"AI and Assessment" (Revisited)

GenAI Didn’t Break the Project. Speed Almost Did.

Intro 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. 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 Aimée When 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 [https://AIEP.lovable.app/] 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 Gates On 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 Resources Resource #1 - Checklist when Using Gates Separate 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 Checklist Here’s a PDF you can print out, along with look-fors, for using a gate in your processes with students. AI Disclosure ChatGPT 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 Links Check out the project here referenced in the article. * TEACHER version - https://buildhomassistantteacher.netlify.app/ [https://buildhomassistantteacher.netlify.app/] * STUDENT version - https://buildhomassistantstudent.netlify.app/ [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 [https://aienhancedprocesses.com?utm_medium=podcast&utm_campaign=CTA_1]

How Did It Actually Go?

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 | Geneva Aimée 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 [https://www.linkedin.com/in/aimeeskidmoreeducator/] | TeachSpark [https://teachspark.mykajabi.com/Sprint2026] Aimée 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 | Canada Jay 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 [https://www.linkedin.com/in/jay-goodman-edd/] | Goodman Learning Partners [https://www.goodmanlearning.com] Vamshi Mugatha | Director of Technology | American School of Brasilia Vamshi 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. LinkedIn [https://www.linkedin.com/in/vamshi-mugatha/] Leon Lam | A-Level Head of Humanities | Beijing National Day School Leon 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 [https://www.linkedin.com/in/leonlam/] This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit aienhancedprocesses.com [https://aienhancedprocesses.com?utm_medium=podcast&utm_campaign=CTA_1]

Scaffolding

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. Definitions I 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 [https://www.cast.org], 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 [https://www.novakeducation.com/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 [https://www.ascd.org/people/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 Scaffolding Scaffolding 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 [https://www.amazon.com/Scaffolding-Language-Learning-Second-Mainstream/dp/0325056641/ref=sr_1_1?crid=1L7CH90ZCZN6Q&dib=eyJ2IjoiMSJ9.gbWpNl2grvibXir37m2uAAg6Pn7Cn0jvYs00oh8HiuGuQMwvVF9SItm-cREj5Pm98mU31aEeOHI5-2SFJEiDvBYIb2aLFQoKnLA2aC30JB-GQ6XaHJ5YZV1YXCJWhQJnoN_RGCDYuegQ1pkDCm-s0hZY0_d5cG1BT47w1PzMl1rCJ026jHPks13gCswz7WsnKUXaXTQkwFfyDc3I3Yxe7PJf73cZwIej1Yk72mckBa4.uKYSU_HRQyr8VHz-fhwHWQADgCFneHs2OWIYhtvJwHU&dib_tag=se&keywords=Scaffolding+Language%2C+Scaffolding+Learning&qid=1777861416&sprefix=%2Caps%2C626&sr=8-1]. 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 Together Here’s a Claude Artifact [https://claude.ai/public/artifacts/346ae0f7-5ad4-4f92-bc25-24843ee55531] (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 Pit When 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 [https://doi.org/10.1186/s40594-022-00361-z] rather than build reasoning. Frey, Fisher, and Almarode put it plainly in How Scaffolding Works [https://www.amazon.com/How-Scaffolding-Works-Supporting-Responsibility/dp/1071904159/ref=sr_1_1?crid=21RB2WMNSDP1C&dib=eyJ2IjoiMSJ9.SkrngHF5wOn06bGrkqspRxZlQmpIINum0KL9o5omh3ZbgwA8hNCPjUxW9sPQ0tfKMVtQ6ccSUsy11R4ZVBrpAzrm-D3hXokfnamMEY-_rDAMLFlM0nHGStbrG5w8o0nogYgp_r1wUZKUqpsj5Q27pMFefmjd1nDm9ITZueFooaRYalpj7Ew0kKAeHSZCEIX6pe-IrjLo1hlTnmIL6pJO1SOtq3nEWujnBMW0-uqfsGc.eBHiU-rA-1_xrcTDKz5X4jO8JHj1QX4Qv3KsOEcFJ2U&dib_tag=se&keywords=How+Scaffolding+Works&qid=1777861439&sprefix=scaffolding+language%2C+scaffolding+learning%2Caps%2C623&sr=8-1]: 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 [https://learningpit.org]"; 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 [https://www.teachthought.com/pedagogy-posts/3-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 [https://doi.org/10.1007/s11256-019-00509-2]: scaffolds built around one cognitive tradition can exclude learners who don’t share it. Erin Meyer’s research in The Culture Map [https://www.amazon.com/Culture-Map-INTL-ED-Decoding/dp/1610392760/ref=sr_1_1?crid=N3GCNEDHFN0Z&dib=eyJ2IjoiMSJ9.6SuyYCUFTcl2desnzCnsi8_DA4eh9bRMPs7B7v1UR0TwjZtADrDxRmuuIeHsOeyG8KsIrS9j-ISEzry1ouPnHdpK7PHb0HG13W4D7kiWb9PX80bchyVNXMqj4kDF2rp_CYxDjh-j0wn5Srx1vM7_PZxVflrGre4BppL16yUv_ijLa4Amib-88GXm4Oi9uxAxNgsT7rwLv-KpPTBgIkzDZp8Ei2NNkHl15VA7CkEH6aI.8NTnrIavSCDIBu_STo1--nuAjMb-IkuDwQudnWu5A4U&dib_tag=se&keywords=The+Culture+Map&qid=1777861471&sprefix=how+scaffolding+works%2Caps%2C631&sr=8-1] 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 [https://aienhancedprocesses.com/p/thinkgenerateedit] 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 Coach This bot guides students through seeking, making sense of, and acting on feedback using the Acquire, Analyze, Act [https://aienhancedprocesses.com/p/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 [https://doi.org/10.3389/fpsyg.2023.1110086] produce stronger outcomes than those focused only on task completion. David Rock calls one planning move “prioritize prioritizing” in Your Brain at Work [https://www.amazon.com/Your-Brain-Work-Revised-Updated/dp/0063003155]: 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 Metacognition I 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 Coach This bot helps students plan before they begin using the Foresight stage of the Hindsight, Oversight, Foresight [https://aienhancedprocesses.com/p/hindsight-oversight-foresight-hof] 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 AI Scaffolding 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. Conclusion As 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 Disclosure In 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. Time I 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. Process To 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 AI In this step, I took my research questions and did a search in Consensus [https://consensus.app/]. 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 [https://aienhancedprocesses.com?utm_medium=podcast&utm_campaign=CTA_1]

If It’s Difficult, You’re Doing It Wrong

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 [https://musubikiln.com/blogs/journal/onigiri-facts-that-even-native-japanese-people-dont-know?srsitid=AfmBOopVPOLaoc] rice ball [https://musubikiln.com/blogs/journal/onigiri-facts-that-even-native-japanese-people-dont-know?srsitid=AfmBOopVPOLaoc] 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 [http://7-Eleven Japan to Replace Plastic Onigiri Wrapping With ... Saigoneer https://saigoneer.com › society › 16794-7-eleven-japan-t...]! 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 Faces A quick clarification before I go further. Direct teaching is a powerful tool (see Hattie’s work [https://visible-learning.org/2016/04/hattie-ranking-backup-of-138-effects/]). 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 Moves Here’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. Conclusion I 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 Disclosure This 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! Thank you so much for reading this article. If you found it helpful or enjoyed its content, please hit the like button, or go one step further and send it to someone who you think might like it. If you want to try building your own process, I have a free tool at aiep.lovable.app [http://aiep.lovable.app] that walks you through it step by step. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit aienhancedprocesses.com [https://aienhancedprocesses.com?utm_medium=podcast&utm_campaign=CTA_1]

"AI and Assessment" (Revisited)

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 [https://aienhancedprocesses.com/p/aiassessment], 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 [https://www.linkedin.com/in/emily-j-thomas-61a64135/] 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 [https://emilyjthomas.substack.com/archive]Elsewhere, Examined [https://emilyjthomas.substack.com/archive]. 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 [https://www.linkedin.com/in/timothy-cook-092713138/] is an educator and the founder of Connected Classroom [https://connectedclassroom.org/], 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 [https://www.linkedin.com/in/nsoentgerath/] 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 [http://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 [https://aienhancedprocesses.com?utm_medium=podcast&utm_campaign=CTA_1]