A friend who’s more optimistic about AI than I am sent me a list of tough questions about oral assessments. In this short episode, I answer them as honestly as I can.

The short version: the oral piece is 15 points on each major test, about 7% of the course grade. I grade by gut, not a rubric, and I’m still deciding whether that’s okay. I record only with permission. I’m figuring out how to scale this to six sections, and I’d love to hear how you’d handle a class of 35.

Key moments (raw timestamps, so adjust after edits):

• 0:00 A skeptic’s questions

• 0:21 How much of the grade?

• 1:40 Same questions for everyone?

• 3:00 Am I cutting anything to make room?

• 4:13 The pogo-stick question

• 5:21 Scores, and why I don’t use a rubric

• 7:01 Quiet students: “Stop talking and let me think”

• 7:53 Am I recording these?

• 8:47 Can this scale to five sections of 30?

• 10:19 “Building the airplane in the air”

This week, a round of summative vivas showed me something my tests never caught: students getting the right answer on a foundational physics concept without understanding it. In this short episode, I share how I found it, what I’m trying now, and a Plain English episode with Ethan Mollick on why struggle matters in the age of AI. I’d love to hear what you’re seeing in your classroom.

Key moments

• 0:00 Hook: right answers, no understanding

• 0:52 The pattern shows up in the vivas

• 1:40 The reteach

• 2:22 Maybe this one’s on me

• 3:05 Why I missed it: rewarding correct numbers

• 3:34 A podcast worth your time: Plain English with Ethan Mollick

• 4:18 Your turn: what are you seeing?

Vicki spent 24 years in the classroom, including teaching AP Computer Science with AI, and today she hosts the 10 Minute Teacher podcast and Cool Cat Teacher Talk, a radio and TV show she says reaches about a million people a week. We go back a long way.

Free 2-Minute Oral Check

What struck me is how close she is to what I’m doing. Her classroom rule: everything a student turns in, they should be ready to defend out loud. If AI helped write their code, they have to explain every line. We talk about why AI detection doesn’t work, why a revision history isn’t proof of learning, and the tools that make a student’s AI use visible in a draft. We also disagree a little. She’s skeptical of AI tutoring, and I’m still using AI for practice. If you’ve been wondering what oral assessment looks like for someone who isn’t a science teacher, start here.

Key moments:

• 0:00 Cold open: the biggest AI mistake

• 1:01 Her 1,000th episode, and being interviewed by her son

• 3:03 Why outrage travels farther than help online

• 5:51 Her policy: if you turn it in, be ready to defend it

• 13:09 “That could be your mama at the computer”

• 16:24 The integrity gap

• 28:49 AI as a magnifier of what you already know

• 31:56 Why she’s skeptical of AI tutoring

• 38:54 What oral assessments do for relationships with students

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It’s been an exhausting week here in the world of teaching. Homecoming football (we won!), the dance, an open house, and the end of the quarter all landed at once.

This week I made one decision: walk into every interaction with a positive attitude. In the middle of the chaos, a few human moments- one in a viva, one with a family at open house- reminded me why I’m still doing this after 41 years. I also share what happened when I emailed my administrators and said I felt stretched.

Key Moments

• 0:00 – Cold open: an exhausting week

• 0:19 – Homecoming, the dance, and choosing a positive attitude

• 2:52 – The viva that turned into a human conversation

• 4:13 – Why human moments matter most in the age of AI

• 5:16 – Open house, and a family lights up at the crystal collection

• 6:14 – Productive struggle: learning to teach physics with calculus

• 6:50 – Feeling stretched, and what happened when I asked for help

• 8:53 – This Thursday: the Misiones Q&A and the Flint webinar

This week’s guest goes back a long way — Don Wettrick, entrepreneur, educator, and founder of STARTedUP, an organization built around one question: how do you make class time feel more like the real world?

Don just stepped back from running STARTedUP day-to-day to go back to what he calls “founder mode” — more classrooms, fewer meetings. We talk about that transition, his “seekers vs. peakers” framework for spotting opportunity, and a line partway through this conversation that’s stuck with me since we recorded it: everybody can write a perfect email now, which means the deals are being lost the moment a person can’t look up and actually have a conversation. Being human is sought after right now. Being flawed is sought after right now.

This episode’s a little different from the last few: no framework, no big reveal, just a good conversation between two people who’ve been doing this a long time.

Key moments:

• 0:00 – Cold open: why “being flawed” is suddenly valuable

• 0:44 – Interview begins, catching up with Don

• 2:12 – Why he stepped back from CEO to “founder mode”

• 9:50 – His “seekers vs. peakers” framework

• 19:34 – Building a class around freedom, mastery, and purpose

• 23:24 – Playing AI platforms against each other to think more clearly

• 30:09 – The full story behind the cold-open clip

• 38:19 – A decades-long journaling habit, and archiving your own growth

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This one’s a solo update — no guest, just what’s actually happening in my classroom, plus a piece of news I’m genuinely excited about.

On the vivas side: students are showing up rehearsed, some of them using an AI practice tool to run through questions ahead of time. And a pro-tip. Asking a kid to diagram something live, on the spot, tells me more about what they actually understand than almost anything else I do.

And on the other side of the equator: ten years ago, I helped run one of the first large-scale flipped-classroom pilots in Misiones, Argentina. That relationship never really ended — and this month, the first unit of a fully Spanish-language, Argentina-contextualized Mastery Flip course went live. If you’re Spanish-speaking, or know someone who is, I’d love for you to check it out.

Register for the Spanish Language Course

Jon’s Upcoming Appearances

October 15th - Online in Argentina

November 14th (In Person) - Teaching Motivated Minds Conference (Austin, Texas)

Key Moments

• 0:00 – Cold open: “They showed up prepared”

• 0:32 – Round 2 vivas: how students are using FlintK12 to practice ahead of time

• 2:20 – The real shift: “I didn’t prepare” is becoming the exception, not the norm

• 2:31 – The whiteboard trick that’s changing how physics vivas go

• 5:41 – Pivot: the Misiones, Argentina backstory

• 6:56 – The first Spanish-language Mastery Flip course goes live

• 11:08 – Closing thought: pressure-testing the framework in real time, in public

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I asked him what he’d add to his own famous list of skills, just for the AI era. His answer: integrity.

Partway through this conversation, I walked him through Mastery Vivas for the first time — no advance warning, no pitch, just explaining what I’m doing in my own classroom. His reaction is on tape, and it’s one of my favorite moments this season.

P.S. — something I'm genuinely excited about: MasteryFlip is now being translated into Spanish, not just word-for-word but re-contextualized for schools in Misiones, Argentina — the first unit is live already. I'll give you the full story and the sign-up link next week, but I didn't want to sit on the news that long. Here is the link to the course:

Key Moments

• 0:00 – Cold open: his answer to the eighth survival skill

• 1:05 – Guest intro and interview begins

• 2:49 – Updating his famous “seven survival skills” for the AI era

• 4:15 – The “forklift in the weight room” metaphor and his fear of a coming bifurcation

• 6:56 – The stat that’ll stop you cold: 109 ninth graders, 14 living-wage jobs

• 11:41 – You walk him through the Mastery Viva live — his reaction is real-time, unscripted

• 36:10 – His one book recommendation for teachers heading into fall

• 37:22 – The kindred-spirits close

• 38:13 – A quick note on the new Spanish-language MasteryFlip course (more next week)

I asked her one question: what’s the single science-of-learning principle every teacher should hold onto in the age of AI? Her answer: be as metacognitive about the AI as you are about your own thinking. Simple to say. Genuinely hard to picture in an actual classroom — which is most of what this conversation is about.

That guest is Dr. Tina Grotzer, who runs the Next Level Lab at Harvard’s Graduate School of Education, studying causal reasoning — how people actually trace cause and effect, versus the neat, linear version we usually assume.

That’s three separate people now — me, an MIT researcher, and a Harvard researcher — landing in the same place, independently. I don’t think that’s a coincidence anymore.

Key Moments

• 1:33 – What “causal reasoning” actually means (her research, in plain terms)

• 5:10 – The sinking-and-floating experiment that breaks kids’ assumptions

• 10:40 – Introducing the Next Level Lab: will AI make us smarter or stupider?

• 17:13 – Her core thesis: why metacognition is what makes AI a partner instead of a crutch

• 19:32 – The Russell Hulse story — a Nobel Prize won by refusing to ignore an anomaly

• 21:11 – The “forklift to the weight room” metaphor, and her fear of AI as a crutch

• 27:06 – You lay out AI Engines, Analog Roots, and the Mastery Viva live — and she reacts in real time

• 34:11 – The one cognitive skill she says AI will be the last to replicate: empathy

• 40:00 – Her closing fear: a coming “bifurcation” between kids taught to think and kids who aren’t

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If you’ve been listening for a while, you know oral assessment has been building toward this. This week, I made it official — a required, graded piece of every major exam for the first time. It went well in one class. It was genuinely hard in another. And there’s a piece of it I’m not willing to hand over to AI, no matter how tempting the time savings are.

This one’s a solo update — no guest, just me and what actually happened in my own classroom this week, including the honest math on whether any of this works if your class is twice the size of mine.

Timestamps:

• 0:00 – Cold open

• 0:17 – Welcome / what this episode covers

• 1:16 – How it went in Geology/Meteorology/Oceanography (GMO)

• 2:02 – Gut instinct over a formal rubric

• 2:53 – GMO wrap-up: “it worked swimmingly”

• 3:09 – Honors Physics: 18 students, barely finishing in 55 minutes

• 4:59 – The line I won’t cross: why AI can’t grade the summative assessment

• 5:20 – The formative side: the daily lesson cycle and where it breaks down

• 6:38 – The real bottleneck: not enough time to reach every student

• 7:04 – The fix: using Flint K-12 for after-class oral check-ins

• 8:07 – Tying back to Justin Reich: “nobody knows how to teach in the age of AI”

• 9:33 – A colleague’s test scores jump after going analog

• 10:13 – Close

Justin Reich directs MIT’s Teaching Systems Lab, and I’ve known him since long before “AI in education” was a track at every conference. What struck me most, recording this earlier this year: he arrived, completely independently, at almost the exact same three ideas I built into MasteryFlip.

The moment that’ll stick with you — two computer science professors in Buenos Aires who moved their final exam to a 20-minute oral defense. What happened to their students’ work in the post-ChatGPT era, and why a live, unscripted conversation turned out to be the fix, tells you most of what you need to know about where this is all headed.

This one runs long, about 50 minutes, but it’s one of the richest conversations I’ve had on this show. Grab a coffee.

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Key Moments

• 0:00 – Cold open: Reich on why we should be listening to teachers, not pundits

• 0:21 – The MIT report, and why it’s a bigger deal than it looks

• 1:48 – Introducing Justin Reich

• 3:45 – The “Homework Machine” research project — what 120 teachers and students said

• 6:11 – Jon’s own AI failure story, and the worst reviews of his career

• 12:51 – Laying out the MasteryFlip framework, live

• 13:16 – Pillar 1: AI Engines

• 19:37 – Pillar 2: Analog Roots

• 21:49 – Pillar 3: The Mastery Viva

• 23:09 – The trebuchet story

• 24:22 – The Buenos Aires oral-exam story

• 33:25 – The DeepMind story — nobody’s figured out how to train junior engineers with AI either

• 42:46 – “Get a license” — Reich’s grandfather’s advice

• 43:12 – The fear of a coming “great bifurcation”

• 43:32 – The forklift-to-the-weight-room metaphor

• 52:24 – Jon’s call for real-teacher podcast guests

Here’s why. MIT, the institution that helped build the foundation modern AI sits on, just told its own faculty that the way they teach and assess students needs to be rethought, top to bottom. Not tweaked. Rethought. Every class period. What gets taught, not just how it gets graded.

One paragraph that validates what I have been saying for the past year and a half. The committee was describing what actually becomes valuable once AI can write, code, and summarize competently on demand. A professor watching a student reason through a hard problem. A lab session. An oral defense. An argument at a whiteboard. One student catching a flaw another student missed.

Thinking in the presence of others - thinking out loud - without the aid of AI - this is where I can truly see if a student has mastered the material. I have been saying for some time that the coin of the new realm (in the age of AI) will be things that are distinctly human. And giving an oral defense, seeing students think out loud, is the way to verify what they learned.

And it’s not just this committee. Justin Reich, who directs MIT’s own Teaching Systems Lab, has been saying something similar for a while now: nobody knows how to teach in the age of AI yet, and the way forward isn’t waiting for certainty — it’s actual teachers running real experiments in real classrooms. I’ll be releasing a conversation with Justin soon where he makes that case directly.

We were solving the same problem from opposite ends

I’ve been building the Mastery Viva for a year, and MIT is saying we need to reintroduce the oral defense. MIT’s committee is made up of professors, researchers, and graduate students. People with sabbaticals, teaching assistants, and the freedom to redesign a syllabus over a summer.

I teach high school juniors and seniors. Kids every day, 6 classes. I teach both struggling students and advanced students. I usually eat lunch in my room while I tutor kids. MIT is one of the most selective universities in the world. This is not my reality.

But the interesting thing is that I, a regular classroom teacher, and MIT landed in almost exactly the same place. Their answer to AI eroding written work as proof of learning is oral defense, in-class work, and protecting the human moments a machine can’t fake. My answer, built out of necessity in an actual classroom, is the same three things — I just gave them names: Mastery Vivas, Analog Roots, and AI Engines used on purpose instead of by default.

That’s not a coincidence. When you actually want to know if a student learned something, not just whether they turned something in, you end up in the same room, whether you started at MIT or in a geology classroom in Houston.

Where I’d push further than they did

To their credit, the committee didn’t pretend to have this fully solved. They flagged proctored, in-person exams as the current best fallback. Not because it’s the best possible answer, but because most AI-detection tools are unreliable and feel like surveillance.

I’d agree with that as far as it goes, but I don’t think it’s an either-or. My students still take real tests — multiple choice, worked physics problems, the whole thing — in a locked-down, secure testing environment. I still believe there’s real value in a student sitting quietly and working through a problem, or simply knowing a fact. That part hasn’t gone anywhere.

What’s changed is that a piece of every major test is oral. Not instead of the written test. Alongside it. The written portion tells me whether a student can work a problem and recall what they need to know. The oral portion tells me whether they actually understand it, because they have to explain it to me, live, with no script.

I’m not trading one form of evidence for another. I’m adding a layer that written work alone can’t give me. This is the same layer MIT’s committee was describing when they talked about watching someone reason through a problem in real time.

What this means if you teach kids, not graduate students

Here’s the gap nobody at MIT has to close, and every K-12 teacher does. Their report is right. It’s also written for people with almost none of the constraints you actually live inside.

You don’t get a sabbatical to redesign your assessments. You get August.

You don’t have one section of twelve doctoral students. You might have five sections of thirty.

You don’t set your own curriculum. Someone above you did, and you’re accountable to it whether or not it accounts for any of this.

So if a watershed report from MIT is right, but useless without a plan that survives a real bell schedule, here’s where I’d start:

1. Pick one assessment this month and add a two-minute oral component to it. Not a redesign of everything you do. One assignment. One conversation per student, even a short one.

2. Protect one block of in-class time from devices, on purpose, this week. Watch what your students do with their hands and their attention when the option to reach for a phone is gone.

3. Don’t try to out-detect AI. Their committee said it plainly: the tools are buggy and feel like surveillance. Stop trying to catch AI. Start trying to hear your students think instead.

MIT didn’t build this because it was easy. They built it because the alternative — pretending the last two years haven’t happened — was worse.

I’ll be back in my room tomorrow at lunch, same as always, listening to a kid explain a problem out loud. MIT can call that a finding. I just call it Tuesday.

Link to the MIT Report

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The selfie video. Early in the year, every student records a 2–3 minute video answering a few prompts: tell me about yourself, your hobbies, how you learn best, anything going on at home I should know. I watch every single one and build a grid document — name, photo, a few notes per kid. Then I send a personal welcome email that references something specific from their video (”you play harmonica? So do I”). That one small detail does more for the relationship than almost anything else I do all year — and later, when I’m walking the room during Vivas, that same grid becomes my cheat sheet.

The Friday parent video. Every Friday morning, three two-minute videos — one per class — recapping what we studied that week. I upload them to a shared Google Drive folder, no YouTube needed, and BCC the whole parent list. Total time: about fifteen minutes for three classes. The payoff is disproportionate — parents stop me at football games to say thank you, and when a student is struggling, that relationship is already banked before I ever have to make the harder call.

The Mastery Viva fix. I’ve been jumping straight to the level-4 question instead of guessing which level to ask first. If a student nails it, that’s a 4. If they need help getting there, that’s a 3. Separately — and this is a different thing entirely from the student-facing AI tool that’s still waiting on school approval — I used Claude to draft a bank of Viva questions from my own curriculum. It worked well after editing: about 6 of every 10 questions it generated were genuinely good, which meant previewing and cutting the rest myself.

Analog isn’t really about paper. The more I read, the less this looks like a paper-versus-screens argument and the more it looks like an attention argument. Devices pull focus through notifications and the pull to multitask, not through some inherent flaw in digital ink. I still do plenty of my own work on an iPad. The real fix is removing the ping, not the pixel.

A new study I can’t stop thinking about. Not yet peer-reviewed, but making real waves: 26,000 students in China, grades 7–12. Open AI access raised homework scores and cut homework time — and then tanked exam scores. The damage wasn’t spread evenly, though — it concentrated in students using AI as an easy button, while students who still put in the work alongside AI use barely lost ground at all. I dug into this further after recording and wrote it up properly here: [link to Substack piece].

One more thing. I want you on this podcast — not another expert, a real teacher with real kids in your room. Link to the guest form is below.

Appear on the podcast

0:00 Why I make every student record a “selfie video”
0:16 The exact prompts I give students
1:03 Why relationships matter as much as content
1:30 The personal welcome email trick
2:07 Using my student grid as a cheat sheet during Vivas
2:54 The 2-minute Friday parent video
4:37 How I set it up on Google Drive in 15 minutes
5:27 Fixing my Mastery Viva timing — jumping straight to level 4
6:03 Still waiting on our AI tool’s approval
6:42 I had Claude draft my Viva questions — here’s how it went
7:12 Why our schedule keeps eating into class time
7:34 Analog isn’t about paper vs. screens — it’s about attention
8:58 A new 26,000-student AI study I can’t stop thinking about
11:00 I want YOU on this podcast

One note: this is a working paper, not yet peer-reviewed. It’s already circulating widely among economists and education researchers, and the design is serious. But 26,000 students and thirty months of real school data are worth a deep read.

What is Causing the Learning Loss?

According to the researchers, the lower test scores were “concentrated among roughly 80% of AI users whose behavior is consistent with homework outsourcing.” In layman’s terms, the students had AI do the “thinking” for them, and when it came to a high-stakes test, they didn’t know it. This was evident when you see that their homework took significantly less time. I can just see students copying and pasting and moving on.

But Some Students Used AI Well...ish

The study wasn’t all bad news. 19% of the students who used AI spent the same amount of time on homework as before, and they did not see the exam penalty. The researchers said: “AI users who maintain similar homework completion time as non-AI users experience small learning losses.” I am not totally surprised.

I want to be careful here, though, because it’s tempting to read this as “these kids used AI well.” That’s not quite what the data shows. The paper doesn’t tell us these students found some clever technique or used AI as a tutor instead of an answer key. All we know is that they kept spending roughly the same amount of time on their homework, AI or no AI. That’s a behavior, not a skill. It’s closer to “they didn’t let the tool shortcut their effort” than “they figured out the secret to AI-assisted learning.”

I wish the study had asked how students used AI so we could suss out what helped and what hurt. I wonder if, as we learn more about the best way for students to use AI, we’ll find that students who use it as an actual tutor — one that pushes back, asks questions, makes them work for the answer — see real gains instead of just smaller losses. That’s a different, better outcome than just “didn’t lose as much,” and this study can’t tell us yet whether it’s possible at scale.

The Slow Leak — Why the Damage Takes Two Years to Show Up

This is the part of the study that concerned me most.

Regular exam scores hit their full damage within six months of a student starting to use AI. But the high-stakes entrance exams — the ones that actually determine which high school or college a Chinese student gets into — took two full years to reach their full penalty of 18 to 24%.

Why the difference? Those entrance exams cover material from before a student started using AI and after. Early on, most of what’s being tested is stuff the student learned the old-fashioned way, so the damage is diluted into the average. It takes years for enough AI-affected material to pile up and drag the whole score down. Which means almost every short study on AI and learning — and there have been a lot of them — is structurally incapable of catching the real cost. They’re not running long enough to see it.

There’s a hopeful thread buried in here too. The five-month penalty fell from “around 25 percent in early 2023 to around 16 percent by June 2025.” People are adapting. Slowly, imperfectly, but adapting.

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How should we use AI going forward?

I am obsessed with learning how we can positively use AI in our schools. So what is the best way to use AI? The researchers point out that their findings don’t contradict a Harvard study in which a well-designed AI tutor helped students learn physics faster than in-class active learning did. Their own explanation: “With a well-designed tool and highly motivated students, AI can enhance learning efficiency.” The difference isn’t the technology. It’s the design and the guardrails around it.

That’s the entire premise behind what I call an AI Engine: AI that’s built to make a student produce the thinking, not receive the answer. This study is the largest, most real-world piece of evidence I’ve seen that the design question isn’t a nice-to-have. It’s the whole ballgame.

One more line, this one for every teacher and parent who thinks homework grades still tell them something: “Better homework performance may coexist with weaker learning.” Read that twice. A student’s homework score can go up while what they actually know goes down, and you won’t see it until the closed-book test. I know I’ve said this before, but if you, as a teacher, send home cognitively complex homework, you are crazy. I now assume students are going to use AI. So that means we need to redesign how we do homework. That is why my homework now is primarily for students to watch my flipped videos. Homework has quietly stopped being a reliable signal, and we need something AI can’t do for a kid.

Implications for Assessment

I really want my students to not have the “AI Learning Penalty.” I want them to use AI in a way that enhances their learning. That is why I’ve moved toward oral Mastery Vivas, where students have to explain their understanding to me both formatively and summatively.

As the researchers put it: “students are gradually adapting to generative AI, but there remain substantial barriers.” Bad for most. Good for some. And maybe, if we’re honest about the design instead of the hype, better for more of them than it is right now.

Strömberg, D., Lei, V., & Wu, Y. (2026). The Generative AI Learning Penalty: Evidence from Chinese Secondary Education [Working paper]. June 2026.

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On the win side: Analog Roots delivered immediately. Laptops, phones, even smartwatches went into backpacks, and I watched my students default back to real conversation and paper research the moment their devices were out of reach — a shift that honestly took me back to my very first year teaching in 1986. The Mastery Viva also produced a standout teaching moment: walking the room, asking students about the atmosphere, revealed a real, widespread misconception (several students didn’t actually grasp what “atmosphere” meant, despite watching the video) — exactly the kind of gap a written assignment would never have surfaced.

On the honest friction side: a shortened 65-minute physics period (down from our usual 80, thanks to schoolwide safety meetings) meant I ran out of time to Viva every student who finished — a problem I’m still solving. Separately, EdPuzzle’s video-tracking caught three students who hadn’t done the pre-work; one was a new student who didn’t have his laptop yet, two had simply skipped it, and my in-class fix (headphones, a designated corner) worked for one but not the other.

I also share my early vision for using an AI tool to help scale Vivas for students I can’t personally reach in time — and I close with an invitation: I’m adding guest interviews this season, including upcoming conversations with voices from Harvard and MIT (including Dr. Justin Reich), and I’d love to hear from you too if you want to be a guest.

Appear on the Podcast

Key Timestamps

• 00:00 – Intro: wrapping Week 1 of MasteryFlip

• 00:24 – Analog Roots in action: notebooks, phones away, “minds on”

• 01:16 – A colleague’s parallel observation: kids talk to each other instead of grabbing devices

• 03:16 – The atmosphere/geosphere misconception — the Viva catches something a worksheet never would

• 04:51 – Coaching students to “ask a better question”

• 05:41 – What didn’t work #1: no time for physics Vivas

• 06:11 – Root cause: schoolwide safety meetings shortened the period to 65 minutes

• 06:46 – What didn’t work #2: EdPuzzle catches students who skipped the pre-work video

• 08:49 – Notebook quality: setting expectations without penalizing (yet)

• 09:29 – The vision: piloting an AI tool to help reach every student

• 10:20 – ⚠️ What Vivas reveal about which students need support

• 10:51 – The clipboard system (and covering names for privacy)

• 11:14 – The “binder vs. notebook” Texas vernacular moment

• 11:50 – Guest invitation + upcoming Harvard/MIT interviews (Dr. Justin Reich teaser)

• 13:44 – Closing thoughts: humility, Cognitive Load Theory, and “we’re in a new era”

I posted a short video on the morning of my first day of school. This is my forty-first year in education — six of those years I spent consulting with teachers, two more running a school’s tech department, the rest in front of students. Nothing scripted. Just me, an empty room, and a stack of red boxes full of rocks and minerals, an hour before students walked in.

The replies came in fast. Most were kind. A lot of them were from other lifers — thirty-sixth year, thirty-seventh year, one guy starting his thirty-fifth after retiring twice.

But two replies stopped me.

One was from a retired teacher who said she left “crawling to the door.”

The other was fuve words long: I can’t do it anymore.

I’ve been thinking about those two comments for a week. Because the honest question underneath them isn’t “how do you stay excited?” It’s harder than that. It’s: what’s wrong with me that I couldn’t?

Nothing. That’s the answer. But it deserves more than five words back, so here’s the longer version: why I’m still excited to be a teacher, this far in.

First, the part most of these posts leave out

I get to teach in a place that supports me. My administration lets me try things that probably look insane from the outside. I’ve had years where that wasn’t true, and I know the difference, and I’m not going to pretend the difference is attitude.

A teacher grinding through a building with no support, no supplies, a hostile evaluation system, and a hundred and eighty students isn’t failing at joy. She’s responding correctly to her conditions. If you left crawling, you didn’t lack grit. You ran out of a thing that was never being replenished.

So nothing below is a prescription. It’s just what’s kept the fire lit for me, offered in case any of it transfers.

I have never taught the same year twice

There is an old saying for some teachers. They haven’t taught 30 years; they’ve taught the same year 30 times. For me, I am driven by the question: What is best for students? What will help them become thriving human beings? And because the world is always changing, I need to adjust my teaching every year. A few years ago, one colleague told me I made him tired. I kept changing things up, and he wanted everything to stay the same. I understand his sentiment, but maybe I’m just not wired that way. I feel a need to always reflect and ask what worked, what didn’t, and how I can improve.

I remember in the 90s when my student teacher suggested I move to interactive notebooks - and so, boom - that next year I made the big switch. I did that for a few years, and then I was onto something I felt would be better. Or, in 2007, Aaron Sams and I saw that students were missing so much class that we came up with the idea of having them watch videos so they could use class time better. Little did we know that we were birthing the Flipped Classroom movement. Or in the past year, I realized that AI was robbing my students of productive struggle, and I needed to find a way to address this.

This year I’m going backward on purpose. Pencils, paper, real conversation. Content still happens at home, but the room is analog now.

I piloted this method last spring, and this year it’s off to a genuinely strong start — my students are talking to each other, and away from distractions, they’re leaning into the learning. But I already know it isn’t clean. Come Monday’s episode, I’ll get into exactly where it cracked in the first week — the mastery viva didn’t fit into a truncated physics block, and I’m still working out whether it has to happen every single time. That’s not a footnote. That’s the actual answer to how this is going to go: some of it works, some of it breaks, and I find out which is which by running it, not by predicting it.

That’s the whole thing. I have never once known how it was going to go. The teachers I’ve watched burn out weren’t tired of children. They were tired of certainty — of knowing exactly how October would feel, because it had felt that way many Octobers before.

Manufactured novelty is not a gimmick. It’s maintenance.

I stopped being the performer

For my first nineteen years in the classroom, my job was to be interesting for fifty minutes five or six times a day.

That’s a stage career, and stage careers exhaust people. When I moved the content out of the room, the job changed. Now I spend the period walking around, sitting down next to kids, asking them what they think, listening to them be wrong out loud, and watching them fix it.

Performing drains you. Conversation doesn’t. Same hours, entirely different fuel cost.

I remember a teacher visiting my classroom some years ago who asked me how I was still teaching all these years later. She had just spent the day with me and had interacted with my students. After asking, she paused and answered her own question. She said, “Your class is a conversation with students.” I smiled.

I remain a learner — be a novice

For a career teacher, it is easy to know your subject so well that you can do it cold. I remember in about year 15 that I knew the subject so well I would just look at the topic and go. I would stand up (performer mode) and do a lecture without notes. At first I was proud of that, but in retrospect I had stopped being a learner.

My students are novice learners of geology and physics. I need to find areas where I am a novice. A few years ago I decided to learn Spanish. I’m still not fluent, but I keep learning. This morning I completed my 2299th day streak on Duolingo. And this year I am teaching AP Physics C - the C stands for calculus. And my last calculus class was 43 years ago. I am for sure a novice in this arena.

All this to say, we need to allow ourselves to be novices so that we can empathize with our students. So go out and take violin lessons. Or do a dance class, or ask AI to help tutor you in whatever interests you.

Being a beginner at fifty-nine or sixty or sixty-two is uncomfortable, and it is exactly the feeling I’m asking my students to have every single day. I don’t think you can ask a kid to sit in productive struggle if you haven’t been in it recently yourself.

The fire isn’t a personality trait

Here’s what I actually believe after forty-one years around classrooms, in one role or another.

Excitement isn’t something some teachers have, and others lost. It’s a byproduct. It shows up when the work is still uncertain, when you still see individuals instead of sections, and when someone above you gives you enough room to try something that might fail.

Take away any one of those, and it goes out. That’s not weakness. That’s physics.

To the person who said I can’t do it anymore — you gave more than most people ever will to a job that mostly doesn’t say thank you. I hope you find something that gives back.

And to everyone about to walk into a room full of kids who don’t know you yet: I still don’t know how this year is going to go. But I know the question that gets me there. What is best for my students? Go find that answer.

Reach every student.

— Jon

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In this episode:

• How I communicate the MasteryFlip framework to students and parents before day one

• Setting up AI Engines: why I’m sticking with EdPuzzle this year (and where Perusall might fit for you)

• A tour of my Analog Roots notebooks — GMO and AP Physics C

• Why laptops live in backpacks, not on tables

• The seating-chart method I use to memorize every student’s name in two days

• What’s changing with the Mastery Viva this year (3-point → 4-point thinking)

• My day-one script and the “question of the day” ritual

Links mentioned in this episode:

• Syllabus Blurb - see the bottom of the show notes

• MasteryFlip Certification Course (v1.1, just released):

• Bring MasteryFlip to your school — free licenses for an admin + one teacher to try it:

• Using MasteryFlip in your classroom? I want to talk to you. Reach out here to be a guest on the podcast:

• 2026 Parent Video

Follow along:

• YouTube: youtube.com/jonbergmann1

• Instagram: instagram.com/flipping.jon

• TikTok: tiktok.com/@jon_bergmann

• X: x.com/jonbergmann

• LinkedIn: linkedin.com/in/jonathanbergmann

Go out and reach every student.

Key Moments

0:00 – Cold open: “Jack, Cade, Haley...”

0:15 – Welcome & why this episode

0:57 – Communicating MasteryFlip to students & parents

2:15 – AI Engines: EdPuzzle vs. Perusall

4:03 – Analog Roots: the notebooks & room setup

7:16 – Scripting day one

9:41 – Mastery Vivas: upping the game to 4 points

11:04 – How I memorize every student’s name

16:51 – MasteryFlip Certification v1.1

17:26 – Closing: a story about Mia

Blurb from my Syllabus for the upcoming year

How the Course Will Operate: The MasteryFlip

This class uses a Flipped Classroom model, a proven approach designed to make our time together in class incredibly valuable. Instead of listening to long lectures in class you will learn the foundational concepts at home through short video lessons.

This flips the traditional model on its head: you get the “rewind button” for the easy part (the lecture), and you get ME right next to you for the hard part (the application, labs, and problem-solving). This empowers you to take ownership of your learning, move at your own pace, and get personalized support every single day.

Pre-Work Expectations (Before Class)

To make our in-class time work, you need to arrive prepared. For every flipped lesson:

• Watch & Interact: Watch/read the assigned lesson by midnight the night before class. Don’t just play the video passively—pause it, re-watch tricky parts, and try to solve example problems on your own before I show the solution.

• Handwrite Your Notes: Take detailed, handwritten notes directly in your provided physical packet. You must bring this packet to class every day—it is your primary tool for our in-class activities and Vivas.

• 3-Minute ArticulAI Practice: Complete a quick, 3-minute oral check-in with ArticulAI. This gives you immediate practice speaking your ideas out loud so you’re ready for our in-class Mastery Vivas.

Note: If you arrive to class without your pre-work completed, you will spend the first part of class catching up on the video independently before you can join the lab or group activity.

Analog Roots: Pencils Up, Phones Away

When you walk into this classroom, we return to our Analog Roots. That means your cell phone is put away in your bag, and your laptop remains closed unless we are using it for a specific, teacher-led activity. Our default mode in class is paper, pencil, and real human conversation.

Here is why we do this:

· Real learning requires productive struggle: Tapping a screen or asking an AI for a quick answer gives you a fast result, but it skips the brain effort needed to actually learn. Wrestling with a tough problem on paper—erasing, re-drawing, and trying again—is where real understanding develops.

· Handwriting builds stronger brains: Brain science proves that taking notes by hand and drawing physical diagrams creates much stronger memory connections than typing on a keyboard. Your physical packet is your ultimate learning tool.

· Class time is for human connection: You spend enough of your day looking at screens. Our class time is sacred—it’s designed for you to talk to your peers, work through labs with your hands, and get direct 1-on-1 support from me.

I’m not anti-technology: we use powerful tech like ArticulAI intentionally when it serves a purpose. But during class, we turn off the digital noise so you can focus, collaborate, and truly master the material without constant distractions.

Mastery Vivas

A Mastery Viva is simply a short conversation where you explain your thinking out loud instead of just filling out a worksheet or staring at a screen. Throughout our units, we will use these quick check-ins to make sure you are truly mastering each lesson in real time, catching any confusion early before we move on. Because I can’t sit down for a 1-on-1 chat with every single student every day, about half of these lesson checks will happen through an interactive online tool called ArticulAI. It acts as your practice partner, allowing you to speak your answers and get immediate feedback. The other half of the time, you’ll talk directly with me. These lesson checks are scored on a simple 4-point scale: earning a 3 means you thoroughly know and understand the core material, while a 4 means you can take that knowledge and successfully apply it to a brand-new, unfamiliar situation.

When it comes to our major tests, you will use these same communication skills to prove what you know. On every major test, alongside the written portion, you will defend one conceptual question out loud directly with me. You’ll explain why your answer works and walk me through your logic. This quick oral defense counts for 15% of your total grade on each major test, giving you a powerful way to show true understanding through real dialogue rather than just memorization.

As I sit in my classroom, students are soon to arrive. I am preparing for what I hope will be an outstanding year. This will mark my 41st year in education, and as I prepare, I’m reminded about just how much what we teachers do matters. We have a unique opportunity to reach every student.

And boy are we facing challenges. AI has changed the world and certainly changed our classrooms. There is a growing techlash from parents, policymakers, and fellow teachers - that the misuse of technology in the classroom has harmed kids.

In the midst of all of this change, I come into this school year feeling extremely positive. Last year I felt that I had more impact and connection with students than ever in my long career. I attribute much of this connection to my implementing the MasteryFlip Framework, where human-centered conversations play such a big role. Built into the model are “mastery vivas,” short targeted conversations. And though the conversations focused on science stuff, it also helped me get to know my students so much better.

Free 2-Seat License for MasteryFlip

And over the summer, I continued to think about how to make this model better. In this episode, I walk through the steps I will be taking in the coming year to teach in the age of AI. Below are a few thoughts:

Improvements to the MasteryFlip Model

• 4-point scale for formative vivas: Last year, after a student did a mastery viva with me, I either gave them credit or not. This year, the rubric will change. If they understand the content of a lesson, they get a 3. If they can extend it, they get a 4. My hope is that they will strive for deeper understanding and even use AI to prepare them for the vivas.

• Vivas on Summative Assessments: My amazing colleague, Erin - a math teacher - took the MasteryFlip Certification course and brainstormed with me about how she would implement it. She will use vivas formatively, but also make it a part of summative evaluations. I did this with a big PBL project last year (The Trebuchet Project) - but I didn’t do it on a regular basis. This coming year, I will make every student answer one question orally on every summative assessment, and it will account for a decent portion of their grade.

MasteryFlip Certification

Teachers and teams of teachers from around the world are starting to use the MasteryFlip Framework. I just got off a Zoom call with the Ministry of Education for a province in Missiones, Argentina, and they will be making it their primary teaching strategy for PD in the coming year.

If you are interested in becoming MasteryFlip Certified, you can simply go to the links below. There are three options:

• Individual License

• Team of Four or More (50% off)

• Free Pilot for one administrator plus one teacher

Season 4 Teaser

Starting in early September—when schools nationwide are fully back in session—Season 4 will feature deep-dive interviews with world-class education leaders

• Dr. Tony Wagner (Harvard/LPI) on why the global economy no longer cares what students know, but what they can do

• Dr. Tina Grotzer (Harvard Project Zero) on the biological necessity of paper friction and user manuals for the mind

• Dr. Justin Reich (MIT Teaching Systems Lab) on why Silicon Valley engineers can’t train novices with AI copilots

• And others…

Episode 85 Key Moments: The Human-Centered Classroom Blueprint

• 00:00:00 — Season 4 Kickoff in Room 231: Setting up for Year 41 and navigating the nationwide classroom “techlash.”

• 00:01:08 — The Free 2-Seat Executive Pilot: How administrators can claim two free course passes (Principal + Lead Teacher) with zero district budget risk.

• 00:02:08 — Reclaiming Analog Roots: Why replacing online auto-graders with handwritten AP Physics workbooks stops students from bringing a “forklift to the weight room.”

• 00:03:44 — The 3 vs. 4-Level Viva Scale: Scaling oral checks with Teacher of the Year Erin (Math) and handling parent pushback on grading.

• 00:05:37 — The 15% Viva Test Bucket: Integrating 3-minute “questions out of a hat” oral defenses directly into summative exams.

• 00:07:46 — AI as a Rehearsal Coach, Not a Cheat Button: Reprogramming Flint K-12 to run practice simulations before students face the real teacher.

• 00:09:01 — Season 4 Fall Guest Preview: Announcing full-length September interview drops featuring Dr. Tony Wagner (Harvard), Dr. Tina Grotzer (Harvard), and Dr. Justin Reich (MIT).

• 00:10:48 — 41 Years in the Trenches: Why human connection and student relationships matter far more than rushing through content.

So how do we move forward with a more nuanced approach that embraces the best use of technology while also giving students space and time to process and learn in an environment where they will thrive?

Claim Trial Seat for a MasteryFlip Pilot

To that end, I have developed the MasteryFlip model, which takes into account the reality that AI, when used as a shortcut, will harm students while also leveraging its strengths to prepare them for a future world where they will have ready access to all things AI.

In this video podcast, I share an extended keynote where I mapped out the MasteryFlip framework for professors at a medical college in Brazil. I share the blueprints - this is a masterclass on how to teach in the age of AI. In this video/episode I walk through the three pillars of the MasteryFlip framework and take you into my classroom and see how it actually works. You will virtually meet a few of my students and watch real “mastery vivas” where my students have to defend their knowledge. I am also vulnerable and tell you how I have failed and tweaks I will make this coming year.

If you want to get MasteryFlip certified, I created a four-hour class that teachers and professors can take to prepare to teach in the age of AI. Teams of four or more can get 50% off.

Learn more MasteryFlip

Are you a school principal, dean, or department chair trying to figure out how to handle AI in your classrooms? We’re offering a sponsored 30-Day Executive Pilot of our Harvard and MIT-backed MasteryFlip Framework for school leaders. Head over to jonbergmann.com/masteryflippilot to claim a trial seat for your school today.

Claim Trial Seat for a MasteryFlip Pilot

Enjoy this video podcast. I hope you can get the passion for which I share about the future of education.

Key Moments in this Episode

• 00:00:52 — The 1-Teacher Pilot Offer: How building leaders can test an AI-proof classroom this fall with zero district budget risk.

• 00:05:11 — Sal Khan’s Treadmill Problem: Why Khanmigo backtracked—and why AI tutors become glorified laundry racks without a human coach.

• 00:08:41 — Pillar 1: Socratic AI Engines: Reprogramming large language models to ask questions instead of handing out answers.

• 00:29:11 — Pillar 2: Analog Roots: Enforcing the “Lids Down” protocol to reclaim focus, paper notebooks, and biological friction.

• 00:31:41 — Pillar 3: Mastery Vivas: How 120-second oral check-ins human-proof your assessment data and eliminate cheating.

• 00:40:02 — The Trebuchet Defense: Why forcing high schoolers to verbally defend a physics project on a whiteboard stops them from faking lab reports.

• 00:49:59 — Anesthesia vs. Antiseptics: What 19th-century medical history teaches us about why frictionless tech spreads fast while meaningful classroom change takes decades.

We are watching a quiet educational rollback. Systems are buying paper textbooks again, subscription software budgets are being frozen, and the “techlash” movement is gaining massive steam. At several recent spring graduations, when commencement speakers attempted to extol the frictionless virtues of artificial intelligence, college graduates openly booed them.

So, is technology inherently evil in our schools? Should we retreat into a permanent Luddite posture? Is all generative AI toxic for our students?

As a 41-year classroom veteran who was at the forefront of the global Flipped Learning movement, I see the real, systemic dangers of automation in my room every single day. I have watched firsthand as students use shortcuts, offloading their cognition to AI and actively stupefying themselves. Using AI to complete an assignment is like bringing a forklift into the mental weight room: you get a clean floor, but you build absolutely no muscle.

But I also refuse to be a Luddite. There is a definitive path in which technology serves as an engine for human expertise rather than a bypass of it.

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Our unwavering priority must always be to protect the productive struggle—ensuring our students thrive by leaning into the messy, irreplaceable qualities that make us fundamentally human.

In the masterclass video above, I lay out the explicit operational architecture to solve this cognition crisis. The centerpiece of this video is the unreleased keynote address I delivered in May 2026 to university deans and medical educators in Curitiba, Brazil. In it, I break down the physical mechanics of the classroom space and outline the definitive solution.

That solution is a new structural system I developed called the MasteryFlip Framework. To turn our classrooms into true attention sanctuaries, we must integrate three distinct pillars:

• AI Engines: We shift raw information delivery completely out of the room. Using the best elements of Flipped Learning with an AI twist. This is the flip in the MasteryFlip

• Analog Roots: The moment the bell rings, the devices disappear. Laptops go into backpacks, phones are put away, and desks return to raw pencil, paper, and physical text real estate to enforce biological neural friction.

• Mastery Vivas: We completely change the assessments. To make grading entirely AI-proof, student evaluation is moved back to a face-to-face, human-to-human verbal defense of logic. If a student cannot defend their thinking on the mastery bench, they do not move forward.

If you are a principal, curriculum coordinator, or department head ready to standardize this across your entire building this fall, you don’t have to navigate the transition in isolation. I have packaged this into a fully asynchronous, four-hour MasteryFlip Certification Course.

Are you a school principal, dean, or department chair trying to figure out how to handle AI in your classrooms? We're offering a sponsored 30-Day Executive Pilot of our Harvard and MIT-backed MasteryFlip Framework for school leaders. Head over to jonbergmann.com/masteryflippilot to claim a trial seat for your school today.

Claim Trial Seat for a MasteryFlip Pilot

Educational leaders are moving fast to get ahead of the autumn semester. In fact, just today, a block of nine educators from a single school registered together.

To support building-wide deployments, if you register a team cohort of four or more educators from your campus, the price is cut in half.

Stream the full presentation above to understand both the problems and the promise of AI in your classrooms. Look inside my physical classroom and see how to scale real-time oral defenses across high-capacity rosters without causing staff burnout.

If your school is ready to survive the techlash and hardcode focus back into your classrooms, click the framework link below to claim your team passes.

Click on the link below to get your building/team MasteryFlip Certified

Get MasteryFlip Certfied

Quotes from Certified MasteryFlip Educators

"I think this is my favorite opportunity for AI use... the Socratic Mastery Viva thought partner (either practice for the summative with the teacher), or similarly as a check on understanding and completion of pre-work before the next class." — Certified MasteryFlip Participant

"I love a good 20 minutes (30 if I'm being honest) of direct instruction during class. I do sincerely think it is one of my strengths as a teacher and it is much fun for me, which helps me sell it. However, I'm not sure each and every student is learning enough from it to warrant the use of class time in that way to that number of minutes." — MasteryFlip Graduate

"Relying only on the finished project invites the temptation of students using AI or not actually showing mastery. By having a Mastery Viva or human aspect to the grading it allows for that friction and critical thinking to take place in front of the teacher." — Classroom Teacher

Key Moments in the Presentation

• 00:00:00 — The ISTE Shift: Analyzing the macro trend of school boards, superintendents, and curriculum directors freezing software budgets.

• 00:00:11 — The Crisis of Trust: Why the flight away from unvetted software isn’t a luddite retreat, but an institutional demand for learning integrity.

• 00:00:39 — Seceding to Big Tech: Confronting the reality of placing frictionless devices in student hands without a protective framework.

• 00:00:48 — Stupefying the Novice: How offloading thinking to generative AI causes cognitive drift and atrophies student logic.

• 00:01:02 — Redesigning the Environment: Why the solution to automated shortcuts requires changing the physical space, not buying another software patch.

• 00:01:15 — The Curitiba Keynote: Transitioning to the full, unreleased presentation delivered to college deans and educators in Brazil.

• 00:01:47 — The MasteryFlip Architecture: An introductory blueprint of the three core pillars: Socratic AI Engines, Analog Roots, and Human Checks.

• 00:03:01 — Real-Time Momentum: Establishing institutional social proof by highlighting the nine-teacher team cohort that registered today.

• 00:13:59 — Forklifts in the Weight Room: Deconstructing Ted Chiang’s metaphor on why schooling must remain intentionally inefficient by design.

• 00:19:02 — Case-Based Collaborative Learning: Lessons from a site visit to Harvard Medical School on why active methodology packs lecture halls.

• 00:20:22 — The Threat to Clinical Reasoning: Why textbook-perfect AI data shortcuts de-skill training novices who face unpredictable real-world environments.

• 00:21:46 — Friction as a Feature: Why engineering deliberate resistance back into assignments is the only way to build biological cognitive muscle.

• 00:22:20 — The Papal Manifesto: Analyzing the Holy Father’s warning on how frictionless automated summaries extinguish the human desire to ask questions.

• 00:23:44 — The Rockefeller Symposium: Why a room of nuclear engineering and computer science professors concluded that flipping the class is the only answer to generative AI.

• 00:25:25 — Flipping Bloom’s Taxonomy: Transitioning direct instruction out of the group space to maximize face-to-face cognitive lifting.

• 00:29:12 — Reprogramming the AI Engine: Moving away from automated answer generators toward interactive, sandboxed Socratic dialog tools.

• 00:33:05 — The Mastery Viva: Reintroducing the oral exam as a two-minute formative conversation to human-proof your assessment data.

• 00:35:40 — Handling High-Capacity Rosters: Strategies to scale oral defenses across large groups using secure LMS wrappers and assistants.

• 00:38:34 — The Path of the Thriver: A final review of the great bifurcation and why mastery learning keeps kids on the winning side of the automation wave.

• 00:40:22 — Reclaiming Samuel: The real-world story of a high school football player who rejected the easy button to achieve a perfect score on an honors physics re-test.

• 00:42:52 — Hardcoding the Struggle: Why prospective teachers and veterans must design environments that protect the biological struggle of learning.

• 00:45:19 — Remaining Profoundly Human: The pressing duty of educators to defend human dignity against new forms of mechanical dehumanization.

I lived this week in what I am calling “learner mode.” I stepped into the shoes of my typical student who might be lost with the concepts I teach. I lived with productive struggle all week. And you know what? It was good for me! Not only did I make significant progress in my comprehension, but it gave me empathy for my struggling students.

In this week’s episode, I reflect on what it means to be an uncomfortable learner. I also encourage you, teachers, to find something you don’t know and haven’t mastered and go into learner mode.

I am increasingly convinced that, in the age of AI, the three-pronged approach to teaching is best addressed by the MasteryFlip Framework. I needed to spend my week analog working through difficult concepts, and when I verbalized my understanding (Mastery Vivas), the presenters (and the other participants) were quick to correct my thinking - which led to greater comprehension.

If you are still sitting on the fence about the MasteryFlip Certification course, here are a couple of comments from some of our recent graduates:

“I think this is my favorite opportunity for AI use... the Socratic Mastery Viva thought partner (either practice for the summative with the teacher), or similarly as a check on understanding and completion of pre-work before the next class.” — Certified MasteryFlip Participant

“I love a good 20 minutes (30 if I’m being honest) of direct instruction during class. I do sincerely think it is one of my strengths as a teacher and it is much fun for me, which helps me sell it. However, I’m not sure each and every student is learning enough from it to warrant the use of class time in that way to that number of minutes.” — MasteryFlip Graduate

Are you a school principal, dean, or department chair trying to figure out how to handle AI in your classrooms? We’re offering a sponsored 30-Day Executive Pilot of our Harvard and MIT-backed MasteryFlip Framework for school leaders. Head over to jonbergmann.com/masteryflippilot to claim a trial seat for your school today.

Claim Trial Seat for a MasteryFlip Pilot

Now go out and reach every student.

Jon

Key Moments in the Podcast

• (00:00:00) — Entering Learner Mode: Sitting in the “productive struggle” at the week-long AP Physics C symposium in Chicago.

• (00:01:05) — A Dose of Humble Pie: Navigating a calculus-based course after a 42-year break while surrounded by physics master’s degrees.

• (00:01:51) — Sleeping on It: How a night of sleep cleared cognitive overload, allowing a deep dive into the calculus of “moment of inertia”.

• (00:02:54) — The Derivation Breakthrough: Confronting the power rule and chain rule, and how stepping out of your comfort zone in your 41st year of teaching builds deep student empathy.

• (00:04:32) — Validating the MasteryFlip Framework: How this intense experience proved the need for analog roots and real-world human verification over the digital “AI easy button”.

• (00:06:55) — The “Justify” Hurdle: Failing a sample board problem’s justification criteria, and why students must write conceptual sentences rather than just throwing down math equations.

• (00:08:55) — The Roadmap for Next Year: Planning workflows for the new class using AP Central videos for the AI engine alongside printed paper packets.

• (00:10:41) — Special Editions & Open Invitations: Teasing the upcoming full-length interview drops with Harvard and MIT professors, and inviting MasteryFlip practitioners onto the show.

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