Do you remember when onions tasted harsh, or when all red wine tasted the same? Taste develops through exposure, attention, and experience, and it can also be dulled by a diet saturated with easy sweetness. Writing works the same way. When a machine can draft the essay, the distinctly human skill is not writing the first draft but appraising what the draft is worth. This means sensing what is vivid instead of generic, insightful instead of secondhand, narratively alive instead of mechanically arranged, and evident rather than merely plausible. It is paying critical attention.

Henrik Karlsson’s most beloved essay is called Looking for Alice. It is about how he found his wife. He wrote it as private advice to a friend, it leaked, and it traveled around the world. Two and a half years later, Karlsson reread it and delivered a verdict few writers would survive saying out loud: “at least half of this advice is kind of shady.”

And yet the essay still works. He now understands why, and it isn’t the advice. It works because “you can feel how much I love my wife.”

Consider what that admission means. The energy I wrote about in Part 1 (the snap, the bodily yes, the release Karlsson trusts to tell him a piece is done) was fully present in “Looking for Alice.” The feeling was accurate about the love and wrong about the theory. The body’s signal verified emotional truth. It did not verify factual truth.

So taste, real taste, needs two loops. The somatic loop: a trained body that registers when structure, stakes, and honesty align. And the verification loop: the painter’s discipline, shared in Part 2, claims tested against real cases, against counterexamples, an adversarial reader invited to provide feedback. Karlsson runs both, and runs them separately. The sofa for the first, his wife as adversarial reader and his case files for the second loop. A writer with only the first process becomes eloquently wrong. A writer with only the second becomes correct and dead on the page.

Here is what strikes the educator in me. Part 2 sorted what a writer must never outsource, the posture. Look at everything Karlsson describes across the whole interview, and a second set of four principles becomes clear.

He insists on the vivid. Wittgenstein’s “don’t think, look.” The still-life exercises. Delacroix noticing that a shadow is yellow. Concrete reality, pulled into the prose until the prose stops being about ideas and starts being about the world.

He hunts the insightful. The obvious truths nobody can hear anymore (be kind, honor your commitments) sequenced so they deliver. “I want the piece to end up smarter than I am.”

He engineers the narrative. An animating question with stakes. Open loops the reader needs closed. Images placed to prepare an emotional state before the argument arrives.

He demands the evident. Three or four real cases per essay. Biographies as data. The standing question scribbled in his own margins: is that really true?

Vivid. Insightful. Narrative. Evident. Readers of my book will recognize VINE, the framework I built in The Learn-It-All Educator for training exactly the phenomenon Part 1 named: taste, the judgment that separates average work from excellent work. I did not derive it from Karlsson, which is why watching a master essayist’s native practice decompose into the same four qualities is the most encouraging kind of evidence. Good frameworks are not inventions. They are descriptions of what excellent practitioners already do, packaged so the rest of us can practice deliberately.

Why does this matter now, and why for educators in particular? Three reasons, in ascending order of stakes.

First, the standardization pressure is real. Perell names it in the interview: LLMs are just another force for standardization. Karlsson, a power user of GenAI, agrees without hedging and wants English pushed the other way, wilder. A model’s default prose is, almost by definition, the weighted average of everything. Fluent, useful, centered. Taste is the capacity to feel the difference between that center and the live thing, and like any perceptual capacity it sharpens with contrast and atrophies without it. Students who marinate exclusively in average prose lose the contrast.

Second: thinking grows under load, the argument of the Cognitive Gym chapter in my book. Karlsson’s method runs on deliberately engineered confusion. He writes a claim down so that he “can’t fool myself,” watches it break under his own questioning, and rides the confusion until it collapses into a simpler, truer formulation. He even cites the research tradition behind accelerated expertise: beware the comfortable, half-right mental model, the “knowledge shield,” because it ends learning early. Now watch what happens when a student lets a model draft the essay. The confusion stage is precisely what gets skipped. The load disappears, and the growth disappears with it. The essay was only ever the receipt for the thinking.

This is why the assessment shift I argue for moves from generation to verification. If the machine can produce a draft, the gradable human act becomes the audit. Is this vivid, or generic? Is the insight real, or a rearranged cliché? Does the narrative earn its turn? Is it evident? Show me the case that survives a counterexample. Karlsson grades his own drafts this way every working day. We can teach students to do the same, including to AI output. Especially to AI output.

Third, the reason that moves me most. One of Karlsson’s best-known essays has a title two dozen words long, and its thesis is that a blog post is “a very long and complex search query” for finding your people. Two years before that essay, in December 2021, he had about fifty readers. He kept publishing honest, strange essays anyway, and they went out across the network and came back with collaborators, mentors, friends: a summoned culture that then changed him in return. That flywheel spins on signal. In an ocean of competent average prose, the writing that can still summon anyone is the writing with a detectable human inside it, and the readers who can still be summoned are the ones who can detect one. Taste on both ends. Voice and taste are the network protocol of intellectual life, and educators are now responsible for keeping that protocol alive in the next generation.

Karlsson, asked how he would teach writing, refused the premise of a skills course. Writing well, he said, “is not a skill set”; it is “becoming a certain type of person.” Many educators know that sentence in their bones, because it has always been the actual job description. The syllabus says rhetoric. The work is formation.

The machines will keep getting better at the average. Our students will swim in fluent, frictionless, centered prose for the rest of their lives. The educators who matter in that world are the ones who train both loops: a body that can feel the live thing and a mind that checks it against reality.

The energy in the page was never magic. It is a trained body and a tested truth arriving together, past the reader’s defenses.

Teach them to taste.

The frameworks in this series (VINE, the Cognitive Gym, the shift from generation to verification) are developed fully in The Learn-It-All Educator: A Guidebook for Training Brains, Not Replacing Them, also available through the Open Textbook Library. If this series found you, the search query worked. You are probably my people. Subscribe and say hello.

“The essay was only ever the receipt for the thinking”

“Voice and taste are the network protocol of intellectual life”

“the distinctly human skill is not writing the first draft but appraising what the draft is worth”

The trap is in the language. “AI in education” gets treated as a single thing. It is not a single thing. It is four distinct strata, with different stakeholders, different feedback loops, different success criteria, and different failure modes. When institutions collapse them into one conversation, they end up with policies that govern everything poorly and graduates who are fundamentally unprepared for the workplaces they are entering.

This essay names the four layers, identifies which one your institution is almost certainly under-investing in, and explains why the structure of academic work makes that under-investment nearly inevitable.

The four layers

AI for Education (Infrastructure). Operational machinery. LMS automation, enrollment predictions, advising chatbots, admissions workflows, financial aid processing. The value of this layer is real but capped. It can make existing institutional functions faster and cheaper. It cannot, by itself, improve what students learn.

AI in Education (Pedagogy). AI as a coach inside the learning process. Assignment redesign that uses AI to make students think harder rather than less. Verification protocols. Productive struggle scaffolded by AI. This is the Cognitive Gym layer, and it is the one most faculty discussions focus on.

AI of the Profession (Career Readiness). The specific AI tools students will encounter in their actual jobs. Ambient scribes in nursing. Contract review software in law. Predictive maintenance dashboards in manufacturing. Diagnostic support in radiology. Code generation in software engineering. This is the layer that determines whether graduates can function in the workplaces they are about to enter.

AI Literacy (Foundational). The universal base that sits under the other three. Understanding what probability engines actually do. Recognizing bias and hallucination patterns. Knowing how to evaluate output critically. This is the layer that makes all the others possible.

Each layer has its own stakeholders, its own appropriate metrics, and its own appropriate decision-makers. The IT department owns most of the first. Faculty own most of the second. Industry advisory boards and program directors should own most of the third. The general education faculty own most of the fourth.

The under-investment that is hiding in plain sight

Most institutions over-invest in the first layer and under-invest in the third. The pattern is consistent enough across institution types to suggest something structural rather than accidental.

The reason is feedback timing. AI for Education produces immediate dashboard metrics. An advising chatbot can be measured in queries answered per week. An enrollment prediction model can be validated against the next admissions cycle. Administrators get fast, legible signals that something is working.

AI of the Profession provides no such signal. The feedback loop runs through alumni outcomes, employer satisfaction, and labor market data, which arrive eighteen months to five years after the curriculum decisions that produced them. By the time an institution knows its graduates are entering workplaces unprepared for the tools they will encounter, the cohort has already graduated. There is no Tuesday morning meeting where this problem produces a visible metric.

The result is predictable. Institutions invest where the feedback is fast. The layer that matters most for the student’s career is the layer that gets the least attention.

“AI for medicine is a turbocharger. AI in medicine is a new literacy. One is about efficiency. The other is about competence.”

AAMC Principles for Responsible AI

The AAMC framing applies across every field. Turbocharger work happens at the institution. Competence work happens in the classroom. Confusing the two has consequences.

The structural trap for excellent instructors

Consider an award-winning faculty member with four decades of experience who has embraced AI Literacy and AI in Education. They use AI to draft lesson plans. They have redesigned their assignments for collaboration. They are, by every visible metric, doing the AI integration work correctly.

But they have not practiced in industry for decades. They do not know how AI is currently automating supply chain decisions, how it is transforming legal research, how it is changing the clinical documentation workflow at the hospital their nursing students will work in next year. They are teaching students how to think with AI in the classroom while missing the AI of the Profession entirely.

This is a structural information deficit, not a personal failing. The curriculum process is slow by design, and the adjunct faculty who carry the freshest field knowledge often have a limited voice on curriculum committees. Industry advisory boards meet quarterly, if that. The information pipeline from workplace to classroom was already constrained before AI accelerated the rate of workplace change.

A student needs to know how AI is changing the workplace, not just how it helps them complete a classroom assignment. Pedagogical excellence at the second layer does not substitute for absence at the third.

The Displacement Clock as a planning tool

To navigate the urgency of these changes, the Displacement Clock provides a strategic frame rather than an alarm. The clock maps professional functions onto an analog face.

1 o’clock: Safe. Functions where AI has minimal impact. High-level mentorship. Bedside manner. Trust-based human relationships. Crisis judgment.

6 o’clock: The Horizon. AI is visibly approaching. Early adopters have an advantage, but human judgment remains the core requirement. Most professional work currently sits here.

12 o’clock: Done. The professional middle has collapsed, and the function is largely handled by AI. Routine document review, first-pass medical imaging, basic financial modeling, syntactic code generation.

The clock helps identify what economists call occupational decomposition. As tasks move toward twelve, the human professional does not disappear. They move up to higher-level judgment and work that resists codification. The question shifts from “will there be jobs?” to “will graduates be ready for the higher-level work being created in place of what is being automated?”

This is the question the third layer is supposed to answer. Most institutions are not asking it.

The Companion Spectrum and the boundaries of care

A fifth phenomenon is worth naming, even though it does not fit neatly into the four-layer model.

Students are increasingly using AI for emotional support, not just academic help. The first-generation student. The parent working night shifts. The student with social anxiety who finds it easier to ask a “dumb” question at 11 p.m. without the evaluative threat of looking incompetent in front of a peer or professor. These students often prefer AI companions because the tools offer a 24/7, judgment-free zone.

The slippery slope is real. AI shifts from tutor to confidant, and then from confidant to something else.

Faculty are not therapists and should not try to be. What faculty can do is frame conversations with students about the boundaries of care, the difference between relating to persons and relating to things, and the appropriate role of AI in a healthy support network. This is a literacy issue as much as a wellness issue, which means it belongs in the AI Literacy layer where it can be addressed in general education courses rather than left to individual instructors to navigate.

The mirror property of AI Literacy

AI Literacy has a structural property the other three layers lack. It is the only layer that can be taught without AI, and yet the subject of the literacy (AI) is also the instrument of assessment.

Traditional digital literacy gets taught once in a gateway course and forgotten. AI Literacy supports a continuous assessment loop. A conversational AI probe can hold a sustained dialogue with a student about probability engines, bias, and hallucination, and the quality of the student’s responses reveals whether the literacy is real or surface-deep.

The subject becomes a mirror. The student’s actual level of critical engagement reflects back to the instructor in real time. This is a property worth designing around, because it solves an assessment problem that gateway-course digital literacy has never solved.

What a four-layer strategy looks like in practice

A coherent institutional AI strategy assigns each layer to the people who can actually move it.

The infrastructure layer goes to IT, with appropriate faculty consultation on systems that touch teaching. The pedagogy layer goes to the CTL and individual faculty, with shared assignment libraries and demonstrated case studies. The career readiness layer goes to industry advisory boards, employer partners, and program directors, with explicit annual review of how AI is changing the discipline’s labor market. The literacy layer goes to general education, with a curriculum that takes the mirror property seriously.

What does not work is treating these as a single conversation owned by a single committee. The four layers have different time horizons, different evidence standards, and different stakeholders. A unified AI policy that tries to cover all four will under-serve at least three of them.

The transition into the AI era requires a shift in institutional identity. The Know-It-All model relies on static expertise with a looming expiration date. The Learn-It-All practice is built around systematic humility, layered investment, and the recognition that the product of education is a person capable of navigating a world of synthetic content with human judgment.

Most institutions are not yet structured for that work. Naming the four layers is the first step toward becoming so.

This essay draws on Chapters 5 and 7 of The Learn-It-All Educator: A Guidebook for Training Brains, Not Replacing Them with AI, available at dataii.com/ai/guidebook with Chapters 1 through 4 free under Creative Commons.

She had been at it for two hours. The template her department chair sent her was perfectly serviceable. She had rewritten the learning outcomes in language closer to her own. She had checked that the textbook ISBN matched the bookstore listing. Now she was in Google Fonts comparing Source Sans 3 to Inter, then to Lato, then back to Source Sans 3.

The course began in nine hours. She had not yet sketched out how she would explain why we factor.

Dr. Chen’s first night of teaching preparation was a near-perfect inversion of where her time should have gone. She was spending her cognitive energy on work that could be done in five minutes by an AI assistant, and she had no time left for the one thing only she could do: bring the pattern recognition of a research mathematician to community college students who had been told, often for a decade, that they were not math people.

This inversion has a name. In The Learn-It-All Educator, it is called the failure of Cognitive Triage, and it is the most common mistake new faculty make in the AI era. The diagnostic tool is a simple pair of acronyms: FLUFF and SPARK.

FLUFF: The Work Worth Delegating

FLUFF stands for Formatting, Layouts, Under-the-hood, Filing, and Filtering. These are tasks with capped payoffs. A syllabus formatted to 80% quality serves students just as well as one formatted to 100%. The remaining 20% is pure FLUFF, work that makes a course look polished without building any cognitive muscle in either the instructor or the student.

For Dr. Chen, the FLUFF in her Math 101 prep included exactly the work she was doing at 11 p.m.

Formatting. The syllabus font selection. Citation style for the recommended reading list. Standardizing heading levels across her LMS modules. None of this teaches anyone to factor a quadratic.

Layouts. Designing a course banner. Building visually elegant slide decks for the first three weeks. Perfecting the transitions between examples. A serviceable slide communicates; a perfect one does not teach better.

Under-the-hood. Fixing broken hyperlinks in the publisher’s ancillary materials. Converting the prior instructor’s PDF problem sets into a format compatible with her LMS. Troubleshooting why the embedded Desmos calculator was not rendering on mobile.

Filing. Organizing her growing folder of practice problems by topic and difficulty. Categorizing student emails from the first week into registration, accommodations, and math anxiety. Building a gradebook structure that handled her weighted scheme correctly.

Filtering. Scanning the OpenStax textbook for the cleanest worked examples of polynomial long division. Searching for the best video explanation of completing the square to assign as supplemental.

Every item on that list is real work. None of it requires a PhD. Most of it can be drafted by an AI assistant in minutes and then quickly reviewed by Dr. Chen, with the time she saves redirected to the work that does require her.

That work is SPARK.

SPARK: Ideas Worth Thinking

SPARK stands for Specific, Persuasive, Authentic, Rigorous, and Keen-Insight. These are seeding activities, investments where the more cognitive energy Dr. Chen pours in, the more she gets back. They have uncapped payoffs. They cannot be delegated, because what AI generates when asked to do them is a generic average. And a generic average is precisely what Math 101 students at a community college have already encountered, many times, and walked away from.

Here is what SPARK looks like for Dr. Chen’s Math 101 course.

Specific. Her students are not generic Math 101 students. They are mostly working adults, many returning to school after years away from formal mathematics, several of them parents, a meaningful fraction planning to enter health programs where they will need to dose medications correctly. The generic AI summary of “best practices for teaching college algebra” knows none of this. Her job, the work AI cannot do for her, is to commit to particular claims about what these particular students need. A dosing calculation example beats a generic word problem. A worked example using a real local utility bill beats one using “let x be the cost of a widget.”

Persuasive. Math 101 sits at the boundary of a debate Dr. Chen will have to take a position on, whether she wants to or not. Should her course emphasize algebraic fluency, the kind of by-hand symbolic manipulation that has been the spine of the curriculum for fifty years, or should it emphasize quantitative reasoning, the kind of estimation, modeling, and number sense that adults actually use in their careers? AI will hand her a balanced overview of the debate. It will not tell her which side to teach from. That choice is hers, and her students will feel the difference between an instructor who has thought hard about it and one who is hedging.

Authentic. Dr. Chen is a number theorist. She thinks about integers the way a sommelier thinks about wine. She notices things about factoring that her students have never been shown, because the standard textbook treatment was written by a committee optimizing for coverage rather than insight. The authentic move is not to suppress her training and teach Math 101 the way a generic Math 101 instructor would teach it. The authentic move is to bring her particular mathematical taste into the room, in language her students can follow, and let them see what it looks like to actually find a problem interesting.

Rigorous. AI will happily generate a worked example for any topic in her syllabus. Some of those worked examples will contain subtle errors. A misplaced sign, a step that skips a case, a “therefore” that does not actually follow. Dr. Chen’s job is not to generate the example. Her job is to audit it. To catch the sign error before it reaches a student who will memorize it. To notice that the AI’s explanation of why we cannot divide by zero is technically incorrect in three places. Rigor is the SPARK work that distinguishes a competent professional from a dangerous one, and it is exactly what she should be teaching her students to do with AI output in their own future careers.

Keen-Insight. Some of her students have decided, by the second week of the semester, that they are bad at math. They are not bad at math. They have been mistaught, or under-taught, or taught when they were not ready. Dr. Chen can see this. She can see, in the way a student sets up a problem, where the mental model broke years ago, and she can name it. No AI tutor can do this with the precision and credibility that a human instructor can, because the insight comes from sitting across from a person, reading their face, and naming the specific thing they have been carrying around as a private shame. This is the uncapped payoff. A single moment of accurate naming can change the rest of a student’s relationship to mathematics.

The Reallocation

If Dr. Chen had spent Sunday night the right way, here is what it would have looked like.

The first hour would have been a conversation with an AI assistant, drafting a clean Math 101 syllabus from her department’s template, generating three versions of a learning outcomes list, producing a starter slide deck for week one, and converting the prior instructor’s PDFs into her LMS format. That hour would have closed out essentially all of her FLUFF.

The remaining hours, the ones she actually spent picking fonts, would have gone to SPARK. To writing the explanation of why we factor that she will deliver in the first ten minutes of Monday’s class. To selecting which worked examples she will use and which she will deliberately not use, because they reinforce the wrong mental model. To deciding what she actually believes about the algebra-versus-quantitative-reasoning question, so that when a student asks her on day three, “when am I ever going to use this,” she has a real answer rather than the answer every Math 101 instructor in the country has been giving for forty years.

That is the reallocation FLUFF/SPARK forces. It is not a productivity hack. It is a triage protocol. It says: your cognitive energy is finite, your students need the work only you can do, and the work only you can do is not the syllabus font.

The Trap for New Faculty

The cruel thing about Dr. Chen’s situation is that the FLUFF tasks felt productive. She could see them being done. The syllabus got finished. The slides got built. Her LMS modules looked clean. The SPARK work, by contrast, is hard to see while you are doing it. Thinking about how to explain factoring to a student who has been told she is bad at math does not produce a visible artifact. It produces, on Monday morning, an explanation that lands.

New faculty are especially vulnerable to this inversion because the visible artifacts are also the ones that get reviewed. The syllabus goes to the chair. The slides go on the screen. The explanation, the one that changes a student’s relationship to mathematics, lives only in the moment it is delivered.

The first job of the FLUFF/SPARK framework, before it is anything else, is to give new faculty permission to ignore the visible artifacts long enough to do the invisible work. Let AI handle the syllabus. Dr. Chen handles the student.

That is the trade. Get it right in the first semester, and the rest of the career may avoid some of the major miscalculations.

Dr. Szymon Machajewski is the author of The Learn-It-All Educator: A Guidebook for Training Brains, Not Replacing Them with AI. The free OER edition, Chapters 1 through 4, is available on Zenodo under a CC BY 4.0 license. The Complete Edition is on Amazon. For institutional bulk pricing and faculty common-read inquiries, contact press@dataii.com.