I Like You, But You're Wrong

What happens when four friends agree to disagree

Hey fellow SDLers!

We’ve got some updates for you.

We completed our chapters right on time for the upcoming collaborative volume

Emerging Approaches to Transparency & Reflection: Navigating student+AI work in higher education.

🙌 🥳 🥂

Now the real fun begins.

And tbh, I’m enjoying this part way more than I should be.

I wrote this piece for two reasons:

I love vibe creating and I want to encourage others to try it.

I also want to give this community of the people supporting SDL from early on direct access to the work happening behind the scenes.

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Four Takes, One Book

This book is doing something I think is really unique in the space right now.

We’re bringing together four people who genuinely respect each other’s ideas about and contributions to the discourse about AI in education but who completely disagree about what that looks like in the case of transparency and reflection.

I mean, how cool is that? The world needs more of that. Am I right?

Susan Ray argues that learning happens through sustained reflective practice over time. Her chapter builds a framework around the AI Transparency Journal: a semester-long documentary record that makes growth visible, traceable, and earned rather than claimed.

Doan Winkel argues that learning happens in the decision-making process of collaborating with AI. His chapter makes the case for audit trails, which is a process of cataloging your choices with AI in real time, because the trail is the assignment.

Jason Gulya argues that learning happens in the productive struggle of attempting metacognition, even when students get it wrong. His chapter makes the case for practicing metacognition, even if its accuracy is low.

My chapter argues that learning happens invisibly, even to the learner, and that reflection misses the point entirely. Reflection is dead. We need a better way.

The chapters follow a similar basic structure but are allowed to go where they want within it.

Each chapter starts with a narrative intro, makes the primary claim, supports that claim with research and reasoning while also preempting counterarguments, and ends by showing what the preferred approach looks like in practice.

That alone is worth the price of admission for the book.

But we wanted to do more. And that’s where our work over the next two weeks takes us.

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I Like You, But You’re Wrong

Our charge now is to review each other’s chapters for the sole purpose of critiquing them.

Of course, we’ll highlight points of agreement and moments where an argument seems compelling. But the real task is undermining.

We are politely attacking each other’s positions. Through our individual chapters and our written responses, we want to make the strongest case that the view we presented should be preferred.

There’s no ego here, or not much. And there’s little more than bragging rights on the line.

Still, we hope there’s more at stake for our readers. We hope you will experience the book the same way—as a testing ground of ideas for shaping what is possible and even preferred while navigating student and AI work.

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Vibe Critiquing

By far, this has been the most fun part of the process for me, because I love what AI lets me do.

Here’s the setup for how I’m creating my chapter critiques.

I have the full text of all four chapters loaded into GPT and Claude. Each knows which argument is mine and what evidence I’ve brought to bear for it.

Now I’m reading each of the other chapters section by section and dictating my visceral responses to them through Wispr Flow into Notion.

I’m not hedging. There’s literally zero attempt to be polite. I’m not even sure my critiques are accurate as I’m making them. But they’re mine and they’re happening in real time. I want my critiques to capture my real feelings about the ideas.

My next step is to copy my reactions into both Claude and GPT and ask for three things:

Validate and synthesize. I want to see what’s actually happening under the surface of my reactions.

BTW, this is almost always my first step when vibe creating—dictate, then don’t create from it, but have AI read back what is going on with my words. It’s so powerful.

Stress test. I want to know what’s valid in their chapters, what’s valid in my critique, and where each of us is missing something important.

Outline a response. Given all of it, the LLMs produce an audience-facing critique that names both alignment and the places where an argument is asking for more than it proves.

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Then I Write It

For the final step, my plan is to take all the critiques from every section of each chapter—my dictated texts, the LLM outputs, the full conversations—review and respond to them in the same way I did to the chapters themselves (meta level) then draft the actual chapter response in my own words.

As AI would say, “This isn’t less work. It’s more.” But I think what I produce will be both more charitable and much sharper.

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Vibe Creating

If you’re not vibe creating, why not?

If you haven’t tried Wispr Flow, you really should. Here’s my link — you can get a free month of Pro if you like it. https://wisprflow.ai/r?PATRICK3861

As always, thanks for being the best part of this journey.