AI Summaries Are Not Killing Reading. They Reveal What Reading Already Is

Naomi S. Baron, a linguist who studies technology and literacy, worries that AI is making books feel obsolete. Her concern is easy to understand. When a chatbot can reduce a book to ten points, students may decide that reading hundreds of pages is an inefficient way to collect information. Baron argues that AI may weaken our motivation to read for analysis, empathy, escape, and pleasure. In her view, letting a machine read for us risks weakening the habits that reading develops.

This argument identifies a real danger, but it also treats AI summarization as more novel than it is. Education has always placed summaries between readers and difficult texts.

A scholarly introduction to a classic novel tells us what historical background matters, identifies the main themes, explains difficult references, and suggests how the work should be interpreted. A professor does much the same in a lecture. Few lecturers read an entire article aloud. They select its central claims, translate technical terms into familiar language, supply examples, and omit details that do not serve the course. Textbooks summarize fields. Literature reviews summarize studies. Review essays summarize books. Even a table of contents is a small prediction about what deserves attention.

We do not usually accuse the professor who explains Kant in ordinary language of destroying philosophy. We do not complain that an introduction to The Republic has prevented us from reading Plato. We understand these forms as intellectual support. They reduce the cost of entry.

AI summaries differ in speed, scale, and uncertain accuracy, but not in their basic educational function. They compress and translate. They can provide a map before we enter unfamiliar territory. I have argued elsewhere that educators should not hide from students that AI can be a powerful tutor. A tutor who summarizes a difficult chapter is not necessarily replacing reading. The tutor may be making reading possible.

The deeper problem with the claim that summaries replace reading is that much nonfiction reading is itself a form of summarization. We rarely read a policy report, history book, or research article in order to memorize every sentence. We read to construct a smaller mental representation that we can retain and use.

Walter Kintsch and Teun van Dijk described text comprehension as movement between local propositions and a global “macrostructure.” Their model includes mental operations that reduce the information in a text to its gist. These operations are shaped by the reader’s goals. In plain language, understanding a text requires deciding what matters, combining related points, and discarding much of the rest.

Memory research supports this view. Jacqueline Sachs found that people quickly became less able to recognize changes in the exact wording and syntax of passages, even when they retained their meaning. Fuzzy-trace theory makes a similar distinction between memory for exact form and memory for gist. Adults often rely on the simplest level of meaning that is sufficient for the task. We preserve the substance while losing many of the words that carried it.

The summary, then, does not arrive after reading. It is one of the products of reading.

Human readers are compressors with uneven settings. One reader sees a central argument where another sees an interesting detail. Prior knowledge changes what we notice. Goals change what we retain. Fatigue changes everything. Human summarization is slow and inconsistent, although it is not always less accurate than machine summarization. AI is faster and more repeatable, but it can omit qualifications, flatten disagreements, or present an error with perfect confidence.

The relevant comparison is not between rich human reading and lifeless machine compression. It is between different forms of compression, each with its own strengths and failures.

This does not mean that all reading can be replaced by summaries. Reading a novel for pleasure is not mainly an information-retrieval task. Style, rhythm, suspense, voice, and emotional sequence are part of the experience. A summary of a joke is rarely funny, which may be the shortest available proof that compression has limits.

Language learners also need contact with actual sentences. Close reading requires attention to word choice and form. Legal, medical, and scientific work may depend on qualifications that disappear in a short account. In a randomized study of 195 participants, AI reading tools produced different effects for different readers. Summaries could help some lower-performing readers, but they reduced comprehension among stronger readers, apparently because details and nuance had been removed.

The proper question is therefore not whether a summary counts as reading. The question is what kind of reading a task requires.

Sometimes we need orientation. Sometimes we need a central claim. Sometimes we need to check whether a document deserves closer attention. An eight-week study of college students found that some students used AI summaries to decide which parts of assigned texts merited deeper reading. This practice can become passive, but it can also support triage in a world where no scholar can read every relevant source from beginning to end.

We should teach students to move between levels of compression. They can begin with an AI overview, inspect the original text, compare the two, identify what was lost, and revise the summary. That process makes summarization visible. It turns a hidden mental act into an object of judgment.

The educational task is not to protect every page from compression. It is to teach students when compression is the work, and when the missing page is the point.

Stop Comparing AI to Humans Who Do Not Exist, or Nirvana Fallacy

AI critique often begins with a sound complaint and then slips into a false comparison. We notice that AI systems hallucinate, distort, flatten context, and produce confident nonsense. All true. The trouble starts when we compare those systems to a human knower who is clear, fair, grounded, self-aware, open to correction, and able to explain the grounds of belief. That person does not exist.

The real human baseline is less tidy. We reason toward conclusions we already prefer. We edit our memories without noticing. We mistake fluency for truth. We give more weight to testimony from people who sound like us. We treat expertise as portable across domains when it often is not. Our errors also do not remain private. They travel through departments, professions, institutions, and cultures.

This does not mean human intelligence is worthless. Human beings have forms of intelligence that current AI does not have: bodies, care, social presence, lived risk, moral weight, and the capacity to mean what we say. The point is not to lower the status of human thought. It is to stop using an idealized version of it as the standard by which every other form of cognition must be judged.

Economists call this kind of error the nirvana fallacy. It appears when we compare a real, flawed option to an ideal one that is not available. In AI debates, the pattern is common. AI is judged against the fantasy of a transparent and accountable human mind. Since AI fails by that measure, we declare it unsafe, shallow, or unfit for education. But the right comparison is not between AI and perfection. It is between AI and the actual alternatives available for the task at hand.

That task matters. In tutoring, the relevant question is not whether AI has full human understanding. It is whether a student with no human tutor learns more with a chatbot than without one. In feedback on writing, the question is not whether AI reads prose as an experienced teacher does. It is whether its comments are more useful than the silence many students receive. In factual work, the question is not whether AI ever makes things up. It is whether its error rate, under defined conditions, is better or worse than the error rate of students, instructors, search engines, peer advice, or institutional folklore.

This is not a defense of AI hype. It is a defense of fair measurement. AI should be tested, limited, and corrected. It should not be trusted by default. But critique loses force when it rests on a hidden ideal of human judgment. We do not ask whether AI is flawless. We ask what it can do, where it fails, what its failures cost, and how those failures compare with realistic alternatives.

The deeper problem is anthropocentric. Modern thought inherited a flattering picture of the human mind. Descartes gave us reason as a secure point of certainty. Kant gave us rational autonomy as the mark of moral agency. Later traditions made reason not just a tool but part of our identity. To be fully human, on this view, is to be a rational agent who can in principle know, judge, and correct the self from within.

That picture did important work. It helped ground dignity, freedom, and rights. But it also made human reason the unmarked standard. Every other kind of knowing became a weaker copy, a failed imitation, or a strange case in need of repair. This habit now shapes how we talk about AI. We ask whether it thinks like us, understands like us, explains like us, or errs like us. When it does not, we treat the difference as failure.

There is an older and less flattering counter-tradition. Augustine and Gregory of Nyssa did not treat human reason as merely underused or poorly trained. They saw it as damaged in a deeper way. We need not accept their theology to learn from their realism. Their useful insight is that human fallibility is not a minor flaw in an otherwise pure system. It is part of the operating condition.

If limitation is our starting point, then humility is not a mood we adopt after error. It is the normal posture of inquiry. Cognitive humility is not skepticism. It does not say we cannot know anything. It says our knowing is partial, bent, social, and always in need of correction from outside itself.

AI becomes more useful in that light. Its failures are not the same as ours. AI may generate a false citation because it predicts language without checking a world. A human may accept a false claim because it protects a theory, a career, a tribe, or a mood. These are different failure profiles. Ranking them as simply better or worse misses the more useful question: how can they correct each other?

This shift also matters for education. We should not design human-AI systems around the dream of one perfect rational agent. Neither the teacher nor the student nor the machine fits that dream. Better design begins with paired weakness. AI can help where human attention is scarce, memory is thin, patience is gone, or feedback is delayed. Humans can help where meaning, judgment, care, and responsibility are needed. The goal is not replacement. It is a more honest division of labor.

The same humility should shape how we think about neurodiverse cognition, animal cognition, collective intelligence, and other forms of mind. Once we stop treating one idealized human pattern as the measure of all thought, difference no longer looks like defect by default. It becomes a source of comparison, correction, and learning.

AI did not create the crisis in our idea of intelligence. It exposed it. The surprise is not that the machine is flawed. The surprise is how long we have mistaken our own ideal self-image for a standard.

A more formal version of this argument is available in the preprint, where I develop the nirvana fallacy, the anthropocentric reflex, and cognitive humility in greater detail.

The AI-Native Course Framework: Outcomes, Assessment, Pedagogy

A course is AI-native when artificial intelligence is assumed to be present throughout, the way a calculator is assumed in a calculus course. The cheating frame falls away on its own. There is nothing to cheat at when the assignment is designed around the use of AI rather than against it. Students learn what matters now, instructors do work they trained to do, and the course aligns with the world graduates will enter.

An AI-native course rests on three foundations. The first redefines what students should learn. The second redefines how learning is shown. The third redefines how teaching happens. Each foundation is independent in design but interdependent in effect. Pull one out and the other two lose their grip.

Outcomes

The outcome map is the first thing to revise. Procedural skills that AI now performs at professional speed no longer deserve the weight they once carried. Citation formatting, grammar correction, routine summarization, and basic calculation drop down the priority list, because none of them measure what the student understands. Their place is taken by conceptual outcomes: interpretation under uncertainty, evaluation of contradictory sources, ethical judgment, strategic decision-making across a human-machine team.

Alongside the conceptual shift, four new AI-specific outcomes enter the map. Extended Executive Cognition: breaking a complex task into parts, deciding which go to AI and which stay with the human, and integrating the result. Theory of Mind for AI: a firm grasp of how these systems work, what they do well, and where they fail. Eloquent Emptiness Detection: recognizing fluent prose that contains nothing of substance. Ethics of Answerability: taking full responsibility for the final product, even when no single actor can claim sole authorship. These are not bolted on. They are load-bearing components, listed alongside the discipline's traditional aims. A research methods course no longer evaluates APA formatting. It evaluates whether the student can prompt an AI for a literature search, verify the sources, and defend the methodological choices that followed. 

Assessment

Once outcomes change, the evidence has to change with them. Authentic assessment is the form that survives the AI shift, because authentic tasks resemble the real work of a discipline rather than the artificial work of a classroom. A community health student designs an intervention. A marine biology student conducts a field assessment. A history student curates a museum-style exhibition with annotated sources. The work cannot be ghost-written, because the work is the thing itself, situated in a context that AI cannot generate on its own.

Declarative knowledge does not vanish from such a course. It moves to a different layer. A class assistant bot, equipped with an AI-generated textbook tailored to the course, delivers content on demand, answers basic questions, and quizzes students on definitions. The assistant absorbs the role that lectures and chapter readings used to play, and frees class time for the kind of work that benefits from a human present in the room.

Pedagogy

The pedagogy of an AI-native course follows from the first two foundations. With AI partially taking on course design, grading and the content delivery, the instructor recovers hours. That recovered time goes into two channels. More frequent formative feedback to students. More personal connection with each student. The relational move is no more complicated than that.

A pre-grading bot performs the first pass on a stack of papers in minutes, with rubric alignment that is often steadier than a tired instructor at the end of a long week. Human input is still essential, but takes less time. Weekly feedback, which formative assessment theory always called for, becomes practical for the first time. One assignment can genuinely inform the design of the next.

The remaining hours go into the room with the student. Office hours become substantive. Class time shifts from broadcast to dialogue. The work that drew most of us into teaching, the back and forth with a learner about an idea, finally gets the share of the workweek it deserves.

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A course built on these three foundations does not police AI use, because AI use is already inside the design. The student who hands in AI prose for a literature review still has to defend the methodological choices, verify the sources, and produce the field interview AI cannot conduct. The student who relies on the class assistant for declarative content still has to apply that content to a real-world task. The work that remains is the work that matters. There is no longer a hidden seam where cheating can enter.

An AI-native course is not a defensive posture against a new technology. It is a positive design for a different baseline. Outcomes that match the world students will inhabit. Assessment that resembles the work they will do. Pedagogy that uses the time AI gives back. Three foundations, one structure, available to any discipline willing to redesign from the ground rather than patch from the top. I tested all of these moves; they work if taken all together. 

The Road Monster Arrives: The Forgotten Backlash Against a Machine

When the automobile first appeared on public roads, many people hated it. Not mildly disliked it. Hated it. Newspapers called cars “road monsters” and “public nuisances.” Villages tried to ban them. Farmers threw stones at drivers. Entire regions voted to keep automobiles out. The resistance was not irrational panic about the future. In many cases, the critics were describing real harms with remarkable accuracy.

It is difficult for us to imagine streets before cars. We think of roads as places designed for vehicles. But nineteenth century streets were social spaces. Children played there. Vendors stood there. Horses, carts, bicycles, and pedestrians mixed together in a slow choreography that had evolved over centuries. The automobile entered this environment like an invasive species. It was loud, fast, dirty, and dangerous. Horses panicked at the sound of engines. Dust clouds covered villages. Accidents multiplied. In Britain, the government responded with the famous “Red Flag Act,” requiring someone to walk ahead of motor vehicles carrying a warning flag. Today this law is remembered as comic overreaction. At the time, it seemed prudent.

The first fatalities intensified public anger. In 1896, Bridget Driscoll became the first pedestrian killed by an automobile in Britain. Witnesses described confusion and terror. A few years later, newspapers in the United States were already using phrases like “the automobile nuisance.” In France, the catastrophic Paris to Madrid race of 1903 ended with multiple deaths and was halted by authorities before reaching Madrid. Early automobiles did not arrive wrapped in the language of safety or environmental progress. They arrived trailing smoke, blood, noise, and class resentment.

The class issue mattered enormously. Early motorists were mostly wealthy people. To many ordinary citizens, the automobile looked less like transportation and more like aristocratic arrogance mechanized. Woodrow Wilson once remarked that cars generated “socialistic feeling” among rural populations because they symbolized the “arrogance of wealth.” Popular literature absorbed this resentment. In The Wind in the Willows, Mr. Toad becomes intoxicated by motorcars, turning into a ridiculous and dangerous fanatic. In The Magnificent Ambersons, one character dismisses automobiles as “a useless nuisance,” a line that sounded perfectly reasonable to many readers in 1918.

Some places resisted for decades. Mackinac Island banned automobiles in 1898 after carriage operators complained that cars endangered horses and pedestrians. The island remains largely car free today. In the Swiss canton of Graubünden, automobiles were prohibited after residents protested the “speed, noise, and smell” of motor traffic. The ban survived repeated referenda before finally collapsing in 1925. What is striking in retrospect is not that these societies resisted technology. It is that they negotiated with it. They imposed limits, argued about costs, defended existing ways of life, and demanded compensation for disruption.

That is the real lesson of the automobile. The story is not that society always loses when it resists technology. Nor is it a simple morality tale about fearful humans standing against inevitable machines. Many early objections to automobiles were correct. Cars did kill people. They did destroy older industries and reshape cities around themselves. They did transfer public space toward private mobility. What changed over time was the balance of interests. The benefits of automobiles gradually became large enough that more people were willing to tolerate the costs. New industries emerged. Rural isolation diminished. Trade accelerated. Personal mobility expanded. The conflict was never humans versus machines. It was always humans versus other humans, each group attaching different values to speed, safety, convenience, labor, status, and ways of living. Or, in many cases, the same humans against themselves. 

AI Will Write the Lesson Plan, and Teacher Preparation Should Be Glad

Lesson planning is problem solving, and teacher preparation has been treating it as something else. The artifact has been mistaken for the skill. A novice produces a plan that meets the rubric, the supervisor checks the boxes, and everyone agrees the candidate is ready. The plan is the visible output. The reasoning that produced it, which is the actual job, has been left largely to chance and to the quality of mentorship in the placement school. AI is about to make this confusion impossible to sustain, and the news is good rather than bad.

Consider what a teacher actually does when planning a lesson. She holds in mind the standards she must address, the students she has (who they are, what they did yesterday, what they fear, what they already know), the time available, the materials at hand, the colleague next door whose schedule overlaps, the assessment due Friday, the child who was crying in the hall this morning. She makes a sequence of decisions under all these constraints. The plan is the residue of that work. A second teacher with the same standards and a different group of children produces a different plan, and the difference is not stylistic. It reflects different problems being solved.

Programs do not generally teach lesson planning this way. They teach the form. The plan template, the objective-and-anticipatory-set sequence, the alignment to a standard. These are the easy parts, and they are also the parts AI now performs at a competent first-year level. A model handed a standard, a grade, and a topic will produce a workable plan in seconds. If the form was the skill, the skill is gone. If the form was the residue, what was always missing from preparation is what remains, and what remains is the actual teaching job.

I have spent enough years near teacher preparation to know that the field has long understood this in principle. The actual teaching job is solving complex, contextual problems, both pedagogical and relational, repeatedly. The repetition is the part that matters. Judgment is not transmitted by lecture. It accretes through volume of supervised decisions under varied conditions. A candidate should encounter hundreds of small cases before graduating. A reading group is stalling on a text from which two students have visibly disengaged. A child has stopped speaking in class after a family event the teacher heard about indirectly. A planned activity is collapsing in second period and four periods remain. Each case demands a decision. The decision can be defended or critiqued. The next case is different enough that the previous lesson does not simply transfer. This is how master teachers were built when the on-ramp existed in schools. The on-ramp has been narrowing for years. AI does not narrow it further. AI provides the platform on which preparation programs can finally do this work at scale.

Here is the part that should reorganize the curriculum. Students should learn to feed the complexity of a real classroom into the AI, and to evaluate what comes back. Not "write me a lesson plan on the water cycle for fifth grade." That prompt deserves the generic plan it gets. Rather, "I have twenty-eight fifth graders, six of whom read two years below grade, four English learners at different proficiency levels, a classroom with no working sink, forty-five minutes, and a state standard that asks for an investigation. Yesterday's lesson left them confused about evaporation. Propose three different approaches and explain the trade-offs." The skill lives in the input and the evaluation, both of which require knowing what could go wrong with each option. That is judgment in the precise sense, applied through the medium of a competent partner. The AI plans. The teacher decides.

Other professions discovered this structure long ago. Business case method has been the spine of MBA programs for a century, because case after case forces the student to decide under conditions the textbook did not anticipate. Law clinics serve the same function for the willing. Medical residency, at its best, does too. Teacher preparation has the analogous structure in student teaching, but student teaching arrives at the end, depends on the placement, and rarely produces deliberate variation across cases. The model is right. The proportion is wrong.

Putting this into practice means a smaller share of credits on form, methods, and standards memorization, and a much larger share on repeated case decisions with AI as the planning partner. It means faculty time for serious case construction and supervision, which is the most expensive thing a program can spend. It means accepting that some of what currently fills the curriculum is now cheap and must be reallocated rather than retained out of habit. None of this is free, and pretending otherwise will produce a watered-down version that disappoints everyone. The genuine version, however, is something teacher preparation has wanted to be for fifty years and never had the conditions to become. The conditions have arrived.

Slop In, Slop Out

The common way of talking about AI-generated text begins with a category mistake. People want to know what percentage of a piece was written by AI, as though authorship were a divisible substance. Even the more sophisticated framing, that good content is a blend of human and machine work, retains the assumption that the two contributions can be measured against each other on the same scale. They cannot. A prompt of six unusual words can shape an output more decisively than a thousand words of generic instruction. Size is not the right unit, and there is no right unit, because the contributions are not comparable in kind.

The economist David Ricardo described, two centuries ago, a structure that fits this situation better than any ratio. Two parties with different productivity profiles still gain from specialization, even when one is absolutely more capable at every task. The relevant variable is not absolute capability but opportunity cost. England and Portugal both produced wine and cloth in his example, and Portugal was better at both. They still gained by specializing, because the opportunity cost of each good differed between the two countries. Translated into our problem: it does not matter whether AI can technically generate a thesis statement or a first draft. What matters is which side has the lower opportunity cost for which contribution.

The taxonomy is becoming legible. AI systems offer their distinctive value in coverage and convention. They have read more than any individual human can read. They know what a competent paragraph in a given genre looks like, what citations a given claim usually carries, what the statistical regularities of expert prose tend to be. Humans offer their distinctive value elsewhere. The novel connection between two rarely connected fields. The judgment that a particular argument matters and another does not. The intent that determines what is worth writing in the first place. The taste that recognizes when a sentence lands and when it slides past. These are not contributions a model derives from corpus statistics, because corpus statistics describe what has been written, not what should be.

Readers who follow these debates know the term "AI slop," used to describe the bland, formulaic, superficially competent output that floods comment sections and content farms. The term names a result. It does not name the process that produces it. The process deserves its own name. I will call it slop in. The phrase borrows from the old computing maxim that garbage in produces garbage out, and applies it specifically to the moment when a human, sitting in front of a model capable of doing a great deal, supplies an input that wastes the capacity. Slop in is what produces slop out.

The obvious form of slop in is the underspecified request. "Write me an essay about leadership." The human has supplied no intent, no angle, no judgment, and the model fills the vacuum with the average of everything it has seen on leadership, which is the definition of unreadable. But there is a less obvious form, and it is the one I find more interesting. It is the redundant prompt, where the human repeats back to the model what the model would have produced anyway. The user who asks for a five-paragraph essay with a clear thesis, three supporting points, and a conclusion has not contributed what only a human can contribute. They have only specified what the model already defaults to. The output will be worse than if they had said something distinctive, however brief. Effort is not the issue. A long, careful, redundant prompt is still slop in.

J. C. R. Licklider sketched the division of labor we are still working out, in a 1960 essay called "Man-Computer Symbiosis." He drew up two columns. The machine would handle rapid retrieval, pattern matching across volume, and calculation. The human would formulate the questions, choose the criteria of evaluation, and recognize when something was significant. The specifics need updating after sixty-six years, but the structure is exactly right. Licklider understood that the partnership only worked if each side did what the other could not. He did not call it comparative advantage, but the logic is the same.

The pedagogical implication follows. The skill we now need to teach is not how to operate AI tools. The interfaces are not difficult, and the difficult ones will become easy soon enough. The skill is the generation of human input that enhances rather than degrades the joint output. This means learning to recognize what the model will produce by default, so that the contribution can fall elsewhere. It means cultivating the judgment of what is worth writing about, the taste that distinguishes a serviceable sentence from a memorable one, the willingness to assert an interpretation the model would not reach on its own. These have always been the harder parts of writing instruction. They are now also the more economically valuable ones, because they are precisely the inputs the machine cannot supply for itself.

There is a deeper reason this matters, and it has to do with what we are now able to see about ourselves. Every culture defines the human against something it is not. For most of history those somethings were animals and gods, and the contrast did most of the conceptual work. Aristotle's rational animal needed beasts to be irrational. The imago dei needed a creator on the other side of the comparison. The mirrors we used were dim, and the reflection was generous. AI is a new mirror, and a sharper one. It produces fluent argument, competent prose, plausible analysis, and it does so without intent or taste or any judgment of what is worth saying. What remains uniquely human, when the mirror returns this image, is narrower and harder to name than before. Teaching people to contribute to a joint output is therefore not only an economic skill. It is the practical form of the older question about what we are, asked under conditions that finally make the question answerable in detail.

You Will Not Like It, But There Is a Solution for AI in the Classroom

The mood in faculty meetings has tipped into a kind of resigned helplessness. Detection tools fail. Honor codes wobble. Bans get circumvented by a sophomore on a phone. So we shrug and say nothing works. But it is not true. Something works. The reason you have not heard much about it is that it is expensive, slow, and demands the resource universities ration most carefully: collective faculty attention and sustained admin support. 

Here is the solution. Take every program, in every discipline, and pull it apart down to the learning outcomes. Audit each outcome against what AI now does competently. Strip out the procedural skills that machines have automated. Strengthen the advanced, conceptual outcomes that survive the audit. Add new outcomes that did not exist five years ago, the ones that deal with directing, verifying, and integrating AI work. Then rebuild the curriculum upward from the revised outcomes. New sequences. New assignments. New assessments. Not the old course with AI bolted on the side. A different course.

Most courses are not designed; they are inherited. A syllabus arrives in a shared drive, gets a date update, and circulates again. The learning outcomes, if anyone notices them, were written by a committee a decade ago and rounded off to fit the textbook. In many professional fields, those outcomes are not even local. They come from accreditation bodies and professional standards, which are themselves the product of expert consensus rather than evidence about how learning actually progresses. Calculus before statistics. Anatomy before physiology. Theory before practice. Ask anyone to defend a particular sequence on empirical grounds, and the answer reduces to tradition. This is not a failing. It is what disciplines do when they lack a better mechanism for coordinating themselves. But it means the curriculum we have inherited is far less solid than it appears.

To deconstruct a course means to set the inheritance aside. Strip the readings. Strip the assignments. Strip the schedule. What remains should be a small set of claims about what students should know and be able to do. That is the course. Everything else is delivery. If the outcomes are vague or quietly procedural, they have to be rewritten first. In regulated fields, this forces a conversation with the accrediting body about whether the standards still describe competent practice. That conversation is overdue in most fields.

Then apply the test. For each outcome, ask whether AI can already do it competently, partially, or not at all. Outcomes AI fully handles should not be assessed anymore. Format an APA citation. Calculate a t-test. Translate a paragraph. Summarize a chapter. These belong to a vanished regime. What remains are the outcomes that demand judgment under ambiguity, interpretation, defense of choices, ethical reasoning, complex multi-stage tasks. These need sharpening. Then add the outcomes that are new. Students must learn to decompose a task and decide which pieces belong to the human and which to the machine. They must learn to specify inputs precisely. They must learn to verify, triangulate sources, revise AI drafts toward a real audience, and leave a recognizable human value-added in the final artifact. These are not soft skills bolted on. They are content. They belong in the outcomes list, with rubrics and assessments to match.

Only after the outcomes are revised can the course be rebuilt. New assignment sequences follow from new outcomes, not the other way around. A research methods course that drops formatting and adds source verification looks different from week one. A composition course that drops grammar drills and adds revision of AI drafts has a different rhythm. One reformed course is hard. A rebuilt major requires every faculty member in the program to do the audit, then coordinate so prerequisites still mean something. A reformed discipline requires cross-institutional collaboration, because no single department has the standing to declare what counts as competence in chemistry or accounting.

The reasons we resist are not obscure. The work is enormous. The cost is real. The collaboration is uncomfortable, because it forces colleagues to argue about what the discipline is for, a question most programs have spent decades politely avoiding. There is no vendor selling this as a turnkey solution, because it cannot be one.

The solution exists. The hard part is deciding, together, that we are willing and able to do the work. And yes, the governments should help. I know none of this is likely to happen, so it is going to get worse before it gets better. We just need some longer term perspective.

The Politics of AI Adoption in Higher Education. Why Resistance Is Not a Strategy

There are roughly three camps in higher education right now on AI: adopters, resisters, and the undecided. Adopters are working through genuine difficulties, trying to figure out what education means when students have tools that can write, analyze, and reason alongside them. The undecided are watching. Resisters are waiting for someone to rescue them from a situation no one is coming to rescue them from.

Resistance to AI in higher education is not a strategy. It is a posture. And it may cost us more than we can afford to lose.

Higher education was already losing the public before AI became a campus issue. Gallup found that American confidence in higher education fell from 57% in 2015 to 36% in 2024, recovering only modestly to 42% in 2025. Republican confidence collapsed from 56% to 20% over the same period. New America found that the share of Americans believing colleges have a positive effect on the country fell from 69% in 2019 to 54% in 2025. The reasons people give are consistent: cost, political insularity, and doubts about labor market relevance. We are an institution under sustained pressure from almost every direction. That is the condition in which we are deciding how to respond to AI.

The resistance position, at its clearest, holds that AI threatens learning integrity and that higher education should push back by restricting or excluding AI use. These concerns are not frivolous; they are legitimate. But the political logic is weak to the point of incoherence. Successful resistance would require persuading legislators to slow AI development, persuading businesses and professions to decline AI adoption, and persuading students to stop using tools that are free, powerful, and already on their phones. None of these outcomes is remotely plausible. The notion that faculty governance statements will reorder the AI development landscape is not a serious political analysis.

There is no coalition that will defend AI resistance in higher education. The right has little sympathy for the institution to begin with. The center is focused on workforce relevance. The left has its own complicated relationship with AI but will not make the defense of essay-writing pedagogy a political priority. If higher education becomes the institution that resists a technology the rest of the economy is adopting, we will not be signaling integrity. We will be confirming the suspicion that we are more concerned with protecting our own methods than with serving students. That story will be told loudly by people already looking for evidence to support it. 

The adopters are doing harder, less glamorous work. They are asking what learning outcomes mean when students have access to generative AI, rebuilding course and program structures around skills that require AI as a collaborator rather than a shortcut, and assessing how students reason, evaluate, and judge rather than what they produce on a first draft. The arrival of calculators did not eliminate mathematical education; it shifted emphasis toward conceptual understanding. The arrival of CAD did not kill architect schools; only the drafting courses. The arrival of legal research databases did not shut down law schools; it ended the careers of some paralegals. AI will similarly shift the locus of educational value without eliminating it. Our job is to figure out where that locus now sits.

That means revising learning outcome statements to specify what students can do with AI assistance. It means building assessments around judgment, argument, revision, and reflection. It means teaching students to use AI critically, which is itself a significant intellectual skill. None of this requires abandoning academic standards. It requires updating our account of what those standards are for.

We are not starting this conversation from a position of strength, and resistance will accelerate the erosion. It will confirm narratives we cannot afford to confirm and alienate students who need us to be useful to them. The conversation worth having is about curriculum and learning outcomes. That conversation is harder and may feel less virtuous  than resistance. It is also the only one that might actually help. 

"Tell Me What I Wrote": Reading, Ownership, and the New Logic of Learning to Write

There is a moment students describe that we do not yet have a clean name for. They have worked with AI across multiple stages of a research paper (gathering sources, collecting and analyzing data, drafting sections) and eventually assembled something much more sophisticated than they could have produced alone. Then they read it back and something odd happens. The text feels both familiar and foreign. They recognize the argument because they built it, piece by piece, but the precise formulation of a particular paragraph, the logical connection between sections, the implication of a finding; those belong to no one they can quite identify. They are reading a text they co-produced but do not fully inhabit.

We have long distinguished between two learning practices with a fairly clean conceptual border. Reading to learn meant exposing yourself to someone else's organized thought and absorbing it. Writing to learn meant using the act of composition to clarify and consolidate your own thinking. The distinction was clean because authorship was clear, and because comprehension was a precondition for production. You had to understand something before you could write about it coherently, and the ability to produce organized text was itself taken as evidence of comprehension. AI has inverted that sequence in a way that I think has genuine pedagogical consequences, most of them unexplored.

When students write research papers with AI assistance, neither model quite applies. The student is not reading someone else's thought. But the writing-to-learn model also breaks down, because significant portions of the text exceed the student's current understanding at the moment of production. The AI cannot produce the entire paper: data must be gathered, analyzed, interpreted, connected to theory. So the student works in stages, producing section after section, each one intelligible locally but not yet integrated into a whole they fully grasp. Coordination of those parts then becomes its own cognitive demand. You write something first, and comprehend what you wrote later: comprehend by revising. 

This is a genuine reversal of the traditional epistemic sequence. Before, instructors used the capacity to produce organized text as a reliable signal of comprehension. Now that signal is no longer reliable in the same way, but something more interesting has replaced it: comprehension becomes a goal to work toward after production, not a prerequisite for it.

Let me offer a provisional name for what students are doing when they return to a co-produced text to make sense of it: reconstructive reading. The student is rebuilding the logic of a text that is partly theirs, working backward from a produced artifact toward comprehension. This is not passive reception. It resembles what literacy researchers describe as the construction of a coherent situation model: the reader actively fills gaps, resolves contradictions, and builds an integrated representation of what the text means. What is new here is that the student occupies a dual position simultaneously: partial author and genuine reader of the same document. That position has no clean analogue in pre-AI pedagogy.

There is theoretical scaffolding available for understanding why this might actually work. Bereiter and Scardamalia's distinction between knowledge telling and knowledge transformation points toward something relevant. A student operating at the knowledge-telling level during composition (retrieving and assembling what they approximately know), may nonetheless produce text at the knowledge-transformation level, because the AI lifts the ceiling on what the assembled parts can become. Reading back that text is then an encounter with a more sophisticated version of one's own ideas. The research literature on desirable difficulty suggests that working through material just beyond your current competence produces stronger encoding than working with material you already control. AI-produced text that slightly outpaces the student's understanding may, under the right instructional conditions, function as exactly this kind of productive stretch.

I teach this explicitly. I ask students to query the AI about their own paper: what is this paper arguing? What do these concepts mean as I have used them here? This is not cheating or shortcutting; it is a structured form of self-explanation using the AI as a mirror. Research on elaborative interrogation shows that generating explanations for why things are true, rather than simply reading that they are true, substantially improves retention and transfer. Asking the AI to reflect your own text back to you in different terms is a version of that process. You are not outsourcing comprehension; you are scaffolding it.

There is also a motivational dimension worth taking seriously. Self-determination theory identifies ownership as a core driver of engagement. The student who reads an article assigned by an instructor has no stake in the text's existence. The student reading back a paper they assembled across multiple stages has a different relationship to that text, even if they cannot yet fully articulate what it says. The text is a record of their own intellectual effort, however distributed. That partial ownership may produce more scrutinizing, more motivated reading than most assigned reading achieves.

There is a prior problem here worth naming. In traditional writing-to-learn pedagogy, students could see clearly how far their actual writing fell short of what they were trying to say. The gap between intention and execution was visible on the page, and for many students it was discouraging. Bandura's work on self-efficacy suggests that repeated encounters with evidence of your own inadequacy erode the motivation to persist. The student who could not make the text do what they wanted it to do often concluded, not unreasonably, that they were not a writer. AI changes that specific dynamic. The co-produced text can be as good as the student's best thinking, often better, which means the student encounters their own ideas in a form they can respect. Reading that text back is not an exercise in confronting failure. It is an exercise in catching up to a version of yourself that the collaboration made possible.

The pedagogical task, then, is to organize situations that demand that re-reading actually happen: assembling the final paper from its parts, checking that the argument holds across sections, reconciling a finding in one chapter with a claim in another.

What this requires is a set of cognitive capacities that sit at the intersection of comprehension monitoring, self-explanation, and editorial judgment. Comprehension monitoring — knowing when you do not understand something in a text — is not automatic, and it is harder when the text is superficially fluent, as AI-assisted prose tends to be. The coherence traps in AI-assisted writing are subtle: individual paragraphs read smoothly while the connective logic between them is missing or contradictory. Students learning to detect those gaps are developing a skill that transfers far beyond any single paper.

The boundary between reading and writing has always been somewhat artificial. Skilled writers read their drafts as readers; skilled readers reconstruct arguments as actively as writers construct them. What AI does is force that boundary into visibility by splitting the production of text from its comprehension in time. That is disorienting, but it is also an opportunity. If we understand what is actually happening when a student reads back their own AI-assisted writing, we find cognitive territory that did not exist before,  and that may turn out to be more educationally valuable than the solitary writing tasks it is disrupting. 

AI Agents Miss the Point of My Work. Human-AI Synergy Is the Work

Software engineers got very excited about AI agents. I did not. Not because the technology is weak. Because the kind of work I do is fundamentally different from what agents are designed to handle.

An agent is a system that pursues a goal with limited human involvement. You assign a task, it plans, executes, checks, and revises. The appeal is obvious: less oversight, less friction, less human labor. That model fits work where the goal is clear, the steps are predictable, and success can be tested against explicit criteria. Software often looks like that.

My work does not. I write, research, and teach. In all three, value is not produced by handing off a task and waiting for a result. It is created in the interaction itself. A small amount of well-placed human input at each stage improves outcomes far beyond what AI can do alone, and far beyond what I could do without it. That is the real advantage. Not autonomy. Synergy.

This is the point the agent hype consistently misses. Human input is treated as a cost to minimize. In my work, it is the source of value. The crucial moves are often small and nearly impossible to formalize: changing the framing, shifting emphasis, spotting the hidden problem, recognizing that this case is unlike the last one. Those are not interruptions to the workflow. They are the workflow.

I see this plainly in a grading assistant I built. Grading appears to be a strong candidate for automation: prompts, rubrics, student papers, structured process. Yet the tool still requires adjustment for every assignment. A classroom discussion from the previous day changes what I want to reward. A pattern of shared misunderstanding changes what feedback is most useful. A stronger cohort shifts the tone and standard I want applied. The assistant is useful not because it runs independently, but because it stays responsive to the classroom. That responsiveness comes from me. Remove the human layer, and the output becomes flatter. Efficient, perhaps, but no longer particularly educational.

The same holds for writing and research. Important changes happen in the middle. A sentence opens a better line of thought. An objection reframes the whole argument. A vague idea sharpens through exchange. I am not executing a plan. I am discovering the plan as I go.

There is a deeper assumption worth naming. Most enthusiasm around agents rests on the premise that human involvement is a bottleneck, and that the ideal system needs us as little as possible. But for much intellectual work, the human is not the bottleneck. The human is the variable that determines whether the output is any good. We bring unspoken assumptions, changes of mind, local knowledge, and felt judgment. We redirect the process without always being able to articulate how. That is not a limitation of human work. That is what makes the work worth doing.

Agents will prove useful for processes that are complex but still predictable. That category is narrower than most people assume. In teaching, writing, and research, the task is rarely to execute a pre-set sequence well. It is to notice when the sequence itself needs to change.

For my kind of work, human-AI synergy is not a temporary compromise while agents mature. It is the model. 

The Relational University: A Vision for AI in Higher Education

Universities have a set of deep, structural problems that long predate artificial intelligence. Student engagement is thin. Bureaucratic barriers are thick. Advising is reactive and understaffed. Instruction often runs on a low-engagement lecturing models. Faculty who entered the profession to work with ideas and people spend most of their time on mechanical tasks. Students navigate systems designed for institutional convenience, not for learning. These are not new complaints. They are defining failures that decades of reform have failed to resolve. 

AI potentially offers a way to resolve them without reducing the workforce. The vision is direct: universities become more relational, not more automated. Faculty gain time for personal contact with students because machines absorb the mechanical parts of teaching. Instruction improves because AI makes genuine formative assessment practically possible for the first time.  Using results of previous assignments to design better subsequent ones is something the theory always called for but practice never had capacity to deliver. Advising shifts from reactive to proactive. Administrative barriers diminish for routine matters, freeing human staff to handle complex ones. The university does not shrink. It refocuses.

Advising is limited by labor. An advisor with a caseload of 800 students cannot know any of them. The result is a pull model: students must identify their own problems and seek help. Those who most need support are least equipped to ask for it. AI can change this by detecting risk patterns across enrollment, academic, and financial data, flagging students who are drifting before they reach crisis. But the flag is not the intervention. The intervention is a human advisor who calls, meets, and listens. If we build this well, AI handles the surveillance and triage and the bulk of simple questions; advisors handle the relationship and complex questions. The same constraint limits course scheduling. We know that certain combinations of courses and workloads predict failure. We know that work schedules, commute patterns, and academic preparation all interact with course selection. But no human advisor can model these interactions across hundreds of students each semester. AI could make risk visible at the point of decision, so that advisors and students choose schedules with open eyes. The underlying problem is the same: knowledge exists that could prevent failure, but the labor required to apply it at scale has always exceeded institutional capacity.

Instruction faces the same bottleneck. The simplistic lecture/exam teaching modes persists because faculty lack time for anything better. Substantive weekly feedback on student work is physically impossible at current teaching loads. So we compress assessment into a few high-stakes moments and call it evaluation. AI could absorb the mechanical first pass of formative feedback, freeing faculty to review, personalize, and respond to individual learning trajectories. More than that: formative assessment theory has always held that results of one assignment should inform the design of the next. No instructor teaching over a hundred students has time to analyze patterns across a set of papers and redesign the subsequent task accordingly. AI could close that loop, identifying collective strengths and weaknesses and drafting assessments calibrated to what students actually need next. The instructor reviews, adjusts, and teaches. If we get this right, the feedback cycle that formative assessment always promised becomes operational for the first time.

The downstream effect on faculty time could be substantial. Time recovered from mechanical grading and routine administration could go toward direct contact with students: structured individual meetings, small-group conversations about ideas rather than grades. The labor has always been there. It has just been allocated significantly to the wrong tasks. Similarly, administrative staff currently spend large portions of their time answering routine questions with definitive answers. AI can handle those instantly, not to eliminate positions, but to redirect human attention toward complex cases that require judgment.

None of this is guaranteed. The technology exists, but institutional habits are strong. Universities could just as easily use AI to cut costs, reduce staff, and further depersonalize the student experience. The reinvented university, more relational, more responsive, more human, will only emerge if we deliberately choose to augment human labor rather than replace it. The defining question is not what AI can do. It is what we decide to do with the capacity it creates. If we play our cards right, universities could become what they have always claimed to be: places where education happens between people, supported by systems that finally serve that purpose rather than obstructing it.

The Trouble with Refusal Rights: On the CCCC Resolution to Refuse Generative AI

The Conference on College Composition and Communication recently passed a resolution affirming "the rights of students and teachers to refuse to sign up for, prompt, or otherwise use generative AI in the writing classroom." The resolution draws on a companion document, "Refusing GenAI in Writing Studies: A Quickstart Guide" by Jennifer Sano-Franchini, Megan McIntyre, and Maggie Fernandes. I find much of what they say about the political economy of Big Tech persuasive. Yet the central move of casting the argument in the language of rights is a strategic and conceptual misstep.

When we talk about rights, we usually mean one of two things: legal rights, which are enforceable protections backed by law or contract, or moral rights, which are claims about what people are owed regardless of what the law says. The resolution tries to invoke both registers and achieves neither.

The resolution leans on the AAUP's 1940 Statement on Academic Freedom. That document does establish a real professional norm: faculty can select course materials, determine approaches, and assess student work without administrative veto. But notice what this actually protects. It protects faculty autonomy in making curricular decisions. It does not create a specific right to refuse any particular technology. When an instructor decides that a given tool does not serve her course, she exercises normal pedagogical judgment. No one calls that a right. It is simply teaching.

Academic freedom is symmetric. It protects choices, not specific outcomes. A resolution affirming one particular choice as a right tilts the field. It implies that using AI requires justification while refusing it does not.

There is a deeper problem with invoking the AAUP framework here. The AAUP has been explicit that academic freedom in teaching operates on two levels, and the collective level takes precedence. As the AAUP's own FAQ states, the shared academic freedom of a faculty to determine courses and materials "supersedes the freedom of an individual faculty member to choose a textbook that he or she alone prefers." The AAUP's statement on Freedom and Responsibility reinforces this: it is improper for an instructor to fail to present subject matter "as approved by the faculty in their collective responsibility for the curriculum." This is why we have curriculum committees and course approval processes. Individual instructors teach within a collectively sanctioned framework.

The CCCC resolution quietly reverses this logic. It asserts an individual right to refuse a specific technology, independent of any collective deliberation about what a writing curriculum should contain. If we establish the precedent that an individual instructor has a right to refuse AI on principled grounds, what else can an instructor refuse? The learning management system, on grounds that it embodies corporate surveillance? Plagiarism detection software, which the Quickstart Guide itself criticizes? Peer review platforms? Email? Each of these technologies carries ideological baggage. The line between principled refusal and personal preference becomes impossible to draw once you frame the question as one of individual rights rather than collective curricular judgment.

I have also struggled with a simple question while reading the resolution: What specific violation of rights does it aim to prevent? Who is forcing writing teachers to use ChatGPT?

The AAUP's 2025 survey found that 15 percent of faculty said their institution mandates AI use. But the same survey found that 81 percent must use learning management systems with embedded AI features. The "mandate" is mostly that Canvas or Google Workspace now has AI baked in. That is not the same as being told you must assign AI-assisted essays. I cannot find evidence of any American college or university requiring writing faculty to incorporate generative AI into their pedagogy. To the extent that real pressure exists, it takes the form of institutional nudging, not directives that could be resisted by invoking a right.

Rights are most powerful when they address a concrete threat. The right to free speech protects against government censorship. The right to due process protects against arbitrary punishment. What does the right to refuse generative AI protect against? Against being encouraged to try something? Against the zeitgeist? Rights are a heavy instrument. They should be reserved for heavy problems.

Buried inside the resolution is a pedagogical claim: that writing instruction develops human thought and expression, and that outsourcing parts of the writing process to a language model may undermine that development. The claim is debatable but plausible. However, it does not require the language of rights. It requires the language of curriculum and evidence. If generative AI undermines learning outcomes in first-year composition, instructors should not use it. Not because they have a right to refuse, but because it does not serve their students. By framing refusal as a right rather than a pedagogical judgment, the resolution removes the question from the domain where it should be argued and places it in the domain where it can only be asserted. You do not argue against a right. You respect it or you violate it. This forecloses the very inquiry the authors claim to value.

The resolution also affirms a student right to refuse AI. This is even more peculiar. Students are routinely required to use technologies they did not choose: a particular LMS, plagiarism detection services, specific software. No one frames these requirements as rights violations. A course has requirements. An instructor sets them. If the instructor has determined that AI engagement is central to the course, allowing opt-outs means running two parallel courses. If AI is not central, the point is moot.

The real concerns here deserve a better framework. Shared governance matters: if institutions sign contracts with AI companies without consulting faculty, that is a governance problem already addressed by existing norms. Pedagogical autonomy already exists: faculty can and do decide what technologies to use. The evidence question is primary and researchable. And the labor and environmental concerns, while legitimate, are matters of institutional procurement and social policy, not classroom pedagogy. An instructor who refuses ChatGPT on environmental grounds should, by the same logic, refuse Zoom, Canvas, and university email, all of which depend on data centers with significant environmental footprints.

There is a final paradox. The language of rights is supposed to project strength, but here it signals the opposite. When a profession asks for special protections against a new technology, it announces that it cannot figure out, through its own expertise and collective deliberation, how to respond to a changed environment. Math departments did not need a right to refuse calculators. They debated, experimented, and made curricular decisions. Some banned calculators from exams, some required them, and the question was always pedagogical: Does this tool help students learn, or does it let them bypass the thinking? Writing studies has every intellectual resource it needs to conduct the same kind of inquiry. Framing the matter as a right suggests otherwise. It suggests a profession that feels so besieged it must retreat behind quasi-legal barricades rather than do what professions do: deliberate, adapt, and lead.

Spitting Into the Wind

I watch my colleagues fight, and I understand the impulse. The desire to preserve what we built over decades of careful work comes from genuine care for learning. But much of what I see on campuses right now amounts to spitting into the wind. The effort lands back on the person making it, and the wind does not notice.

Consider the inventory. Turnitin added AI detection, and departments adopted it as a digital checkpoint. The detection is unreliable, generating false positives that punish honest students and false negatives that miss sophisticated use. Faculty end up in adversarial arguments about whether a 23% AI probability score constitutes an honor code violation. Browser-locking software was built for a world where the threat was a second tab. That world is gone. AI assistants are now embedded in operating systems, available through voice, woven into browser extensions. Locking down a browser is like reinforcing the front door while the back wall of the house is missing. 

Some faculty have turned to handwritten assignments, stripping away every advantage of digital composition in order to verify authorship. Others schedule 20- or 30-minute oral defenses for each student, a practice that collapses under its own arithmetic in a class of 35. Still others declare their classroom an AI-free zone, a principled stand that increasingly resembles teaching navigation while pretending GPS does not exist.

And then, in February, Einstein arrived: an agentic AI tool that promised to log into Canvas and complete entire courses on a student's behalf, from watching lectures to submitting assignments. The reaction was predictable. Social media erupted, faculty declared it the death of education, and within 48 hours the product was taken down after a trademark dispute over the Einstein name. Faculty breathed a sigh of relief. But the relief is misplaced. As one observer noted, the line between a flash in the pan and a harbinger of things to come is very thin. The underlying technology is open-source, improving rapidly, and replicable by anyone with modest coding skills. Einstein was a crude prototype. Its successors will not announce themselves with a viral marketing campaign. All of these measures share a common feature. They are perimeter defenses. They try to keep AI out of an existing structure rather than asking whether the structure still makes sense.

Here is what we are avoiding. The entire curriculum, in every discipline, needs re-examination from the foundations. That means returning to learning outcomes, asking which ones still hold, which have been made trivial by AI, and which new ones have become essential. It means rebuilding assessments from those revised outcomes upward. It means redesigning courses so the process of learning, not the product, carries the educational weight.

This is enormous work, and it cannot happen in one summer workshop. It requires sustained time, structured collaboration, and genuine institutional investment. Course releases for faculty redesigning their programs. Instructional design teams embedded in departments, not available by appointment three weeks out. A clear signal from leadership that this work matters as much as research productivity or enrollment targets.

That signal has not come. University leaders have mostly treated AI as a policy question rather than a curricular one. Faculty professional associations could be leading discipline-specific conversations about learning outcomes in a post-AI landscape. Some have begun. Most have not. A conference panel on "AI and Teaching" is not a plan.

Every semester that passes with the old curriculum intact is a semester of lost opportunity. Faculty exhausting themselves with detection and enforcement could be doing the creative, difficult, rewarding work of rethinking what their courses are for. They are dedicated teachers. They simply do what people do when the ground shifts and no one offers direction. They reinforce what they know. They defend what they built.

But the wind does not care. And the longer we spend spitting into it, the less time we have to turn around and walk somewhere that leads to solid ground. 

Why Hobbled AI Tutors Do Not Prepare Students for Real Learning with AI

I spent a good amount of time building what I thought was an ideal AI tutors for my courses. I made it carefully Socratic. I asked it to avoid direct answers, to respond with questions, to nudge students toward their own reasoning. Technically, it functioned just as I designed it. When I used it like a student who is tired and under time pressure, the charm faded quickly. I wanted a clear explanation, and it kept giving me more questions. After another round of tuning, I tried to make it friendlier and more supportive. Students then told me that the tutor felt noisy and overwhelming. At that point I understood that I was training them to handle my special tutor, not the kind of AI they actually meet outside the class. That is a strange educational goal.

Hobbled AI tutors feel safe and ethical, but they miseducate. They train students to work with artificial constraints that disappear the moment they open a normal AI system in a browser. We act as if a restricted tool is a good stepping stone toward a more powerful one. In practice, students build habits that do not transfer. They learn that AI always refuses direct answers, always behaves in a certain tone, always follows classroom rules. Then they encounter a general model that does none of those things, and much of their practice becomes irrelevant.

This is not a new pattern. Education has long relied on simplified versions of reality. We create word problems that clean up numbers, experiments that always work if you follow the manual, and case studies that fit on two pages. Those devices lower risk and cognitive load. They provide a controlled environment where mistakes are safe and visible. The logic is understandable. A student who is still learning should not make an error that costs a patient, a client, or a company. For that reason, some version of a sandbox is necessary.

The trouble appears when we forget that the sandbox is not the field of practice itself. The rules inside a controlled environment do not match the rules in workplaces, in graduate study, or in everyday online life. In many disciplines, educators now try to bring more authentic tasks into courses, so that students face messy data, conflicting evidence, and imperfect instructions. With AI, that tension between safety and authenticity becomes sharper, because the distance between the restricted version and the real tool is very large.

Once we start to protect students by modifying AI itself, we create a peculiar hybrid. We instruct the model never to give full solutions, or to delay any concrete suggestion until several rounds of questions. We narrow its sources and formats. We insist on a very specific teacherly tone that no professional tool will ever reproduce. The model still has the power to generate complex text, but it is prevented from using that power in the ways that matter most outside class. Students feel both burdened and confused. The system is strong enough to dominate the interaction, yet weak enough to be unhelpful when they need efficiency.

I do not think the answer is to abandon scaffolding. It is to move scaffolding out of the model and into our teaching. Instead of hard technical restraints, we can offer social and cognitive guidance. We can talk about appropriate and inappropriate uses of AI for a given assignment. We can model how to break a task into steps and how to design prompts for each step. We can teach students to read AI output with the same suspicion they bring to an unfamiliar website, to check claims against other sources, and to notice when the model clearly fabricates information.

In my own courses, this leads to a split strategy. For routine classroom work, a custom AI assistant still makes sense. It can generate weekly reading lists aligned with the syllabus, create small formative quizzes, and support simple administrative tasks. Those are narrow functions where tight constraints are actually helpful, because students do not need to reuse those bots later in life. They will not need my quiz generator at work.

For substantial projects, I will now invite students to use the same broad AI tools that everyone else uses. I want them to confront vague or partial answers, and to learn how to ask for clarification. I want them to see different versions of an argument and to practice choosing which one is worth pursuing. That means teaching very specific skills. For example, how to ask the model to reveal its uncertainty, how to request alternative lines of reasoning, how to move from a generic first draft to a more precise second version, and how to document their own use of AI in an honest way.

This approach accepts that mistakes will happen. Some students will trust the model too much. Some will misread an answer. But those risks already exist when they use AI on their own phones and laptops, far away from the course platform. In that context, a hobbled classroom tutor does not protect them. It leaves them underprepared. They know how to navigate a special kind of AI that appears only inside one course, and they lack practice with the systems they actually depend on.

An AI tutor that is permanently handicapped may look safer to us as instructors, but it does not prepare students for real learning with AI. It produces clever conversations inside a narrow frame and trains habits that fail outside that frame. I would rather expose students to the real tools and walk with them through the confusion, than give them a polished imitation that vanishes as soon as the course ends.

Learning With a Machine in the Room: What Students Said After a Semester of AI-Integrated Teaching

Last semester I ran an experiment across three courses that I will call Course A, Course B, and Course C. Each course used an AI Class Companion as a constant presence rather than an occasional tool. Students interacted with it for planning, drafting, testing knowledge, and reflecting on their progress. The exit survey gives an initial picture of how students perceived that experience.

Seventy seven students completed the survey. The headline number is straightforward. Fifty nine students reported that they learned more than they would have in a typical class without AI support. That equals 76.6 percent of respondents. Thirty nine selected “Somewhat Agree,” twenty selected “Fully Agree,” fifteen selected “Somewhat Disagree,” and three selected “Disagree.” These numbers suggest a strong perceived learning gain, but not unanimity.

Another important question asked whether students would take another course using an AI Class Companion. Sixty three students agreed or fully agreed. Thirty two chose “Fully Agree,” thirty one chose “Somewhat Agree,” eleven chose “Somewhat Disagree,” and three chose “Disagree.” This pattern matters because willingness to repeat an experience is often a better indicator of acceptance than enthusiasm in the moment.

The strongest agreement appeared in the skills question. Seventy two students said their AI skills increased significantly. Fifty six selected “Fully Agree,” sixteen selected “Somewhat Agree,” three selected “Somewhat Disagree,” and two selected “Disagree.” Even students who were skeptical about learning outcomes often acknowledged growth in technical fluency.

Below is a simple summary table of the core survey items.

Survey Snapshot (N = 77)

Statement	Fully Agree	Somewhat Agree	Somewhat Disagree	Disagree	Agree Total
Learned more than typical course	20	39	15	3	59 (76.6%)
Would take another AI supported course	32	31	11	3	63 (81.8%)
AI skills increased significantly	56	16	3	2	72 (93.5%)

The numbers alone do not tell the full story. Students did not describe AI as flawless or magical. Several comments mentioned frustration when the system misunderstood context or produced shallow responses. That tension is important. The Companion was designed to provoke critique rather than passive acceptance. Many students reported that their stance toward AI changed during the semester. Early interactions focused on efficiency. Later reflections described more careful questioning and revision.

It is also important to note that the survey captures only perception. There is rich data beyond these numbers. Students generated extensive interaction logs with the Class Companion across the semester. Those logs include prompts, revisions, and moments where students corrected or challenged the system. In addition, each course produced substantial final artifacts such as research manuscripts, professional portfolios, and organizational proposals. Together, these materials provide a detailed empirical record of how learning unfolded in practice. I plan to analyze those interactions and final products separately.

One pattern that emerges from the survey is continuity. Students interacted with the Companion repeatedly rather than only at moments of difficulty. Many described returning to earlier conversations to revise ideas or test their understanding again. That continuity appears to have shaped perception of learning. Students often framed the Companion as a thinking partner that extended learning time beyond formal meetings.

At the same time, variation across responses should not be ignored. About one quarter of respondents did not agree that they learned more than in a typical course. Some learners may prefer clearer structure or less autonomy. Others may find constant interaction with AI cognitively demanding. These courses asked students to assume a high level of responsibility for their own learning process. For some students that autonomy felt empowering. For others it introduced uncertainty.

There is also a methodological concern that must be acknowledged openly. The survey results may be influenced by social desirability bias. Students may feel pressure to respond positively when a course emphasizes innovation or when AI is framed as central to the learning experience. Even though participation was voluntary and responses were anonymized after grading, the possibility of bias remains. For that reason, I treat these numbers as provisional indicators rather than definitive proof of impact.

Another interesting finding involves how students described their relationship with AI. Many said that the Companion felt supportive but non judgmental. That framing may matter more than technical capability. When AI becomes part of the learning environment rather than an external evaluator, students appear more willing to experiment, make mistakes, and revise their thinking.

What do these numbers suggest overall. First, most students perceived increased learning and strong skill growth. Second, willingness to repeat the experience was even higher than reported learning gains. Third, skepticism and frustration remained present, which may be a healthy sign that students were not treating AI as an authority.

The experiment raises a larger question about pedagogy. AI does not automatically improve education. What matters is how courses are structured around it. When AI becomes a continuous cognitive environment, students begin to externalize drafts earlier, test ideas more frequently, and engage in iterative reflection. The exit survey captures that transition from novelty toward routine practice.

However, I consider the main point to be proven: the use of AI does not prevent learning. 

If You Cannot Solve AI in Your Classroom, That Is Not Proof It Cannot Be Solved

When the first big AI tools became public, the first thing I did was join a handful of Facebook groups about AI in education. I wanted a quick, noisy sense of what people actually do and what they fear. It felt like walking into a crowded hallway between conference sessions, with excitement, outrage, resignation, and some careful thinking mixed together. In that mix I began to notice one pattern of pushback that worried me more than any privacy or cheating concern.

The pattern sounded like this: "I tried AI in my class, it did not work, therefore it cannot work." Sometimes it was softer, something like "If I cannot figure out how to use this in a responsible way, then there is no responsible way." This is a classic fallacy, the argument from personal incredulity. In plain language it is the belief that if a solution is not obvious to me, then no solution exists. Many academics would tear apart this argument in a student paper, yet some repeat it when the topic is AI in teaching.

In higher education this fallacy feeds on a deeper habit. Most faculty members think of themselves as experts in teaching. We earned doctorates, we lectured for years, we survived student evaluations. It feels natural to think that we have figured out teaching as we went along. Yet teaching is a complex problem, shaped by cognitive science, sociology, technology, and institutional constraints. Being good in a discipline does not automatically make one an expert in this kind of complexity. Classroom experience is valuable, but it is not a substitute for engagement with a knowledge field.

AI exposes that gap very quickly. The first time someone asks a chatbot to write an essay and the result looks like a B minus paper, the temptation is to generalize. "Well, that kills writing assignments." The first time a student cheats with AI, the next step appears just as obvious. "Well, that kills academic honesty." After two or three such impressions it is easy to feel that one has seen enough. In reality, one has seen a tiny, biased sample at the worst possible moment, when one knows the least.

By now there is a growing body of research and practical accounts of successful AI integration into teaching and learning. Colleagues document AI supported feedback cycles that help students revise more often. Others describe using AI to model thinking aloud or to simulate peer critique. On social media, teachers share specific prompts, assignment designs, and policies that reduce cheating and increase authentic work. This is no longer a complete mystery. There are patterns, lessons, and tested strategies. We just do not see them if we stare only at our own classroom.

The big picture work starts one level higher than tools and tricks. It starts with deconstructing a course all the way down to its learning outcomes. What exactly are students supposed to know and be able to do, in a world where AI is a normal part of knowledge work? Rather than trying to defend old outcomes against AI, we can revise those outcomes to include AI related competencies, such as prompt design, critical evaluation of AI output, and collaboration with AI in discipline specific tasks. Once the outcomes fit the new world, we can reconstruct the course upward, aligning readings, activities, assignments, and assessments with those updated aims. At that point AI is no longer an intruder. It becomes part of what students are explicitly learning to handle.

This is what I mean by a scholarly stance toward teaching. We already know how to do this in our research lives. We begin with a question, look for existing literature, notice methods and results, then run small experiments of our own and compare our findings to what others report. AI in education can be approached in the same way. Before banning or fully embracing anything, we can read a few recent studies, scan what thoughtful practitioners report, try a limited pilot in one course, and gather data that goes beyond a couple of loud student comments.

Some colleagues tell me they do not have time for this. I believe them. The workload in higher education is often absurd. Yet we would never accept "I do not have time" as a reason to ignore scholarship in our own disciplines. No historian would proudly say, "I just ignore recent work and go with my gut." No chemist would say, "I saw one failed experiment with a new method, so the method is impossible." When we treat teaching as exempt from scholarly habits, we send a message that the learning of our students is somehow less real than our research.

What worries me most is not moral panic about AI or even poorly designed bans. It is the quiet decision by thoughtful people to stop at "If I cannot see the solution, it does not exist." In research we teach students to distrust that move. We tell them to assume that someone, somewhere, has thought hard about the same problem. We tell them to read, to test, to revise. When it comes to AI and teaching, we owe our students the same discipline. The fact that I cannot yet see how to integrate AI well is not proof that nobody can. It is only proof that I am not done learning.