Douglas Kiang teaches his students how to evade AI detectors.
For many educators, that fact alone is enough to end the conversation.
We all know that schools have spent the past several years attempting to stop students from palming off AI-generated writing as their own. AI has prompted teachers to rewrite assignments, scrutinize revision histories, and otherwise search for signs that a submitted essay is not actually the student’s work.
And then there is Douglas. An experienced English teacher, computer science teacher, and AI literacy leader, Douglas willingly helps students defeat the very systems designed to preserve academic integrity.
So, when I interviewed him recently, I started with the obvious question:
“What were you thinking?!”
His answer was a lot more thoughtful than my grab-your-attention headline implies.
This is my favorite interview to date. I hope you’ll watch it.
More Than a Provocation
To be clear, Douglas did not ask students to generate an essay, disguise it, and submit it dishonestly. Indeed, he asked them to study their own writing first by asking the following questions:
What does your voice sound like?
What words, sentence patterns, rhythms, and habits make your writing recognizably yours?
What does AI-generated prose tend to do that you would never naturally do?
Students compared their own work with AI-generated text, identified the differences, and developed instructions that could help AI produce language closer to their actual voice.
Okay, you still might want to end the conversation.
Many teachers would understandably argue that the process of writing sentences is part of the thinking process itself. We do not always form ideas first and then simply convert them into words. Often, we uncover what we think through the struggle to write. From that perspective, revising AI-generated prose is not the same as writing.
But Douglas’s experiment forces us to address a difficult question:
Are schools protecting writing, or are they protecting a version of the writing process that has already changed?
A Classroom Laboratory for the Rest of Us
Douglas teaches at Menlo School, an independent school in Silicon Valley. As you might imagine, he works in an environment with strong technology access. He also benefits from a supportive administration, parents working in technology, and enough instructional freedom to experiment. His school also offers a freshman course in AI literacy and fluency. Douglas can redesign assessments, invite candid discussions about AI use, ask parents to contribute, and implement instructional practices that would trigger immediate resistance in most other school systems.
But that does not make Douglas’s teaching irrelevant.
If anything, it makes his classroom a laboratory for the rest of us.
Douglas’s laboratory does not aim to create a model everyone must follow. Rather, it is to test assumptions, uncover problems, probe solutions, and provide information to evaluate. Douglas can certainly move farther than most educators, but the questions that come from his classroom will eventually reach every school.
And the most provocative of these concerns writing itself.
When Writing Becomes Directing
Douglas argues that writing has acquired another layer. Traditionally, writing instruction has strictly emphasized close writing. The writer chooses words carefully, building sentences, shaping paragraphs, organizing arguments, and refining style. Douglas is not saying these skills are disappearing. But he believes they are the foundation for something else: writing as directing.
He uses the analogy of a film director. A director does not personally operate every camera, design every set, compose every scene, or deliver every line. The director establishes the vision, evaluates options, makes decisions, and is responsible for the final product. Douglas believes writers are increasingly doing something similar with AI. They develop the main idea, organize a structure, define a tone, direct different systems to produce drafts, compare the results, and assemble a final piece that reflects their intent.
Of course, for writing teachers, this comes across as supervision rather than authorship.
Douglas’s retort is that good directing requires deep knowledge of the craft. A person who cannot recognize a weak argument, a generic sentence, a misleading claim, or a stylistic mismatch will not direct AI well. He argues that close reading and close writing do not become less important. They become the foundation for making sound judgments about AI-generated language.
And many in our society are already attempting to direct AI.
The problem is that students haven’t developed the knowledge and judgment required to do so effectively. A student who has never struggled to construct an argument has no foundation for evaluating one. A student who has never developed their voice when writing will not know if AI has captured it. A student who lacks subject knowledge may accept polished nonsense because it sounds convincing.
Douglas’s classroom points toward a future in which schools may need to teach both independent composition and AI-supported direction. I’m sure that’s bound to unsettle most educators, but similar changes are already occurring in professional work. Software engineers supervise AI-generated code. Marketers compare machine-produced publicity. Lawyers review AI-generated contract drafts. In this environment, the human element revolves around judgment, context, and responsibility.
The Skills AI Cannot Replace
Douglas’s other ideas reinforce these human elements.
He warns that “accuracy breeds complacency.” Educators often focus on what LLMs get wrong, but Douglas argues that AI’s greater danger may be that it is most often right. As AI performs more reliably, people stop checking its output and gradually let go of the measures required to identify the occasional serious error. The challenge becomes deciding which abilities students must continue to practice even when AI can perform them accurately.
Douglas also talks about the peril of “unseen unseen,” the ideas or perspectives that AI fails to provide us. When a chatbot produces an answer, it limits the field of possibilities because it’s always presenting the next most-probable step. Students instinctively react to what appears on the screen and often don’t consider what is missing. As a result, Douglas asks students to brainstorm before using AI. He doesn’t do so because he’s sure the student’s first idea will be better, but because AI should not limit the possibilities before the student has entered its space.
Douglas also draws an important distinction between AI literacy and AI fluency. He points out that teachers are often thoughtful about when AI should or should not be used, but many lack practical “in the weeds” experience with the tools. Students, in contrast, use AI constantly, but fail to reflect on why they are using it or how it might impact their learning. In other words, teachers and students possess opposite halves of the competence schools need.
It may surprise you that Douglas does not saturate his classroom with AI. In fact, he has brought back more pencils, paper, quizzes, and in-class checks. Students may use AI on take-home assignments, but they must also demonstrate understanding without it. In this way, he separates supported practice from independent proof. This may be one of the most practical lessons teachers can take from his work.
Students Need Teachers More, Not Less
Douglas’s progressive activities may not travel to many other classrooms. But the underlying questions certainly do.
What should students be able to do without AI?
When should human thinking come first?
How should students explain their use of AI?
What evidence shows that learning actually occurred?
Which skills must be protected even when technology can perform them faster?
Which brings us to Douglas’s most important argument:
Students need teachers more, not less, in the age of AI.
Teachers have legitimate concerns about privacy, bias, environmental cost, misinformation, dishonesty, and cognitive offloading. Douglas does not dismiss them. He argues that these concerns make teacher involvement more necessary. Students are already using AI. If teachers refuse to engage, it does not create an AI-free world. It may leave young people to navigate powerful systems without adults who understand learning, ethics, and responsibility.
AI can produce answers, explanations, examples, drafts, and feedback in seconds. It cannot decide what a student should value, what struggle is worth preserving, or whether efficient completion has replaced learning.
A Path Forward, Not a Blueprint
Douglas’s teaching may seem radical. His ideas are much harder to dismiss. He is not offering a universal blueprint, and not every experiment should be copied. But his classroom gives us an early look at the choices schools will soon have to make.
The greatest danger is not that teachers will move too quickly.
It’s that they will refuse to move at all, and leave students to find their way alone.