports this thinking move, in this way, at this stage. That is, teachers, name the thinking for each step of a process and how AI can support it. If you haven’t seen my post titled “How To Design AI-Enhanced Processes”, it’s worth checking out. It covers the above ideas bolded above. In it are simple ways you can design a process that names the thinking, sets expectations, and considers what evidence you would find compelling to demonstrate student thinking.The pitfalls follow the same logic as over-scaffolding more broadly. Frey, Fisher, and Almarode state in How Scaffolding Works that the most common error with graphic organizers is when filling out the organizer becomes the end goal: students turn it in, the lesson continues, and the opportunity to build schemas is forgotten. The same thing happens with AI. A student handed AI without a clear role or purpose probably won’t use it as a scaffold. More likely, they will use it as a completion machine. They finish without building the thinking that the task was meant to develop. And unlike a graphic organizer, AI is fast enough and fluent enough that the student may not even notice the thinking didn’t happen. The bots I shared in this article are designed to respond to each student in ways that honor their thinking and communication norms, asking questions before giving answers and holding back direct explanation as a last resort. But the bot isn’t the relationship. You are. Your role while students work with AI is to circulate, notice, and show genuine interest in what they are thinking. You are their biggest audience, and your curiosity about their ideas is what makes the process feel worth doing.ConclusionAs a teacher, what you’re watching for is curiosity, critical thinking, grit, and metacognition. Those are the signals that the scaffold is working and that students are moving toward an independent, self-directed mindset.Build a process that scaffolds agency with metacognitive routines. When we know those intentions and make them clear to our students, then we can invite AI into the learning. And when a student struggles, resist the urge to rescue them; instead, ask them if they anticipated this and what strategies they prepared. Phrases like “you know that it’s totally normal to be in the learning pit. Let’s take a minute to consider what options you have” go a long way toward establishing longer-term, sustained independence.A dependent, transactional culture teaches kids something, too; it just teaches them that struggle means stop, that help means answer, and that learning ends at the final report card. So scaffold with intention.AI DisclosureIn each article I write, I love to take different approaches in my process. Below, I have named my process and indicated when and how AI supported my writing. The feedback I have been getting from my readers is that these disclosures help them see how I model the practices I promote. TimeI think that it’s important to share how many hours I spend writing articles because I want people to know that it is not necessarily about saving time. I still spend 8-10 hours writing each article. My process is different because AI is a part of the journey and I have access to a wider corpus of research as well. This particular article took me about 8.5 hours to complete across three days. The most time-consuming things were ensuring it captured accurate research and practices, ensuring I endorsed the ideas, and that the language was my own.ProcessTo write this article, I spent three mornings waking up early, drinking some strong coffee, and going to work. I focused on getting the ideas down on paper, editing it once, then stepping away and editing it again with a fresh perspective. Since I often promote the approach of naming the thinking, I thought I would similarly share my steps here. Reflect, Write, Research (with AI), Edit (with AI), Edit (without AI by speaking with three fellow teachers), Record, Share.Update: I came back a month after publishing this article initially. I learned new information about differentiation and updated this article. That’s the wonderful thing about an article-based approach: it is a representation of my thinking that can evolve over time!Research with AIIn this step, I took my research questions and did a search in Consensus. While there is free access, I pay for it because I find that it greatly enhances my job as a coach, and I use it frequently. If I am going to recommend an instructional practice to a teacher, I want to know what the experts say. It’s a great way to get very specific questions answered with credible sources. On Consensus, I ran a report and summarized the relevant findings. I also included a couple of books I was familiar with and that were referenced throughout the article. It’s a reminder that good teaching practices have a considerable body of research already out there. The question I like to ask when doing this sort of writing is, how does AI fit into the well-researched and impactful practices of teaching and learning, if at all?Thank you for reading! This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit aienhancedprocesses.com

In this episode Joellen Killion joins the podcast and talks about what impactful feedback could look like as a practice as well as what it could look like in the age of AI. ⁠Joellen's Book on Feedback (link)⁠About JoellenJoellen Killion champions educator learning as the primary pathway to student success. She serves school systems, schools, regional, state, and national agencies within the U.S. and abroad as a consultant and learning facilitator. She is senior advisor to Learning Forward and formerly was its deputy executive director. Joellen leads, facilitates, and contributes to a number of initiatives related to examining the link among curriculum; leadership; quality instruction; professional development; and student learning. She has over 30 years of experience in curriculum development and implementation and planning, design, implementation, and evaluation of professional learning at the school, system, state, national, and international level. She was the recipient of the Don Deshler Leadership Award and the Adams County District 12 Merit Award. She serves on the advisory board for the Association for the Advancement of Instructional Coaching in International Schools and is a member of the editorial board of the International Journal on Mentoring and Coaching in Education.Joellen is a frequent contributor to education publications. Her books include What Works in the Middle; What Works in the Elementary Grades;, and What Works in the High School; Teachers Who Learn Kids Who Achieve: A Look at Model Professional Development; Assessing Impact: Evaluating Professional Learning, 3rd edition; Collaborative Professional Learning Teams in School and Beyond: A Tool Kit for New Jersey Educators; Taking the Lead: New Roles for Teacher and School-based Coaches; The Learning Educator: A New Era in Professional Learning; Becoming a Learning School; Coaching Matters; The Feedback Process: Transforming Feedback for Professional Learning.; and Elevate School-based Professional Learning. She authored and co-authored numerous papers, articles, reports, and workbooks such as PDK’s EDge, The Changing Face of Professional Development; A Systemic Approach to Elevating Teacher Leadership; and resources associated with the Transforming Professional Learning for Common Core Implementation initiative. She serves on the editorial board of the International Journal of Mentoring and Coaching in Education. Her particular interests are collaborative learning teams, coaching educator success, evaluation and program audits, standards for professional learning, policy to support professional learning, and comprehensive planning and implementation of high-quality, standards-based, results-focused professional learning. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit aienhancedprocesses.com

Want a summary of this article? Jump to the bottom where I have a one-pager waiting that is printable with the main ideas. Last week, I was standing in a 7-Eleven in Nara, Japan on spring break, and before setting off to explore the charming city, I stopped to buy an onigiri rice ball as a snack. While checking out of the 7-11, I remembered something from the time when I lived in Japan. My friend Soichiro taught me how to open onigiri about twenty years ago by following the numbers on the packaging: three tabs, a folded plastic wrap that keeps the seaweed crispy and separate from the rice until the exact moment you want them together. Precise folds, purposeful sequence, color-coding— to me, it was the kind of design that seemed to draw upon the wisdom of origami. Check out the video below of me showing the packaging of an onigiri and how opening it is easy and leaves the seaweed dry and crunchy. Fun fact: 7-11 wraps their packaging in bioplastics! Actually, Soichiro was not there the first time I tried to open one on my own. I just started pulling at the plastic like I was unwrapping a granola bar. I tore straight through the seaweed, the rice went everywhere, and I ate a slightly soggy, structurally compromised snack standing outside a convenience store, feeling very foreign. The packaging already had the answer, though; three numbered tabs, right there on the wrapper. The design was not the problem. I just didn’t stop to read it.Over the remainder of my trip, I kept noticing the same well-designed logic everywhere, from vending machines to train exit gates to conveyor-belt sushi restaurants. One of my favorite designs was a paper cup dispenser with a single button to release exactly one cup from a locked stack. I watched a tourist wrestle with that type of machine for thirty seconds before noticing the button. Back when I lived in Japan, I learned that when I struggled with something like a paper cup dispenser, the right response was to self-correct. That is, if something is difficult to open, use, or do, you’re probably doing it wrong. In Japan, the user experience is often carefully planned and meant to be easy. I came home thinking about teaching and learning, and I kept thinking: what if we applied the same logic to classroom instructions? Much like a wrapper with instructions, classroom instructions should be easy. The task should be where the energy is put. Students’ effort belongs to the thinking, not to decoding what you want them to do. In other words, opening the onigiri was not the point. Eating a delicious snack was. The packaging exists to serve the experience, and the best packaging gets out of the way quickly. Classroom instructions work the same way in that they are the vehicle for learning, and not the purpose or when learning happens.Picture a high school student with four classes, each coming with lengthy instructions and teachers who carefully cover every edge case before anyone touches anything. By the time a student opens a task on their computer, they are more glazed over than a honey-baked ham! And because we live in an age in which everyone is using AI, they’ve probably got their favorite model running in the background of their laptops. Once they reach the point that the instructions become overwhelming, the internal monologue becomes: I honestly couldn't care less. I'm exhausted. I just want to get through this. This classroom and day-to-day experience sets kids up to have a mentality that is vulnerable to AI misuse. Kids who feel less engaged and disinterested will want to complete tasks quickly, and AI can provide a shortcut. If your instructions lose them from the get-go, you’re heading in the direction of compliant task completion. Too much teacher talk that muddies the instructions might indirectly push them toward feeling overwhelmed and toward a desire to cognitively offload the task as efficiently as possible. My suggestion is this: get into the intellectually engaging, stimulating process of active learning in class. The better you can design your instructions to be short, verb-based, and clear, the better. If you are noticing friction with instructions, processes, or any element, that difficulty is highly informative and can help us to adjust.So in other words: difficulty is data.The Look on Their FacesA quick clarification before I go further. Direct teaching is a powerful tool (see Hattie’s work). There is absolutely a time to stand at the front of the room and teach. This article is not about that moment. This article is about when you ask students to do something, and you are explaining how to engage (e.g., create, discover, reflect, collaborate, analyze, build). The task is meant to generate learning, and before any of that can happen, you have to explain what to do. From my experience as a teacher and coach, fifteen minutes or less with an exemplar is the limit. When teachers overexplain instructions, it leads to a kind of glazed-over, fading anticipation mixed with compliance. It’s funny too, kids will avoid asking questions because they just want to get on with it, even though they actually have many things they want to ask you, they bide their time and plan to ask a classmate what they are actually supposed to do. Myth: good instruction means frontloading every common misconception and pitfall before students have touched the work. To be clear, anticipating roadblocks is good design; that is what Universal Design for Learning asks us to do. But there is a difference between designing for barriers and narrating all of them upfront before students have had a chance to think. When teachers over-explain every obstacle in advance, they usurp the learning; students never have to construct cause and effect for themselves because the teacher already did it for them. They arrive at the work with a head full of caveats and nothing left to figure out. That is not so different from handing a task to AI in that the thinking gets outsourced before it ever begins. Just as we don’t want AI to do the work for students, we also don’t want teachers to do the work for them either.I used to be the over-explaining guy: I’d hover while students work, point at their screens, announce new pitfalls I just remembered or noticed, and announce that there are thirteen minutes left. I would not necessarily call that a rich thinking environment; you know what kids are thinking in that situation? I’m going to just get through this block so I can go home and do it on my own, and I’ll just ask AI and my friends if I get stuck.Could you imagine if 7-Eleven sold onigiri that required 27 steps to open, and a lengthy training video that walks you through every possible way it could go wrong, and then you are given 13 minutes to do it, while in the back of your mind you know that you have a really important train to catch at the station? You would be exhausted, uninterested in the snack, stressed, and looking forward to the whole thing being over. If we are explaining the instructions to an activity and the students have their heads down, that’s data. It is the equivalent of struggling with an onigiri wrapper. It does not mean your students are necessarily unprepared. It could mean your instructions have friction in them, or the students are just not paying attention due to distraction, confusion, or feeling overwhelmed. Every minute a student spends decoding your instructions is a minute they are not spending on the actual thinking you designed the task around. That thinking, the brainstorming, the analyzing, the revising, the reflecting, is where the learning happens.Teachers are designers who are constantly testing their products and empathizing with their clients. So with that design thinking mentality, when students look lost before the learning starts, we can think of this as an observation in which we ask ourselves: what did I build here? What can I subtract? How can I activate thinking and step out of the way? How can I provide just-in-time feedback?Monday-Ready MovesHere’s a list of a few strategies that I have seen work as a teacher and coach. They directly support process-based learning in that a strong process can actually serve as clear instructions that do not necessarily require lengthy explanation. 1. Limit teacher talk. Read your instructions once and keep the total instructions to 15 minutes or less. The shorter your instructions, the more energy your students will have. If you are still talking after 15 minutes, something needs to come out, or additional instructions can happen later in the same lesson. Again, this is not for direct teaching in which essential information has to be taught; I’m talking about the instructions for an activity.In terms of designing a slide, make the words large and easy to read from across the room. Don’t write all the instructions, just the main points so they can recall what they’re supposed to do. 2. Lead with an exemplar. Show before you explain a model paragraph, sample sketch, before-and-after comparison, etc. When students can see the destination, your words serve as confirmation as they build theories about the task and its outcomes, rather than as orientation. 3. Use verbs to name the thinking. Replace vague nouns with precise action verbs. Not “work on your essay” but argue, support, challenge, revise. Not “think about the data” but interpret, compare, decide. Verbs tell students what their brains are supposed to be doing. They also support clear expectations about where AI can or cannot do the move for them (#4 below). For more independent students, you can also ask them to engage in metacognition before starting by considering which steps in the process would be most strategic for meeting the learning objective, then, as they are ready, proceed with their own. 4. Name the AI expectation for each step. For every thinking move, students need one clear statement: what do I do, and what does AI do here? For example, “AI will give you counterarguments, debate it, then record your key findings in your process journal.” Another example could look like, “No AI on this step; this is your thinking.” Vague AI expectations can invite interpretation. A single sentence per move removes the guesswork and keeps the cognitive effort where you want it: on the learning, not on figuring out the rules. For older and more independent students, you can also co-design AI agreements together. Students also tend to like when teachers allow AI on a step in a process that they create a purpose-focused bot for them to use (e.g. on School AI, Flint, etc.) That way they feel less anxious about misusing AI on the task. Students all have access to AI and will likely use it at some stage, but you can make doing the right thing easy.5. Tell them “the why of the how”. Once students know what they are doing and how AI does or does not support each step, add one more sentence: why this particular approach? “We are using journals here because writing slowly pulls thinking out of your head and away from the distraction of your screen.” Another example might be: “We are using AI to role-play as an audience member so you can practice your speech and feel confident before you perform it for real.” Using that language is a direct signal a teacher can send: I thought carefully about how you learn, and I chose this because I want you to succeed. That message contributes to belonging, trust, and independence because it helps students see a process as something they can actually use again on their own.ConclusionI am not saying instructions should be dumbed down. I am saying they should be carefully designed, succinct, and clear to help students access the thinking that leads to deeper understanding. Dumbing down removes the challenge and presupposes students’ incapability— what you might say is setting low expectations. Focusing our language during instructions removes the confusion so the students can engage in the process and put effort where it matters.Students should struggle with the counterargument, wrestle with the revision, sit with the discomfort of a claim that does not quite hold up yet. That productive struggle is where growth happens, and it is worth protecting. Let’s help students put energy into that thinking and not want to turn to AI for task completion. Look for signs that students are thinking. They are writing, discussing, touching their faces, drawing, reading, etc. If they move immediately into the thinking, you have built something that facilitates their thinking. If they look low in energy, slumped down, or frustrated, change something.The second time I opened an onigiri, even with Soichiro showing me how, I still tore the seaweed a little, and that moment of friction is exactly what made the experience stick. I learned from it and gained independence from Soichiro as my teacher. I never tore it again and actually went on to show my friends how amazing it was to eat.When it came to that onigiri, though, the packaging was never the problem. The instructions were printed right there with three numbered tabs. When I tore the seaweed, it was not because the design failed me; it was because I jumped straight in without reading. The moment I paused, or when I had an example to follow (Soichiro modeling it), it worked. That is the other half of the teacher’s job. Write instructions that are clear, yes. But then create a pause, ask about their clarity, read them aloud together, point to the exemplar, ask students what they will do first, second, third. Give students a moment to actually look before anyone touches anything. The packaging can be perfect and still get ignored if nobody stops to read it first. When they struggle with the task itself, that is not a problem; in fact, that is the point! Struggling with a counterargument, sitting with a claim that does not quite hold up, wrestling with a revision that keeps slipping, that is productive. That difficulty is data too. It is just pointing at the learning instead of the design.AI DisclosureThis article was started with Claude 4.6. It started with me dictating long and disjointed ideas on a train leaving Osaka, using voice-to-text as a way to capture a loose set of noticings and half-formed ideas\. I shared the transcript with Claude and used the conversation to brainstorm, push back on my own thinking, and gradually move from a vague noticing into something more structured.Then I took a two-hour Shinkansen up to Tokyo and looked at misty mountains and patches of cherry blossoms along the way. My favorite German electronic music, Apparat, on headphones. I had time to think, write, and look out the window at nothing in particular, which turns out to be one of the better conditions for getting ideas to settle into something real.I want to be transparent about what that human-AI collaboration looked like, because I think it matters. The ideas in this article are mine; the experiences are mine; the framing is mine. Claude helped me organize, sharpen, and refine them. There were moments it wanted to take things in a different direction, toward UX frameworks I did not need, or toward sensory details that sounded vivid but making my writing verbose.I share this because I believe we should normalize transparent disclosure of AI in writing, especially those of us asking students to do the same. If you are not disclosing, you could be modeling secrecy. I wonder if I were to name my process, it might look something like this: * Speak. Use Claude to organize your verbal ideas. Think outloud and share what you are currently thinking, then the AI can help you to organize them into a narrative outline. * Develop. Take the outline and write your ideas out. Do this step without AI.* Edit. Take your first draft and show it to Claude. Ask it what it thinks about your flow and connection of ideas.* Revise. Independently on a bullet train in Japan, re-read your draft a few days later with a fresh perspective and consider your draft. Edit it using your expertise as a teacher and ensure the article has clarity for your given audience of educators. * Share. Schedule your article to go out on Monday morning to share with your community.After the content was written, I went back to add extras to make it more engaging like pictures for each section and a video of me opening the onigiri. The video was me in the streets of Nara actually opening a tuna and mayo rice ball, but the audio was enhanced using Adobe’s Voice Enhancer. The pictures throughout the article were cited with the model and process I used in their creation; all were generated using ChatGPT or Gemini. I also want to state for the record that the en and em dashes used in this article were all me! I’m reclaiming them! If you want to try building your own process, I have a free tool at aiep.lovable.app that walks you through it step by step. One Pager of This ArticleIf you enjoyed this article and want to share it with someone else, here is a one-pager summarizing its content. Download it, print it, email it to a friend who you think would appreciate it, or you could directly share the link to it! Thank you for reading! This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit aienhancedprocesses.com

In this four-part episode, Alex has an interview with five different guests who share their insights on using AI to meaningfully help students to write. Key ideas that emerge: grading chats can be fun and insightful, writing is a form of thinking, process and product are important, it's possible to write with AI and still know your content, and much more. Below are the details about this episode's guests:Mike Kentz is an award-winning educator and former journalist with 15 years' experience across teaching and news media. He is a TEDx Speaker and the founder of AI Literacy Partners, a professional development and curriculum design firm that aims to build AI literacy in educators and students through high-quality instructional materials. His work in AI and Education has been featured in The Harvard AI Pedagogy Project, EdSurge, The Writing Across the Curriculum Repository from Colorado State University, The Wall Street Journal, and more. He lives in Morristown, New Jersey, with his wife, son, dog, and cat.With over 27 years dedicated to advancing educational excellence, Eileen Heller serves as an Education Consultant for Professional Learning at ESU #3, supporting 18 diverse school districts across Omaha’s metro communities. Her career journey—from sixth-grade classroom teacher to technology specialist, instructional facilitator, and instructional technology trainer for Omaha Public Schools, as well as adjunct instructor for multiple higher education institutions—has equipped her with a deep understanding of how to design and sustain impactful systems of professional learning. Her varied experience has led her to focus on building effective professional learning systems. She is committed to supporting educators’ growth through collaboration and encouraging self-directed solutions that improve student outcomes.Chase Heller is beginning his freshman year of high school and enjoys staying actively involved in both his school and community. He serves on the student council and volunteers whenever possible. Passionate about athletics, Chase runs cross country and plays soccer, consistently working to improve his fitness and teamwork. In his free time, he enjoys walking his dog Lucky, swimming, playing with his brother McKennon, and spending time with friends and family.Amelia King is the Director of Digital Transformation at one of the UK’s leading independent schools, where she helps educators navigate new technologies without losing sight of deep learning and student wellbeing. With a Master’s in Smart EdTech and Co-Creativity, she has researched how students think when using AI, sharing her findings at international conferences and through her widely read newsletter for educators. Amelia mentors colleagues worldwide, teaches her “Thinking with AI” course, and speaks regularly about the need to blend artificial and human intelligence in education. Known for translating academic research into practical classroom strategies, she is passionate about ensuring that technology lifts attainment, deepens learning, and protects the well-being of both students and teachers. Learn more about her work at amelia-king.com.Andrew Easton is an education speaker, author, and consultant specializing in personalized learning, artificial intelligence in education, and learner engagement strategies. He serves as the Digital Learning Coordinator for Nebraska’s Educational Service Unit Coordinating Council, supporting schools across the state with innovative technology integration. A former classroom teacher with more than a decade of experience, Andrew has delivered over 50 conference presentations and 125 professional development sessions for educators across the U.S. and Canada. He is the author of Empowered to Choose: A Practical Guide to Personalized Learning and the host of The Good Life EDU Podcast, where he explores the latest ideas shaping the future of teaching and learning. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit aienhancedprocesses.com

TL;DRII’m back after a break for Chinese New Year!Process-based teaching is meant to protect thinking. With AI in the classroom, it can accidentally protect the appearance of thinking instead. But before any process can hold, the essential conditions for learning have to be in place. This article looks at both through a self-determination lens: the load-bearing walls a classroom needs, and the six predictable ways a process still breaks down even when they’re standing.IntroIf you’ve seen me speak, you’ve heard me say that AI doesn’t create new problems. It reveals old ones. Take one that’s been hiding in plain sight: we have long valued the static artifact of a finished essay over the messy, cognitive act of actually constructing an argument. When a student can generate something that looks finished in seconds, old habits get exposed. When the essential conditions for real learning aren’t in place, students learn quickly that what matters is to look finished, not to think deeply, or even to know what that looks like. And when a dysfunctional system meets a tool that can produce polish without effort, it shows its hand.Process-based teaching is meant to protect thinking. In the AI era, it can accidentally protect the appearance of thinking. And here’s the uncomfortable part: most teachers think of themselves as process people. They believe in the work. They value thinking over polish. The problem isn’t bad intentions or misplaced values. It’s the behavioral implementation gap between believing in process and actually designing for it.That gap is what separates an AI-enhanced educator from a slop-enabling one. Not philosophy. Design.This article is a list of the six predictable ways process-based learning breaks down with AI, plus the early look-fors that tell you it’s happening.Before we start, great process design requires the essentials to be in place first. I didn’t invent those essentials. Deci and Ryan’s “Self-Determination Theory” (SDT) names three of them: autonomy, competence, and relatedness. That third one is worth pausing on, because relatedness is the term that tends to trip people up. In SDT, it refers to the felt sense of being connected to the people around you, of mattering to others, and having others matter to you. But in a classroom context, relatedness fails in two distinct ways that are easy to confuse. A student can feel genuinely cared for by their teacher and still have no personal stake in the work. And a task can connect to the real world and still land in a room where a student doesn’t feel seen or cared for by their teacher.So I’ve split relatedness into two walls, drawing on Zaretta Hammond’s work in Culturally Responsive Teaching and the Brain: belonging, the felt sense that a student is known in the room, not just present in it, and relevance, my own interpretation of her ideas around cultural connections to learning: the sense that this particular work has something to do with a student’s own life, community, and experience.So: Belonging. Competence. Relevance. Autonomy. These are the load-bearing walls of a classroom, as I see it. A student who doesn’t feel seen will feel invisible in the learning. A student who finds the task too difficult will use AI as relief. A student with no personal stake in the work will use AI as the fastest way out of what looks like a checklist. Process design without these walls just creates more elaborate hoops to jump through.So the conditions come first. But here’s the harder truth: even when they’re strong, a process can still fail. Small design mistakes compound fast when “finished work” is one prompt away. That’s what this article is about. Here’s how processes break.Six Common Pitfalls of AI-Enhanced Processes 1) Vague language leads to vague behaviorEvery breakdown in this pitfall starts with unclear communication. When teachers say “AI allowed” without defining what that means, and when schools frame AI as “just a tool” without examining what that metaphor actually teaches, students are left to interpret expectations that were never made explicit. That’s not an academic honesty problem, though many teachers see it as one. For many students, it’s actually a communication problem.“AI allowed” without a definition leaves students guessing. Some use AI for idea support. Others outsource the whole task. Mismatched voices, “gotcha” moments, and disputes are the predictable result of thinking expectations that were never stated clearly in the first place.The same problem lives in the metaphors we use in the classroom. Tool language is genuinely useful early on. It makes AI feel manageable, puts responsibility back on the human, and gives schools a practical framework for early questions: What is this for? Who should use it? When does it help? But tools are neutral. Generative AI is not. It pushes back, persuades, and carries patterns from its training data in ways a hammer never could. Framing AI as a guest collaborator picks up where tool language stops, and it carries a different set of values with it. A guest has a role, operates within norms, and is welcome but not in charge. But more than that, a guest changes the nature of the work. Learning isn’t transmitted from AI to student. It’s constructed through the interaction: the push and pull of a draft that gets questioned, an argument that gets challenged, an idea that gets stress-tested and comes back stronger. The guest doesn’t write your essay. The guest reacts to your draft, pushes back on your argument, and asks what you actually meant. That’s a co-learning relationship that we like and want to continue to instill in young people. There’s a relevance cost to vague language, too. When task language is generic, it signals that any student could have received this assignment. It doesn’t invite students to bring their own experience, community, or perspective into the thinking, and it doesn’t honor or celebrate the families, values, and identities they carry into the room. A student who doesn’t see themselves in the work has no particular reason to do the cognitive heavy lifting when AI can produce something plausible without any of it.Spot the pitfall early:* You might see “AI allowed” in the task brief, and nothing specific beyond that. Fix it with one concrete allowed/not-allowed example and a short why. Then ask yourself one more question: does the task brief invite students to bring their own experience, community, or identity into the thinking? Generic language and vague AI permissions tend to arrive together, and they send the same message: this work wasn't designed with you in mind. Students need to see what your expectations look like in action, and they need to see themselves in the task.* You might hear students say “I just used it to clean it up,” “you never said we couldn’t,” “just tell me what to do,” or “does it have to be in my own words?” Fix it with precision: name the step in your process where AI enters, and name who owns the decisions. “In this step, use AI to draft two alternative structures. In the next step, the decisions are yours.” And model it first: “Here’s how I used AI this week and what I noticed.” That positions you as a co-learner and shows students what honest reflection on AI use actually sounds like.* You might notice students describing AI's role in completely different ways in the middle of a task that you thought had clear expectations. That's not a dishonesty problem. It's a norming problem: the class never built shared language around what AI's role actually is in this process. Fix it by making that conversation public. Post a one-sentence class agreement on the wall: “In this task, AI is a guest at the drafting stage. The decisions stay with the writer.” 2) Documentation serves policing rather than learningA process designed to protect academic integrity isn’t automatically a bad thing. But when documentation serves the teacher’s need to prove honesty rather than the student’s need to see their own growth, the purpose gets inverted. Breadcrumbs that could show a learner how far their thinking has traveled become evidence in a case. A folio that could make effort visible and meaningful becomes a paper trail. And students who already weren’t sure they belonged in the room now have confirmation that they’re being watched rather than supported or coached. They’ll invest in not getting caught rather than in the learning.The better question isn’t “how do we catch them?” It’s “what have we designed that makes outsourcing feel logical?” If the assignment is product-only and thinking never has to show up during the work, detection won’t fix the design problem. A task that hides thinking produces students who hide AI use. That’s a design flaw, not a character flaw. The stronger move is a process so intentional that AI use is built in, expected at a specific moment, and tied to a specific cognitive move. When the teacher defines where AI enters and what the student has to do with it, there’s nothing to hide. There’s also no ambiguity about where the effort needs to be exerted. A rigorous system of thought (I love that phrase) doesn’t leave room for shortcuts because it already accounts for them.Spot the pitfall early:* You might see process steps treated as proof rather than support, and AI use that is hidden or defensive rather than disclosed. Fix it by making transparency the default: share the prompts you used to build a task, the output you rejected, and the decisions you made. Ask students to follow your example.* You might hear students say “I didn’t know we had to document that,” “the AI just helped me format it,” or go quiet when you ask them to walk you through their process. Fix it by building documentation into the process itself: “Your note should include one place where AI pushed your thinking and one place where you pushed back on AI.”* You might notice more energy going into catching AI use than into redesigning the tasks that produce slop in the first place. Fix it by co-creating norms with students and naming the purpose: “We document AI use so you can see your own thinking grow, not so I can audit it.” That shifts your role from enforcer to mentor.3) The process becomes a ritual without meaningA process with named steps isn’t automatically a thinking process. If students can’t explain why a step exists or what cognitive move it requires, they’ll complete it the same way they’d fill out a form: accurately and meaninglessly to efficiently complete the task. A student can brainstorm with AI, select an idea, and move on without making a single deliberate cognitive move. The brainstorming happened, but the thinking didn’t.Remember the walls of the classroom in the opening? Well, in the case that students follow a process without effort, it’s likely a competence issue. A student who can follow steps but can’t name their thinking doesn’t feel capable; they feel procedurally compliant. And a student who can’t see what their effort is actually producing has no reason to invest more of it. One example I have experienced as a student is “reflection”. It was something many of my teachers had me perform over my academic career and while it was a powerful learning strategy, I wouldn’t say I ever understood why it was important or how to do it well. Today, students could easily say: bah, I’ll just get ChatGPT to whip something up and skip this nuisance of a reflection, it doesn’t offer me new insights.Spot the pitfall early:* You might see students complete every step but struggle to explain what thinking they actually did, or why the step mattered. Fix it by adding a “name the thinking” prompt at transitions: “Before you move to the next step, write what you just decided and why.” * You might hear students say “I just did what the step said” or “I wasn’t sure what I was supposed to be thinking about” when you ask them to walk you through their process. Fix it by giving students language they can actually use: “I accepted this because...” or “I pushed back here because...” A shared anchor chart of thinking moves with verbs and brief definitions externalizes the invisible. When students can name what they’re doing cognitively, they’re more likely to do it intentionally. Without that language, thinking stays implicit and the step stays mechanical.* You might notice transition points passing without anyone surfacing what just happened cognitively. Fix it by talking with students about why certain thinking moves matter: “Why do we reflect? How does it help us hold onto what we just experienced?” When students understand the science behind the step, the step stops feeling like a hoop.4) Documentation as post-mortem autopsiesThis pitfall is related to the second one but distinct from it. Pitfall 2 is about why documentation exists: when it’s designed to catch rather than coach, it becomes policing. This pitfall is about documentation happens: even well-intentioned documentation fails if it arrives too late. A teacher can genuinely want to support student growth and still fall into this trap by collecting everything after the work is done.This is where autonomy matters, and where formative assessment becomes the practical alternative. Documentation that lives after the work is done belongs to the teacher’s evaluation, not the student’s learning. But documentation designed as connected tasks during the work, each one building on the last, creates something different: a visible record of thinking developing over time. That’s where feedback does its most powerful work. Not as a judgment on a finished product, but as a response to thinking in motion. A teacher who can see a note about a decision from day one, an annotation from day three, and a concept map from day five can give feedback that actually moves the learner forward. Breadcrumbs are invitations for formative response.Spot the pitfall early:* You might see process artifacts that only exist at submission, with no thinking trail built during the work itself. Fix it by requiring tiny breadcrumbs in the moment: “Annotate one place in your draft where you disagreed with AI and explain what you did instead.” * You might hear students say “I already finished it” when you ask for a decision note, or notice that the only documentation in the room is happening at submission. Fix it by building checkpoints during work time: “At the halfway point, drop a decision note: what have you changed since you started and why?” Breadcrumbs built during the work are invitations for formative response. * You might notice grades that still signal product matters and process is optional, with no weight given to how thinking developed over time. Fix it by including a short “defend your choices” moment: “In your pair share, explain one decision your breadcrumbs show that your final product doesn’t.” Consider delaying the grade until the process is complete and feedback has been acted upon. 5) The process is too rigid or too looseUnder pressure for certainty, processes start to resemble algorithms: fixed sequences meant to produce predictable results. Every student does the same steps in the same order at the same pace. But the opposite failure is just as common. A process with no real structure or access to the teacher leaves students guessing what the teacher wants, and when students are confused, AI becomes the fastest way to resolve the uncertainty. Both failures produce School Slop (this term refers to student work that is AI-generated rather than AI-enhanced; output that looks complete but reflects no meaningful cognitive effort.) One produces slop through compliance, while the other through perceived abandonment.The balance isn’t a middle point between structure and freedom. It’s a design that places cognitive load on the thinking rather than on figuring out what’s expected. A well-designed process gives students clarity about where they are, what they’re doing next, and where they can get support. It doesn’t predetermine where they’ll end up. It scaffolds the journey while leaving room for the thinking to go somewhere real.This is an autonomy and competence problem. A rigid process communicates that student judgment doesn’t matter. A loose one communicates that the teacher isn’t invested enough to be present. Both erode the walls of the effective classroom. What students need are frequent check-ins, strategic feedback at the right moments, direct teaching, and a clear understanding of how AI supports the class's goals without replacing the cognitive work that builds capability. It’s so much more than content.There’s also a longer-term goal on the horizon worth designing for. The goal was never compliance with a process. It was students internalizing the thinking moves behind it, adapting them to new contexts, and eventually designing their own. A student who has genuinely absorbed a process doesn’t need the scaffold anymore because the scaffold has become instinct. That’s also where wise AI use lives: not in following rules about what AI can and can’t do, but in a student who knows their own thinking well enough to know when AI is enhancing it and it’s replacing it. Spot the pitfall early:* You might see every student moving through the same steps at the same pace, regardless of where their thinking actually is, with no room for a different interpretation to land. Fix it by designing for clarity about expectations, not predictability of outcomes. Students should know what’s expected at each step, but the process should leave genuine room for their thinking to go somewhere unexpected. Turn steps into decision points: “If your draft is coherent, move to revision. If it’s messy, use AI to propose two structures, then justify which one you chose and why.”* You might hear students say “I don’t know what you want” or “can I just use AI to figure out the next step?” Fix it by being present during the work and building in frequent check-ins: “At each checkpoint, I’ll spend two minutes with each group. Come ready to tell me where you’re stuck.” Strategic feedback at the right moment matters more than a perfectly sequenced process. Make it explicit how AI supports the class’s goals: students should be able to answer what AI is helping them do and what thinking stays theirs.* You might notice students who can follow the process but can’t explain why any step matters, or students who turn to AI the moment the structure gets ambiguous. Fix it by planning for fading explicitly: “By week four, students design their own process for this task type using the thinking moves we’ve practiced together.” We want students to internalize the thinking moves well enough that the scaffold becomes a natural part of how they operate.6) How we assess learningThis is where every other pitfall converges. Undefined AI use produces polished work with no thinking behind it. Policing replaces the coaching that would have caught it early and kept thinking with the students. Silent steps mean students completed the process without understanding it. Post-mortem documentation gets retrofitted to match the product. Rigid and loose processes push students through without checking whether understanding is being built. And now, at the end, if the only thing being evaluated is the final product, every one of those design failures gets rewarded and reinforced with a grade. A rubric that can't tell the difference between a thinking student and a polished AI draft isn't evaluating learning. It's evaluating presentation. This is a relevance problem: when the grade doesn't value thinking, the assessment tells students what actually mattered, and it wasn't their ideas.Thinking = effort. When thinking doesn’t show up in the grade, students get a clear signal: the cognitive work doesn’t count. And a student who has figured out that thinking doesn’t count will find the fastest path to a product that looks like it does.The fix is a holistic evaluation: criteria that only a thinking student can meet. Justified decisions. Evidence of revision. Original connections that held up in conversation. A student who can explain their choices in a two-minute conference is demonstrating something a polished AI draft never could.Spot the pitfall early:* You might see AI-first drafts becoming final drafts because revision is optional, and process evidence that gets collected but carries no weight in the grade. Fix it by grading process and product, even lightly, so thinking has perceived value: “Twenty percent of this grade is your decision trail: where you changed direction and why.” Make revision count explicitly. A revised draft with a rationale earns more than a polished first attempt. * You might hear students say "I don't remember why I changed that" or "the AI suggested it and it seemed better" when you ask them to explain a decision in their own work. Fix it by building a process journal from the start so thinking is captured as it develops, not reconstructed after the fact. Example assignment prompt: "Alongside your final product, submit a journal that shows where your thinking started, where it changed, and where you landed." Example conference prompt: "Open your journal. Walk me through one moment where you changed your mind." * You might notice a rubric that rewards polish and completeness more than decisions and reasoning, where a polished AI draft scores the same as a well-thought-out one. Fix it by adding criteria only thinking can meet: “Your rubric includes one criterion your final product alone can’t satisfy: explain one original connection you made that AI didn’t give you.” Monday-Ready Design Checklist for Process-Based Learning with AIEach pitfall above comes with a concrete fix. If the look-fors resonated, here’s a free downloadable checklist PDF that pulls all six into one diagnostic you can use before your next learning task with AI in the room. Print it, post it, share with a colleague, bring it to a planning meeting with your team, or keep it on your desk. Five Readings Worth Your TimeSelf-Determination Theory, by Edward Deci and Richard Ryan, is the research backbone behind the load-bearing walls I describe in this article. What I appreciate about their work is how cleanly it names the psychological conditions for human motivation and growth: autonomy, competence, and relatedness. It’s a simple framework that holds up in classrooms every day.“Tomorrow is Today: The classroom I’m afraid of”, by Alex Kotran, is one of the clearest pieces I’ve read on AI in education. He warns that AI doesn’t automatically create equity for all; in fact, it can entrench inequity if we keep defaulting to a “pedagogy of poverty” and skip serious teacher training.10-25, by David Yeager, connects nicely to the Carol Dweck mindset work I’ve mentioned in other posts. Yeager is a contemporary and collaborator of Dweck’s, and he makes a great point about what young people are scanning for constantly: belonging and respect. The Paradox of Choice: Why More Is Less, by Barry Schwartz. This book connects directly to the autonomy wall and pitfall 5. Schwartz makes a counterintuitive point that shows up in classrooms every day: more options do not automatically create more freedom. Too many choices can overload students, increase second-guessing, and push them toward avoidance or shortcuts. Culturally Responsive Teaching and the Brain, by Zaretta Hammond, reframes relevance as a neurological condition, not a motivational one. Hammond’s argument is that the brain won’t take on the cognitive risk of hard thinking unless it first feels safe and seen. If students don’t sense that a task connects to something real in their lives, the brain treats it as low-stakes noise and looks for the path of least resistance. That path, right now, is AI. AI DisclosureThis article took 9-10 hours to write. There was a lot of effort put into it in the form of brainstorming, researching, revising, word-smithing, editing, and creating visuals.The ideas are mine, mostly drawn from my book and previous articles as well as the literature that I listed in the reading section above.I used Claude Opus and Sonnet 4.6 and Gemini 3 Pro with Deep Research to test my thinking against the broader literature, then worked section by section with AI as a collaborator. I found Claude Sonnet 6.2 did the best job with understanding the intentions and giving me feedback on how to adjust and make it digestible for readers. Then I used ChatGPT 5.2 Thinking to check that it still sounded like me. ChatGPT was sort of my top-level editor that I ran everything by to make sure it all made sense and the thread running through the article was clear.Thank you for reading my article! If you liked it, please share it with someone who you think would enjoy its content. See Me SpeakThis academic year, I have a few more public speaking engagements lined up where I will be speaking about the above ideas and more. I hope to see you in person and talk more about AI-enhanced processes!* 21 CL, Hong Kong - Breakout sessions* AIFE, Yokohama - MC and breakout sessions* AI in Action, Beijing - Keynote and breakout sessions This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit aienhancedprocesses.com

In this episode, I have three chats with different international educators who are working with AI and assessment in different contexts. My previous episode on assessment was one of my more popular, so I thought it was time to come back and see where we were at in terms of thinking that might be developing or getting more refined. It’s been a year since we recorded the last episode. Wow, time flies! Let’s take a look at the details of what you can expect and the folks joining me in order of appearance in the show. Emily J. Thomas is an educator, educational consultant, and entrepreneur who supports international schools in strengthening curricular development, coherence, and a clear vision for teaching and learning. She has spent over a decade in IB international schools as an MYP/DP English language and literature teacher and, most recently, served as an MYP Coordinator; she’s also an IB Educator Network workshop leader and a DP Literature examiner, and works as a literacy strategist with Erin Kent Consulting (EKC). Alongside her work in schools, Emily founded Playground Pedagogy (“playful minds, serious learning”) and leads yoga-focused work through Teaching Matters Yoga and Drift Yoga in Bangkok, and she writes the weekly Substack Elsewhere, Examined.In this conversation, Emily reframes assessment as an opportunity to extend learning; a way to “tune in” to what learners have actually acquired, not a checkbox to end a unit. She unpacks why formative vs. summative terminology can create anxiety and mixed signals for students and argues for schoolwide clarity, including shared definitions, consistent language, and policies that treat formative evidence as meaningful rather than “worthless.” Turning to AI, Emily’s message is “process first”: the best response is doing the fundamentals well with simple, standardized task sheets and clear expectations (including what AI use is appropriate) that teachers and students see consistently across classes. She closes with empathy for educators navigating this moment and a call for leaders to “steer the ship” with clarity so teachers can feel calm and supported.Timothy Cook is an educator and the founder of Connected Classroom, exploring how AI shapes student cognition and learning. He currently teaches third grade at the American Community School in Amman and writes Psychology Today’s “Algorithmic Mind” column, where he examines the intersection of education, AI, and human cognition, especially the risks of dependency and what schools can do to protect critical thinking, creativity, and moral development.In this conversation, Tim argues that writing still matters more than ever because it’s fundamentally a process of thinking: the focus, word choice, revision, and self-argument that helps students clarify what they actually believe (and that AI can’t authentically replicate). He introduces the idea of “jagged edges” that include the human, lived, imperfect uniqueness that gets flattened when AI produces the same “academically average” response to predictable prompts. From there, he makes a practical case for “AI-proofing” assessment by redesigning tasks around community, identity, and design: prompts where students must apply content in locally grounded ways (and where AI can still be used as a tool without replacing the thinking). Nick Soentgerath is a Technology Learning Coach at Yokohama International School (Japan), where he supports teachers and students in designing practical, future-focused learning with a strong emphasis on ethical, responsible, and safe use of AI. In our conversation, Nick brings a practical, classroom-grounded lens to what assessment can be when it’s less about “gotcha” grading and more about clarity, feedback, and growth. Helping schools move from measuring learning to actually improving it. He also presents at international conferences and works with educators on assessment practices that are more authentic, equitable, and aligned with the skills students need beyond school. In the episode, Nick and I discuss the upcoming conference at his school. Find out more here: www.AIFE.community. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit aienhancedprocesses.com

Inviting AI into your classroom and simply saying, “Use it responsibly,” is like inviting a wacky stranger to your dinner party and saying, “Just don’t make it weird.”Nobody knows what that means. You turn your back for five minutes to grab the wine, and you come back to find the stranger reorganizing your spice rack by scent and trying to sous-vide the family cat. Everyone is staring at you, and you’re left scratching your head, trying to reverse-engineer the chaos from the mess.That’s the problem with vague instructions: they invite interpretation. As Katie Novak points out in the foreword to the forthcoming second edition of my book, AI-Enhanced Processes, being a host isn’t just about opening the front door; it’s an intentional design. In a classroom, we’re hosting a digital guest in a room full of humans. If we don’t facilitate the introduction, the humans don’t know how to engage, and the guest, with all its enthusiasm and desire to please, takes over the party learning experiences.That’s basically the current state of AI school policies. Often, they're labels that sound decisive but rely on conceptual language. Many teachers are still unsure how to put that language into practice. Don't get me wrong, schools need a policy that establishes a shared language for how stakeholders will engage with AI in classrooms. However, action plans, practice, professional development, and coaching are the necessary next steps. Without the practical implementation plan, we likely will encounter selective interpretation: a teacher thinks “use AI only for brainstorming” when indicating AI is allowed. A student hears “drafting with AI is fine.” A school leader hears “future-ready learning.”We need a better way to host AI as a guest in our classrooms, and that means practicing our shared agreements, not just writing them.That’s where an AI-enhanced process (AIEP) comes in as one meaningful strategy to operationalize good practices. AIEPs are short, repeatable sequences of thinking moves that make expectations concrete. They keep the effort with the learner and give AI a clear role without letting it take over. Instead of “AI allowed,” we get shared language for action: here are the thinking moves, here’s how AI can collaborate on each one, and here’s what stays human.A strong AI-enhanced process includes these facets by design:* Multiple steps that break a complex task into smaller moves, so learning happens through a sequence.* A variety of action-oriented thinking moves (generate, analyze, evaluate, reflect) that build toward a larger outcome.* Visible thinking through drafts, notes, prototypes, and reflections so the work can be coached while it’s still happening. Process journals, folios, photos, Padlets, to name a few, are great ways to document student learning. It’s most powerful when it’s for the students first, and adults second. I will share more about this below, but I also have a two-article series you might enjoy. They touch on big ideas like efficiency and effort. Links below.* Explicit roles so each step clarifies what students do and how AI can collaborate without taking over like our wacky guest at the dinner party.* Embedded AI literacy happens through classroom dialogue and intentionally designed learning experiences, where AI shows up in ways that actually enhance learning. Students learn by doing. They build meaningful habits with AI when lessons use it to strengthen thinking, not replace it. Reflection helps students notice how the process actually flowed for them: where AI supported their growth, where it got in the way, and what they want to adjust next time. Over time, that cycle of practice and reflection becomes a lifelong strategy that students can carry into adulthood.In summary, AI-enhanced processes are strategies we intentionally design in conjunction with meaningful classroom dialogue about how we use AI with our students, which can lead to powerful learning experiences and lifelong strategies.Ok. Now that we have shared language for what an AIEP is, we can actually host AI in our classrooms. In the next sections, we’ll look at three powerful moves to build your own processes that take the above ideas into account and operationalize them. * Powerful Move 1: Name The Thinking* Powerful Move 2: Set Expectations around Student-AI Collaboration* Powerful Move 3: Make Learning VisiblePowerful Move 1: Name The ThinkingNaming the thinking in a process is an idea borrowed from Project Zero’s work around making thinking visible. And in that sense, this is not really a new idea; it’s the notion that we consider the category of thinking, and then name the verb we want students to practice. For example, the category might be to think critically, and the verb might be to evaluate. Project Zero suggests that if you care about thinking, you have to design for it. You have to name it, value it, and make it observable, because what a classroom repeatedly reinforces and rewards is what students learn to do. And with AI, if we don’t name the thinking, the easiest path of the least resistance wins: creating a polished product automatically. It’s tempting for students to skip the messy process that requires effort and to focus on task completion. If we do name it, we can build the habits of mind we actually want to instill in our students.Another important thing to note when naming the thinking is that each thinking move is not an “activity” to keep kids busy, nor are they compliance checklists. They’re steps that make thinking visible enough to practice another powerful move: metacognition, in which we can talk about the thinking, coach for improvement, encourage students to notice their own growth, and plan their own independent next steps. In addition to Harvard Project Zero’s Thinking Routines, there are other processes that follow very similar patterns, like Stanford D.School’s Design Thinking cycle, writing workshops, lab reports, Socratic seminars, critique protocols, coaching cycles, research, and a hundred other classroom processes that work because they support the thinking that leads to a solid product. Below is a preview of my book with samples of words that we can draw upon. It’s not an exhaustive list, but it’s a good place to start, helping us think about what we mean by “naming the thinking moves”. Notice the bolded words under the “Example Thinking Verbs” column. Those are things that AIs can currently do quite well on their own or in collaboration with our children. When planning, ask yourself:* What types of thinking are important in this task?* If I were to name what students should do as verbs, which ones come to mind?* How can I build a shared vocabulary with my learners about what these words mean?* When in the year is it helpful to introduce, practice, and review this language?In the next section, we will take the named thinking moves and set expectations for when and how AI collaborates with students on each move.Powerful Move 2: Set Expectations around Student-AI CollaborationI like to frame AI as not a tool, not a calculator, and certainly not a threat, but a guest collaborator. That framing changes the way we introduce it to students. We’re not handing them a device and saying, “Go use this.” We’re inviting AI into the learning space, and we should be deliberate about the roles it plays.For example, if the thinking move is brainstorming, you might say: “AI will collaborate by asking Socratic questions that push your creativity. It will not generate ideas or suggestions for you.” Now students know what they’re responsible for, what AI is responsible for, and what’s off-limits.You can take that one step further by narrowing which AI students use. In class, that might sound like: “I built a bot for you to use that’s aware of our learning objectives. Please use this bot only. It helps me be a better teacher, it allows me to coach you with more precision, and it makes your AI use transparent so we’re both clear on when and how you leveraged AI to support your effortful thinking.”From there, the planning move is straightforward: decide how AI supports each thinking move based on your knowledge of the learning goal and your students. Caveat: this kind of decision making does require some AI literacy from us as teachers, enough familiarity to predict what the tool will do well and where it might take over the thinking.If you’re new to it, AI-use scales can make expectations easier to name and share. You can use an existing one or build your own.One option is the Expectations Scale that Holly Clark and I created. It helps you label what AI can and cannot do within each step of a process. For example: “During empathizing, you may use AI to give feedback on the depth of your interview questions,” or “During outlining, you may use AI to help you structure your plan.” Another option is the AI Assessment Scale by Perkins et al. It’s widely used and has influenced a lot of school-based scales. The concept overlaps with what you saw above, and to be transparent, our work was influenced by theirs. Their team updates the scale regularly and grounds it in ongoing research, which makes it well worth reviewing as you design expectations for students. Check out more here: https://aiassessmentscale.com/Ok, so now you’ve seen two moves: name the thinking, then decide how AI will collaborate with students, or not, on each step of the process. In the next section, we will build on those two ideas with a third powerful move. In the next section, I will argue that a process only works if we can actually see it. If the only thing we ever see is the final product, we’re back to guessing how the students arrived at that work and praying that AI didn’t do it all for them. The third powerful move is making the learning visible through documentation. Let’s take a look. Powerful Move 3: Make Learning VisibleI realize that up until this point it has sounded a lot like I’m placing process over product. Let me be clear: the final product still matters. For most kids, it’s the most motivating part, the destination, the why of it all. But when a compelling product sits on top of visible thinking, it becomes something richer: a story of learning they can actually explain. Look at what I made, and here’s how I got there. I struggled. I persevered. I pivoted. I grew.The problem is that AI can generate polished work instantly. So a polished product alone is weaker evidence of thinking than it used to be. That’s why making the thought process visible is powerful. Meaningful learning requires both process AND product. Products give purpose. Processes shape the thinking that makes the product worth creating. Drafts, prototypes, revisions, annotations, and reflections are mini-products that capture thinking along the way.But there’s a trap that teachers should be aware of: documentation can slide into policing. When visibility becomes prove you didn’t cheat, students experience it as compliance, so I use a better frame: breadcrumbs. Breadcrumbs are lightweight traces of thinking captured during learning: a quick reflection, a sketch, a revision note, an outline, a screenshot of a chat plus a short takeaway. Not everything, just enough to make learning visible in context and help students notice their own growth over time.There are countless ways to document student thinking. Here are two visibility moves that work across subjects, then a list of more possible approaches. If you would like to see more inspiring ideas, make sure to check out my book.Option 1 - Process journals:A process journal gives students a single place to capture “breadcrumbs” of their thinking as it evolves, and it gives teachers something coachable while the work is still happening. It also scales cleanly: a simple template in Google Docs, Word, or Pages can be duplicated for every student through an LMS, then adjusted up or down with prompts, feedback boxes, links to vetted tools, and clear expectations. The result is one adaptable tool that holds instruction, links to purpose-built bots, reflection, and evidence of learning together, instead of trying to reverse-engineer what happened from a polished final draft. In fact, final drafts can sit in the process journal! It’s such a powerful move to ask students to submit their process journals to their teachers that include the final draft because it shows that we value the thinking and the work leading up to the final piece. If you like the idea of using a process journal, I have templates and more ideas that you can get for free from these two articles. Option 2 - Think Collaboratively on PaperVertical learning, the Peter Liljedahl move you’re seeing pictured below, is a simple design choice with a huge payoff: get students standing, working in groups, and thinking out loud on visible, shared spaces. In the age of AI, this technique is powerful because it shifts the class from “who can produce the cleanest final answer” and back to how ideas form, change, collide, and improve while simultaneously building in movement. My goodness, do kids sit a lot during the day! Vertical work makes an active process unavoidable: students get up, and start talking about learning away from distractions like laptops.If you notice, the laptops are closed in this classroom! Students go from discussing, collaborating, debating, and then perhaps opening their computers to share their findings with an AI bot from their teacher. Such a powerful and intentional use of technology at key times.More ways to document student learning I only shared two approaches in this article, but there are plenty of ways to document learning. Below is a list of additional options students can use to capture their thinking as it’s happening. The goal is simple: make learning visible enough that you can coach it, students can learn from it, and AI stays in the role of collaborator. You’ll probably mix and match a few of these rather than rely on just one.Possible approaches include:* Blank template/drafting space (work-in-progress thinking). * Chat thread (shows thinking changes, not just answers)* Pivot point notes (moments thinking shifted and why)* Metacognition question trails (curiosity evolving into sharper questions)* Idea graveyard (discarded ideas kept as evidence of exploration)* Connection notes (new idea + prior knowledge)* Annotated snapshots (image + one sentence of meaning)* 60-second voice memos (messiest parts + next moves)* Evidence selection (best evidence set + short rationale in slides)* Physical notebook entries (kids love these)* Conference/exhibition script (student-led explanation supported by evidence)How to Design Your Own ProcessOne of my favorite ways to build an AI-enhanced process is to use intentionally low-tech tools: sticky notes and a pen.To be clear, the stickies are for planning. They’re a coach’s sketchpad. They’re perfect for teams because you can move them around fast, test different sequences, and talk through how students will flow from thinking move to thinking move without getting stuck in a Google Doc spiral. When I’m coaching a teacher, I’ll make two rows. Row one is the thinking moves. Row two is how AI collaborates, or doesn’t, on each move. That’s it. You can see a quick example in the photo above from a high school writing task. I made it up for this post, but you get the idea.Once you’ve got a sequence you like, share it with students in whatever format fits your classroom. Take a picture and project it. Turn it into a slide. Write it on the board. Print it and put it on tables. The format doesn’t matter. The point is simple instructions that keep the focus on the learning and make expectations easy to follow.Teachers, I gotta say, me included, we talk too much. We frontload instructions like we’re reading the terms and conditions of a credit card. We overexplain the pitfalls, the rules, the exceptions, the “don’t do this,” the “also don’t do that,” and by the time students actually start working, their brains are already tired. That’s cognitive load in the worst place: on the directions instead of the thinking! A simple process fixes that because it keeps instructions short. A process can enable you to provide just-in-time teaching. You can circulate, coach the move students are actually in, and drop a mini-lesson, a metacognitive guide, or feedback right when it’s useful. Direct teaching is not bad; in fact, it is one of the highest impact approaches to teaching, according to Hattie. But when it’s the predominant teaching move, class can become disengaging and focused on content, compliance, and control.Here’s the sticky note move I use when coaching teachers to plan their own AI-enhanced process:* Step 1: Write thinking moves across the top row (verbs only).* Step 2: Under each one, write the AI expectation in plain language: “AI can help by… / AI cannot… / Student must…”* Step 3: Share it in class. Read it once. Ask for clarifications. Then start the work. Coach during the process and avoid over-explaining, as tempting as it is!Pro tip: take it one step further and add a third row for breadcrumbs. Under each thinking move, write what students will capture to document their learning and make the thinking visible, for example: a brainstorm snapshot, a decision point, a revision note, a reflection, or a screenshot of a chat. Whatever it might be, it helps you and your students to align around what thinking will look like.Monday-Ready Resources and Powerful MovesThere were a lot of ideas in today’s article. What I want to leave with you with are big picture, powerful moves, and further tools you can start using in class immediately. 1) Good readsThe Power of Making Thinking Visible, by Ron Ritchhart and Mark Church. If you want a practical backbone for naming thinking and making it observable, this is a strong place to start.Mindset, by Carol Dweck, has had a powerful influence on my work with AIEPs. Dweck explores the concept of a growth mindset versus a fixed mindset and how a fixed mindset can limit our growth and potential. This mindset shapes many areas of our lives. What Dweck does so well is help us recognize the language of a growth mindset. With AI in our classrooms, we need to pay close attention to the language of a fixed mindset. I’m seeing many ways a fixed mindset could lead to students leveraging AI to complete tasks rather than seeing that productive struggle is an important and necessary part of the learning experience.2) My free process builderYou can use my tool to generate an AI-enhanced process and a detailed prompt you can feed into a chatbot so it understands your context, your students, and your documentation approach. It helps you to not only get specific about the learning experience you want to design, but it also helps AI to align with how it will support because you can feed the prompt from this tool straight into School AI, Flint, Magic School, etc., to follow your process. https://processes.lovable.app/3) The three takeaways from the article.These are the three things I really want you to walk away with. Immediate and simple steps to support AI use in your classroom with clarity.* Name the thinking.* Set Expectations around student-AI collaboration.* Document student thinking and make it visible.ConclusionIf you want a starting point, don’t overhaul everything. Redesign one task as a process, and watch what happens when expectations stop being implied and start being practiced. Start small on purpose, and start where you already have momentum. Think about the repeated thinking moves you see all year: brainstorming, outlining, arguing from evidence, checking for bias, revising for clarity, or reflecting on choices. If there’s a strategy you use frequently, that’s your best candidate. Operationalize it. Name the moves, make them teachable, and run the same process again and again until students stop asking, “What do you want?” and start asking, “Which move are we on?” or even better yet, show independence and fluency with their use of the process.Write a short AI-enhanced process for that task, and keep it simple:* Name the thinking (verbs only).* Set expectations for how AI collaborates or doesn’t.* Add breadcrumbs so the learning is visible while it’s still alive.And that’s it. You’re not building a system. You’re building a routine that students can actually run without you reading the terms and conditions out loud for 30 minutes.“AI allowed” is a label that invites interpretation, and interpretation is exactly how you end up with a spice rack sorted by scent and a cat in a sous vide bag.A process turns shared values into shared behaviors. It protects the parts of learning that can’t be outsourced: judgment, reasoning, taste, decision-making, and reflection. And it gives students something better than “What do you want?” It gives them a map that reinforces your shared values. After frequent practice, the questions shift to the ones you actually want to hear: “Which move are we on?” Or even better, no question at all. They just start, and they run it. The point being that processes need to be regularly reinforced to get to automaticity and internalization. With AI, kids have few internalized processes due to the fact that it’s a relatively new technology, so your efforts to practice meaningful use of AI are essential.While I believe in the power of process, to be clear, the point is never the process itself. The point is the thinking and the effort that the process makes visible.Start small on purpose. Run the same process again and again. Tweak one step at a time. Find ways to demonstrate understanding. Keep the focus where it belongs: on student thinking, not on policing, not on overexplaining the steps, and definitely not on cleaning up the mess after your digital guest takes over the party.AI DisclosureSupporting artwork in this post was created with the support of ChatGPT and Gemini. All images have AI disclosures to indicate my process with transparency. Other graphics from my book were created by me (e.g. table of words).The audio accompaniment of this article with me reading was my voice, and then run through Adobe Voice Enhancer to clean it up and make it sound professional. This article was co-written by ChatGPT 5.2 and Claude 4.6. My process followed several cycles of Think, Generate, Edit. This article is in many ways a summary of the core ideas in my book. I took a PDF of it (and compressed the hell out of it), uploaded it to ChatGPT, and had a conversation with it about my work. We created an outline that followed it and the typical structure of my Substack posts. My typical flow is to start an outline on a Sunday, think during my workweek, especially while walking to work and talking to my partner (walk-and-talks are the best). Then on the weekend, I refine the work for Monday morning to be delivered to your inboxes.What I found is that ChatGPT and other LLMs are great at helping me write, as I can get a summary of my existing work/ideas and start laying it out. I’ll wake up early, have coffee, and read where I am in my writing, revise it through a conversation with my synthetic collaborator, and then go about my day. I find new ideas come to me, and I will edit the work later to help it flow, or sometimes even to visualize it better. I would estimate that, with AIs as my collaborators, I saved a significant amount of time, but I still spent around 10-11 hours completing it.Finally, I believe we should normalize transparent disclosure of AI use in education. When adults model openness, students are more likely to talk honestly about how they’re using AI rather than hide it. If you are not disclosing your use of AI, you might be unintentionally modeling secrecy.One Pager of This ArticleIf you enjoyed this article and want to share it with someone else, here is a one-pager summarizing its content. Download it, print it, email it to a friend who you think would appreciate it, or you could directly share the link to it! Thank you for reading! This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit aienhancedprocesses.com

In this episode of The EdTech Lens, Alex explores one of the most powerful ideas in learning: metacognition. Inspired by Amelia King’s recent book, Thinking with AI, and the rising need to understand how AI intersects with thinking, this episode looks closely at how learners plan, monitor, and make sense of their thinking before, during, and after learning. To do that, Alex speaks with four educators whose combined experience stretches across continents, disciplines, and decades.The conversation begins with Ochan Kusuma-Powell, an internationally respected educator, consultant, Cognitive Coaching trainer, and author whose career has helped shape how schools understand learning, thinking, and inclusion. With experience in the United States, Saudi Arabia, Tanzania, Indonesia, and Malaysia, she brings a global perspective to how students learn and how teachers can help them think about their own thinking. A founding member of the original Design Team behind Next Frontier Inclusion and co-founder of Education Across Frontiers, Ochan has influenced schools worldwide through her books and her ability to blend research, storytelling, and practical strategy. In this episode, she shares a crystalline view of metacognition as holding your thinking in the palm of your hand and examining it from many angles, and she describes how she uses AI as a thought partner while writing a new book.Next, Alex is joined by Ty Urquhart, Middle School counselor at Shanghai American School Puxi. Ty brings a social emotional lens to the conversation, offering insight into how teens develop self-awareness, self-management, and decision-making skills during a time of rapid cognitive change. He discusses why teens crave independence, why pausing before acting is so challenging, and why shifting from right versus wrong to helpful versus harmful leads to more productive conversations about AI, digital behavior, and wellbeing. Ty also describes AI as the mirror rather than the villain, reminding us that the goal for students is not avoidance of technology but conscious, intentional use of it.The episode closes with Victoria Hoult and Rachel Kalish from Korea International School, Jeju. Victoria is an experienced instructional coach, curriculum coordinator, and educational leader whose career includes New Zealand, the United Kingdom, Brazil, and Korea. Now serving as Director of Teaching and Learning, she leads with relationships, clarity, and an unwavering commitment to building a school culture where all voices feel valued. Rachel, who holds an MA in Educational Leadership, is the school’s Curriculum and Instruction Coach and has worked in Guatemala, California, Dubai, and Korea. As an innovative and collaborative educational leader, she is dedicated to enhancing student learning by prioritizing relevance and engagement. Her expertise includes implementing effective instructional strategies, aligning curriculum with educational standards, and fostering teamwork among educators. By leveraging data driven insights in collaboration with all stakeholders, she works to improve student outcomes academically and socially, ensuring that every learner reaches their full potential. Together, Victoria and Rachel share practical insights from coaching teachers, guiding schoolwide reflection, and helping students develop the habits needed for sustained, independent learning. Their reflections on how metacognition shows up in teacher practice and how AI might support deeper thinking bring the conversation to a thoughtful and grounded close. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit aienhancedprocesses.com

In today's episode, Alex has a chat with Jeremy Willette, Leslie Henry, and Brenna McCandless, three library and information specialists. In the episode, we explore how we can help kids find accurate information in the age of AI. Below you can find information about the guests: Brenna McCandless: Brenna has been a pre-K through grade 12 librarian for 15 years and has lived and worked in the United States, Malaysia, China, and more. She is also knowledgeable about designing materials, AI in education, and more! Leslie Henry: Leslie Henry is her 36th and final year in education. She has worked as both a French teacher and a librarian in Canada, Russia, Indonesia and China. Leslie celebrates the sense of community and safety that libraries provide. Her passion is children’s literature. She marvels at the magic and joy that a picture book can bring to children of all ages! Leslie is the cross-river librarian at Shanghai American School.Jeremy Willette: Jeremy Willette discovered a love and appreciation for libraries as a kid growing up in rural Maine. In addition to being a frequent visitor at the nearby town library, he volunteered for years at the one in his school. Since then, he has become an international educator working for over 20 years in the USA, Brazil, Hungary, India, and China…and has helped other generations of people love the library too, from infants to adults. An avid traveler, foodie, and library advocate, Jeremy is the Library Coordinator at Shanghai American School. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit aienhancedprocesses.com

In this episode, Alex and Kim Cofino chat about the "other side" of AI-- Kim likens her experiences with social media to AI and gives us some thoughts on how we proceed as we integrate AI into our lives more and more. Kim has been an educator in international schools since August 2000. Having lived and worked in Germany, Malaysia, Thailand, and Japan, Kim has had a variety of roles in international schools, including (her favorite) instructional coach. Kim is the host of the #coachbetter podcast, and frequently speaks and writes about the power of coaching to sustain change in schools. In addition to her work in education, Kim is also a competitive powerlifter, currently on the Thai National Team as the 63kg M1 representative for Thailand. Based in Bangkok, Kim is the Founder and CEO of Eduro Learning, where she supports educators and schools to develop sustainable and successful instructional coaching programs. Kim is also the Executive Director and Founder of the Association for the Advancement of Instructional Coaching in International Schools (AAICIS). Learn more about Kim and Eduro at: https://www.edurolearning.com. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit aienhancedprocesses.com

In this episode Alex interviews Jason Prohaska from ESF, Hong Kong. Jason spoke at 21CL and had a breakout session titled "The Digital Divide" on how tech affects boys differently than girls. Jason's BioJason Prohaska serves as the Educational Technologies Lead at the English Schools Foundation in Hong Kong, developing strategic direction for technology integration across their network of 22 schools and 18,000+ students. He specializes in creating foundational frameworks for educational technology governance, ethical AI implementation, and digital citizenship while chairing the Educational Technologies Network.With over a decade of experience at Renaissance College Hong Kong and previous roles at German Swiss International School, Jason focuses on empowering educators and school leaders through professional development and strategic guidance. He holds numerous certifications including Apple Distinguished Educator and Google Certified Teacher.From his LinkedIn: "I am an experienced educational leader focused on integrating technology to transform teaching, learning, and leadership. At the heart of my leadership philosophy is a belief that technology and STEM education should always serve people—empowering students to lead with creativity, ethics, and purpose."Connect with Jason: https://www.linkedin.com/in/jasonprohaska/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit aienhancedprocesses.com

In this episode Alex sits down with Holly Clark and Steven Chang. The provocation to get our conversation started is: "how can we use AI as a super power like Iron Man rather than something that makes us helpless like in the film WallE?" Holly Clark is the author of "The AI Infused Classroom," and a leading global strategist for AI in education, guiding schools and districts through the integration of AI best practices and policies. As an acclaimed international speaker, bestselling author, and co-host of The Digital Learning Podcast, she draws on her trailblazing experience in one of the first 1:1 classrooms in the nation, to empower educators to adopt AI-enhanced blended learning.Guest Bios:Holly's influence on the educational landscape echoes in her other acclaimed books, "The Google Infused Classroom", "Chromebook Infused Classroom", and “The AI Infused Classroom”, which are esteemed resources for educators globally. She is a Google Certified Innovator, a Microsoft Innovative Educator Expert, and a National Board Certified Teacher. Her passion is for helping teachers find their blended learning and AI genius and learn to create and design unforgettable learning experiences. Connect with Holly on all social media via @HollyClarkEdu or visit her blog at hollyclark.orgSteven Chang was former Corporate VP of Tencent and CEO, Greater China of a global advertising agency. He is a coach and consultant for Business application, transformation, strategy formation and marketing solution of new technology, new business model and China Internet ecosystem. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit aienhancedprocesses.com

In today's episode Alex interviews three incredible educators on a panel. We chat about a hot topic in the education world at the moment-- AI and assessment. The team discusses their personal and professional backgrounds, roles, and expertise in relation to AI and assessment, with a focus on the need for a shift from traditional teaching methods to more student-centered approaches. They also critiqued external standardized tests and emphasized the importance of developing durable skills that are transferable and relevant in the modern world. The conversation ended with plans for future communication and collaboration, including the possibility of a follow-up session and the inclusion of a creative writing project in the podcast.Jennifer DeLashmutt has over 25 years of teaching and leadership experience in the United States and Asia. For the last ten years, she has served as an elementary principal and PK-12 Director of Curriculum and Professional Learning in Hong Kong ( HKIS) and Bangkok (ISB). Her background in curriculum, instructional practices, and assessment spans both primary and secondary years. She is devoted to ensuring that all learners are empowered members of their learning communities and they feel safe and valued. Jennifer is a design thinking leader who is future driven and diligently stays curious! She is an educational consultant with Novak Education. In our episode she mentions:⁠Katie Novak and Catlin Tucker - Embracing AI⁠⁠LUDIA Chatbot on Poe⁠Dr. Shannon Doak is an Edtech and Innovation Leader. Speaker, Author, Lucky Father and husband, #AI Enthusiast, #PoeCreator #CoffeeLover, and #HomeBarista | He is currently the Director of Technology at Nanjing International School. Some helpful links from Shannon are:⁠This is the book by Dr Sonny Magana⁠⁠This Grade School Offers AI-Only Classes, No Teachers Involved⁠⁠How a new Arizona school will use AI to teach students in 2-hour models⁠Walk into many schools today and you'll find a system as outdated as a sundial at NASA - not because educators aren't innovating, but because the system itself needs reimagining. John Nash partners with school leaders to close the gap between where education is and where it needs to be. John doesn’t treat school transformation like making microwave popcorn - push a button and pray. John’s approach is more like brewing the perfect cup of coffee: methodical, intentional, and guaranteed to wake people up. Drawing from 30 years in the trenches of education reform, he shows school leaders how to use design thinking to fundamentally rethink how schools work. As founding director of the University of Kentucky's Laboratory on Design Thinking, he helps transform traditional institutions into dynamic learning environments where students drive their own education. His approach on design thinking and generative AI has caught the attention of everyone from the U.S. State Department, to district superintendents, to international schools. His book "Design Thinking in Schools" (Harvard Education Press) gives leaders a tested blueprint for turning bold ideas into meaningful change. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit aienhancedprocesses.com

In today's episode, I sit down with three incredible educators: Amelia King, Vera Cubero, and Dr. Shannon Doak. We explore the idea of whether or not AI is a support or a hindrance to one's creativity. You can all three of my guests on Linked In. Finally, at the end of the episode, I share a song that I made as a collaboration with AI: I uploaded a finished song, remade it for several hours, and then mixed/looped/edited the output. I hope you enjoy the episode! This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit aienhancedprocesses.com