AI Conversations Are Texts. We Can Teach Them That Way.

A writing teacher’s method for modeling interactions that transfer.

The modeling of effective AI use has been an increasingly contentious topic in education circles, and for good reason. Ethical or effective AI use is highly context-specific, situational, and circumstantial in nature. Its definition shifts with the type of bot, task, student, and more.

The current problem is that the generally accepted method for guiding students to meaningful AI use has settled around the delivery of prompt engineering acronyms, mentor prompts, and frameworks for ethical use. Mentor prompts and acronyms, in particular, act as helpful starting points, but do not guide the entire interaction.

Providing guidance around an initial prompt construction and expecting a meaningful interaction with a GenAI tool is similar to a Writing Teacher providing a student with sentence stems and expecting a well-written essay. Writing teachers will recognize the shortcomings of such an approach immediately. Pedagogically speaking, the scaffold does an effective job at getting a student (in this case, an AI user) started. But it doesn’t layer in a meaningful understanding of the universe of choices that an AI user (read: writer, communicator, language-based design thinker) can (and probably should) consider. To use a layman’s analogy, the scaffolding of a building is not the foundation. We need to layer in the foundation first.

How I Learned To Teach Writing

What I’ve found in my experiments in trying to teach AI literacy (specifically, in the chat itself) is that the pedagogical and instructional methods of early-stage literacy classrooms are highly effective.

Here’s a sample of how I learned to teach writing at the middle school level approximately a decade ago — and how I continue to teach remedial writing at the college level.

This simple method involves comparative textual analysis of exemplar introduction paragraphs, thesis construction, analysis of evidence, contextualization of evidence, and more.

Crucially, students are not told which text is “better” or “worse.” They analyze each text against one another (and against the beginnings of a rubric) and find the differences first. After locating the differences, they vote on which is better — using a partially-constructed rubric to guide them.

At this point, teacher (T) leads a short discussion (emphasis on short) to land the key point: A is better than B (or vice versa) because _______. In the case of an introduction paragraph at the middle school level - for example - the differentiating factor might be the inclusion of a clear thesis statement at the end of the paragraph, the inclusion of clear background information on the topic or text, or an effective “hook” at the beginning of the paragraph. The level of nuance in delivering one of those points is determined by the student’s level and what the teacher thinks they can grasp.

Finally, the “key point” is collaboratively embedded into the partially-constructed rubric mentioned earlier. Educator opinions regarding how much agency to provide to the student at this moment in the method vary, but the important point is that the student feels some ownership around the construction of the rubric/evaluation mechanism. Providing this agency in determining how they are going to be evaluated is extremely important. Without it, students consider writing rubrics to be arbitrary, subjective, ambiguous, and sometimes annoying. But via this procedure, they were a part of the process, had a chance to grapple with the concept, and (ideally) understand why the rubric was constructed the way it was.

Applying It To AI

When I first started teaching my students how to engage with AI, I found prompt engineering tactics to be woefully inadequate. As mentioned before, it was like giving sentence stems to students and then expecting the entire essay to be well-written. This, I believe, explains the palpable frustration across the education market with the idea of “teaching AI literacy.” The stuff we have just doesn’t work.

So, I went back to the drawing board. “Prompting is a (creative) writing skill,” I remembered Jules White saying in the first Prompt Engineering course I took on Coursera. “Good writing is just good exposition,” I’d heard Amanda Askell say on podcasts.

Okay, I thought. If “prompting,” a.k.a. “using AI well,” is a writing skill, why not teach it the same way we teach early-stage writing?

With this in mind, I developed a module utilizing the middle school writing pedagogy described above. But instead, I replaced the essay samples with AI transcript samples.

(For the purposes of this discussion, let us define ‘transcripts’ as the text of an interaction between a user and AI.)

I pretended to be a student and engaged with AI on grade-level with a task that my students would recognize. You can find these sample transcripts here.

**Author’s Note: The construction of these fictional transcripts occurred in the Spring of 2024 and leaned heavily on technical approaches associated with prompt engineering that were the predominant method for engaging with AI at the time. The approach to creating fictionalized transcripts for student analysis has evolved with the evolution of platforms, chatbots, and AI capabilities. Furthermore, this part of the process is its own topic - one with many layers that I hope to describe in future posts (if stamina permits and fatigue remains at bay).**

Below is the mirrored procedure:

What I was really doing — though I didn't have this language for it at the time — was treating the AI interaction as a piece of media unto itself. Not a tool use session. Not a productivity hack. A text, the same way an essay is a text or a speech is a text — something that can be read closely, analyzed, compared, and taught with.

There’s a bigger point here that I think people keep walking past. Everyone from Peter Thiel to Connor Grennan has been saying that humanities skills matter more than ever in the age of AI. The reasoning is obvious: AI runs on language. The people who use it well are the people who think clearly, write precisely, and communicate with intention. Those are humanities skills. Fine, great, we all agree.

But then what? If we agree that humanities skills are now critical to effective AI use — why are we teaching AI literacy with STEM pedagogy? Why are we handing students technical frameworks and engineering acronyms to develop what is fundamentally a communication skill?

The method I used above isn’t revolutionary. It’s comparative textual analysis. It’s what English teachers have been doing for decades. I just pointed it at a new kind of text.

Ultimately, the analysis of the chats (which included pen-and-paper annotations as well) led to a deep, fascinating discussion about what it meant to use AI well. My students largely agreed that Student X was better than Student Y — not only because the prompts themselves were more thoughtful, contextualized, and nuanced — but because the outputs increased in quality as well.

This is usually the part where someone shouts from the back, “But AI is getting so much better! It almost doesn’t even matter what you put in the chat, they all produce extremely similar outputs that are often high-quality!”

True, kind of.

#1: There’s always a difference. It might be imperceptible at first, but that’s why I encourage teachers considering this approach to devote at least an entire class session to analyzing both chats. We spent two full sessions (an hour each) and I assigned two homework assignments associated with it. Agentic is another story, but thanks to documentation efforts that occur within most agentic systems, the textual analysis approach works there as well.

Purposeful design choices are also crucial. Creating the fictionalized transcript is by no means an easy task, but if you limit your over-arching conceptual goals to 2-3 key “moves,” you’ll find yourself in better territory.

#2: You will have students disagree with you. But a) that is not a bad thing and b) you are a good teacher! You can handle it. Think of it as engagement rather than discord. And remember that the skill we are working with is inherently subjective, and that’s okay.

To wit, most of my (pre-AI) middle and high school students thought that the principles of good essay writing were arbitrary, subjective, ambiguous, and often annoying. They sometimes thought I was making them up on the fly. And those principles have been developed and defined over the last 156 years. So, if you are thinking about trying this method, start by tossing out the idea that everyone is going to agree about “good” AI use. The goal is to show “better” and “worse” in comparison to one another; and to subsequently help your students see some of the differences. That is very much doable.

Where This Has Been Tested

I’ve run versions of this at the middle school, high school, and college level. Aimée Skidmore and I tested it with her seniors at the Collège du Léman in Geneva, and a full account of that experiment will be published in the Elsevier volume GenAI in Higher Education later this year. I’ll share snapshots of those results here next week.

The results are promising. The method works mechanically — Humanities teachers, in particular, pick it up quickly. Students engage deeply, which is the most fun part. The discussions are genuinely rich. But promising isn’t proven, and I want to be honest about the gaps.

What I Don’t Know Yet

Does this work outside of writing and humanities? Comparative textual analysis feels natural in an English class. I don’t know what transcript design looks like for a biology course, a nursing program, or an engineering class. The core method should be portable. The specifics won’t be.

How do you build the transcripts well? This is the hardest part of the whole thing, and I’ve barely scratched the surface of documenting it. You’re constructing two fictional AI interactions that differ in specific, deliberate ways — but the differences can’t be so obvious that analysis feels pointless, and they can’t be so subtle that students miss them. You have to calibrate to student level. You have to decide which 2-3 “moves” you want them to notice. And AI platforms change constantly, so a transcript that illustrates a real principle today might feel dated in six months. Right now this design skill lives mostly in my head. That’s not good enough.

What does good facilitation look like versus bad? I’ve seen the rubric-building conversation land beautifully — students genuinely grappling with what “good” means. I’ve also seen it flatten into students agreeing with me because they can tell what I want to hear. I can feel the difference in the room. I can’t fully articulate what I’m doing differently on the good days versus the bad ones.

What happens when the transcript isn’t text anymore? Voice interfaces, agentic AI, multimodal interactions — the “transcript” is evolving. The pedagogy probably needs to evolve with it.

What I’m Working On Next

I can’t close these gaps alone, and a few more semesters of my own classroom testing won’t do it either. This needs practitioners across disciplines trying to build transcripts for their own contexts, running the analysis with their own students, and reporting what works and what doesn’t.

The model I keep thinking about is the National Writing Project — practitioners in a room together, arguing about what “good” looks like, building tools they take back to their classrooms. The NWP worked because it treated teachers as researchers. That’s what this needs: 20-30 educators spending a summer stress-testing transcript-based pedagogy across grade levels and disciplines, producing a shared framework and published findings.

I’m actively working on making that happen. If you’re interested in being part of it — as a participant, a collaborator, or someone who knows where to point me — I’d like to hear from you.

Here are some of the threads I’m pulling on, in case any of them pique your interest:

1. The development of a transcript-based pedagogy as a formal framework

2. Parallels between the beginnings of AI Literacy as a discipline and the beginnings of Composition Studies as a discipline (1869)

3. A collaborative, community-based research plan

4. Agentic AI and the transcript

5. How to create the fictional transcript for your students

6. Using this in the workplace — for hiring, talent acquisition, employee evaluation, and more

Leave a comment below if any of this strikes you — or shoot me a direct message here or on LinkedIn. Thanks for reading.

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A peer-reviewed version of this methodology appears in the foreword to "A Field Guide to Effective GenAI Use" at the WAC Repository. You can read it here. The Elsevier volume GenAI in Higher Education, which includes a full account of a classroom experiment using this approach, is forthcoming in 2026.

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