94 days until launch . . .
SDL's Second Official Volume: Emerging Approaches to Transparency & Reflection
A few days ago, I scrolled to the top of the group LinkedIn chat.
I had sent the message on March 11, 2026 confirming that Doan Winkel , Susan Ray , Jason Gulya, and I would be writing the four chapters for what is now Second Draft Labs’ first completely on-purpose publication:
Emerging Approaches to Transparency & Reflection: Navigating Student+AI Work in Higher Education.
Somehow, again, we had delivered our work right on time and in record time—just 75 days after project kickoff our chapter drafts were submitted.
Now the next countdown begins: just 94 days until launch. ⏰
Obsessed with Thinking
Here’s how this volume came to be.
I follow the work of Doan Winkel , Susan Ray , and Jason Gulya closely. I learn so much about teaching, posting, creating, and connecting from them. At the time this idea came to me, I was writing a really long Substack series on metacognition which folded into several posts about our obsession with “seeing” student thinking.
And, as is usually the case, I’m pretty sure that that work was informed by what I had been reading from Doan, Susan, and Jason who were sharing differing perspectives from mine to this challenge of “visibility” into the originality and development of student thinking.
If you put the four of us into a Venn Diagram, there is going to be a ton of overlap amongst us around some of the most foundational principles of education. And yet here—in this exploration of what teachers need to know from students, or what teachers should be encouraging for students—I saw very different, perhaps competing, maybe even incompatible positions.
And that’s what makes this volume specifically so interesting. In What Education Becomes, the chapters were written very much in “isolation” from each other. I selected the contributors simply because I appreciated the unique thoughts they had about AI and education. I wasn’t trying to position them against each other, or even with each other in any meaningful way.
This volume is different.
In this volume, the goal is very much to position different approaches to the same problem against each other.
The Problem Space
Here’s what we share as common ground:
AI can generate the assignment. Whether it’s a literature analysis, a business case study, or even a reflection on learning, current AI systems can produce competent work in seconds.
Students can use AI without our knowledge. Even if we wanted to detect or prevent AI use (we don’t), students can generate content, modify it to appear authentic, and submit it as their own. The arms race between detection and evasion is unwinnable.
Students still need to learn. This is where it gets interesting. We’re not content to throw up our hands and say “let AI do everything.” We’re educators. We believe students need to develop genuine capability. But if AI can do the visible work, what does that capability look like? And how do we facilitate its development?
Traditional evidence of learning is compromised. The essay doesn’t prove they can write. The project doesn’t prove they understand the content. Every output can be AI-generated or AI-enhanced to an extent we can’t determine who did what and what it means for learning.
This creates a genuine pedagogical crisis. We aren’t thinking about: “how do we catch cheaters” but “what do we ask students to do that actually serves their learning? And how do we know it’s working?”
That’s the question driving this volume.
How the Book Works
Here’s what this will look like in practice:
Each contributor will argue—through anecdote, through experience, and through research, and as vigorously as possible—for their specific position. And after each chapter, each contributor will then critically—though irenically—respond to each other’s chapter, questions assumptions, offering objections, and further strengthening the case for their own preferred approach.
I imagine this experience to be very much like the proverb, “The first to state his case seems right, until another comes and cross-examines him.”
That’s the ride I hope this volume takes readers on. And from that experience, I hope other emerging approaches and ideas take hold, get tested, get shared, and help us continue to imagine what education becomes after AI.
The SDL Community
Our website is live: seconddraftlabs.com/transparency-and-reflection
Cast your vote for whose chapter will make the best case. No hurt feelings. Just some early competition.
And then join the community.
Sign up, pick your team, and earn points for every action you take between now and launch — sharing, recruiting readers, pre-ordering, reviewing. The members at the top of the leaderboard win some real things. We’re still finalizing what that is, but I can tell you it’s not gonna suck!
New actions will unlock as the launch gets closer. The biggest points drop once pre-orders open.
We need our most committed readers to help spread the word!
Start here → seconddraftlabs.com/transparency-and-reflection