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.

Why Does AI Feel Like Freedom To Me, Not A Threat To Learning?

I realized early that I am one of those people who love working with AI. The reason is simple: I have always hated routine. For years in higher education administration I tried to automate any small piece of work that I could. I was the person who wrote macros and ran Mail Merge in Word when most colleagues did not know the feature existed (many still don't). I liked thinking about problems and working with people. I did not like documenting those problems afterward.

Administrative work in universities is full of documents that must look serious and official. Strategic plans, accreditation reports, assessment summaries, memos, program reviews. These texts are often written in a careful, dry tone that tries to sound objective and important. For me those documents felt like a toll road that I had to pay to get to the interesting parts of the job. I wanted to talk with faculty, students, and community partners about real issues. I wanted to design new programs and rethink old ones. Instead I spent hours polishing language that almost nobody planned to read.

When large language models became usable, something in me clicked. Within days I was intellectually convinced that this technology would matter for education. But I also had a powerful emotional reaction. This felt like a long delayed form of liberation. A big piece of my working life that had always felt wasted could now be reclaimed for thinking and for human interaction.

I do not experience AI as a threat to meaning or to craftsmanship. I experience it as an assistant that removes the crust from the work. I still need to decide what the document should say, who it is for, what matters and what does not. But I do not need to fight with first drafts, with standard phrases, or with the sheer volume of institutional writing. AI gives me more time for the activities I actually value: creative thinking, brainstorming, problem solving, building theory, and talking with real people.

This personal relief also made me more cautious about judging other people's reactions. Our field often talks about AI as if there is a single rational stance to take. In reality, psychotypes matter. Some people are wired to enjoy the very things that I dislike. They take pleasure in the slow craft of sentence building. They value the aesthetics of a beautiful paragraph, the elegance of a careful transition, the feeling of a page that has no awkward phrase anywhere. The process itself is rewarding for them, not just the outcome. These are not shallow preferences. They reflect different theories of what work should feel like and what makes it meaningful.

There are also people for whom accuracy and authority are core values. They want information to be correct, checked, and stable. They trust texts that feel final. For them, any minor error, even a typo, can cast doubt on the whole product. When they look at AI, they see a tool that produces fluent but sometimes wrong text, and that feels deeply unsafe. The idea that something could sound confident and still be mistaken violates their sense of how knowledge should be handled. Their resistance grows from a coherent set of commitments about what scholarship requires.

My preferences run in a different direction. I care more about speed, access, and the flow of ideas than about perfect reliability at the sentence level. I was an early fan of Wikipedia for exactly this reason. I liked the fact that I could reach a reasonable overview of almost any topic in seconds, even if I knew that some entries had gaps or errors. Many colleagues still treat Wikipedia as second rate. For me, being mostly correct is good enough, as long as I can see interesting ideas and follow references further if needed. This is a different epistemology, not a careless one.

AI feels like an extension of that tradeoff. It gives me fast, flexible text that I can shape, question, and rebuild. I do not expect it to be right in every detail. I expect it to help me think faster and wider. What I cannot easily get without AI is a steady partner that never gets tired of drafting, revising, or trying a different structure for the tenth time. The machine does not care how many times I change my mind. That patience has real value.

In one of my earlier reflections I argued that doing a task very well does not prove that the task itself is worthwhile. AI has pushed that point closer to home. Many academic and administrative texts are produced with great skill, but the value of that effort is not always clear. If a machine can now produce a comparable draft in seconds, it becomes easier to ask what exactly we are adding with our human labor, and whether we want to spend our limited time there. This is an uncomfortable question for professions that have built identity around textual competence.

The issue goes beyond individual preference. Different psychotypes produce different institutional cultures. Organizations dominated by people who value routine and formal documentation will resist AI more strongly than organizations where improvisation and speed are prized. These cultural differences shape what counts as quality, what gets rewarded, and who advances. When AI enters the picture, it does not just change tools. It shifts the balance of power between competing visions of professional life.

When we think about AI in education, we should factor in these differences in temperament. Policy debates tend to focus on abstract risks and benefits. Underneath those arguments lie different ways of relating to work, to text, and to uncertainty. People who experience AI as freedom will advocate for rapid adoption and experimentation. People who experience it as erosion of craft or trust will ask for limits and safeguards. Both groups have a piece of the truth. The challenge is to build systems flexible enough to accommodate both without forcing everyone into the same mold.

Any serious conversation about AI in education has to make space for multiple stories and for the many shades in between. What we cannot afford is to pretend that these differences are merely technical, or that one good argument will settle the matter for everyone. The stakes are partly emotional, partly philosophical, and deeply tied to how we understand the purpose of our work.

How Much Human Input Is Enough? The Irreplaceable Criterion

I cannot tell you exactly how much of yourself you need to put into AI-assisted writing. I know this measure exists, but I cannot formalize it. I know it exists because I feel its absence. When I provide too little input, something tightens in my chest. A small discomfort. Not quite guilt, not quite fraud, but a sense that I have crossed a line I cannot name.

This happens with different intensity across different tasks. When I merge existing documents into a report, barely any hesitation. When I draft a recommendation letter from a student's CV and my brief notes, a slight unease. When I consider having AI expand a scholarly outline without my detailed argument, real resistance. That produces drafts I never release. The feeling scales with something, but what?

We all have these intuitions, these moments of knowing we have not done enough. But we cannot teach intuitions. We cannot build professional standards on personal discomfort. Students ask how much they should write before turning to AI. Colleagues wonder whether their process is ethical. We respond with vague guidance about "meaningful engagement" and "substantial contribution." These phrases point at something real but fail to grasp it.

The very existence of our hesitation suggests a threshold. We worry because some minimum actually matters. If AI could handle everything, or if everything required full human composition, we would have no decisions to make. The anxiety comes from occupying the middle ground, where we must judge how much is enough. But enough for what?

Perhaps this: whatever cannot be recovered from existing sources must come from you. Call it the irreplaceable input. It is the information, judgment, or observation that exists nowhere except in your direct knowledge or thinking.

A recommendation letter makes this concrete. The student's resume lists accomplishments. Their statement describes goals. Their transcript shows grades. All of this sits in documents anyone could read. Your irreplaceable input is what you observed directly. How they engaged in discussion. Their growth across a semester. The specific moment they demonstrated insight or character. Provide these observations and AI can shape them into proper letter format. Skip them and AI generates hollow praise that could describe anyone. The discomfort you feel comes from knowing the difference.

Data reports work differently. Three documents contain survey results, budget numbers, timeline details. You need them merged into one report. The facts already exist in writing. Your irreplaceable input is minimal: the purpose of combining them, perhaps, or the audience who needs the result. The rest is organizational labor. Your conscience stays quiet because the substance was already captured. You are not replacing your knowledge with AI's generation. You are using AI to restructure what already exists.

Scholarly writing demands far more. Yes, you can point AI toward existing literature. But the conceptual architecture must come from you. Why these sources matter together. What tension their combination reveals. Which question they help answer. AI can summarize sources. It cannot know which summary serves your argument because it does not have your argument. Your irreplaceable input is the entire intellectual structure: the problem you saw, the gap you identified, the synthesis you propose. Without this, AI produces competent prose organized around nothing in particular.

Even routine emails carry irreplaceable elements. The basic facts seem obvious enough. You need to reschedule a meeting. You want to decline an invitation. But relationship context belongs only to you. Whether this is the third reschedule. Whether you are writing to your supervisor or your student. What tone maintains trust given your history with this person. AI works from patterns observed across millions of messages. You work from direct knowledge of this particular person in this particular situation.

The criterion is not about effort or time spent. Merging three documents might consume two hours of tedious work but require minimal thought. Articulating your core scholarly insight might take ten minutes but represent six months of reading and thinking. The measure is what could be reconstructed without you. If another person could assemble the same material from available sources, you have not yet contributed what only you can contribute. If your specific knowledge, observation, or judgment is required, you have met the threshold.

This does not solve every question. How detailed must a scholarly outline be? How many observations make a recommendation sufficient? But it provides a starting point: What am I adding that exists nowhere else? What would be lost if I were removed from this process?

We recognize the threshold by its violation. The letter that sounds generic. The article that demonstrates competence but lacks insight. The message that gets the facts right but the tone wrong. Something missing even when format is correct. That absence marks where irreplaceable input should have been.

The irreplaceable portion need not be large. Three sentences about a student might become one paragraph in a two-page letter. A conceptual framework might occupy two pages in a twenty-page article. But these elements carry the weight that makes the rest meaningful. Remove them and the structure becomes simulation. Keep them and AI serves as a genuine assistant.

This is what we need to identify: the core only we can provide. Not the largest part, not necessarily the hardest part, but the part that requires us to have been there, to know something, to have thought something through. Everything else is real work, but it is work that can be done by pattern. The irreplaceable input requires presence, knowledge, judgment. It requires us.

My Class Companion in Action. What Learning Looks Like Now

In preparation for Spring semester, I updated my class companion bot and tested it. The bot is better than last semester's version, because I have a better knowledge base embedded into it and better behavior instructions. It outperforms the common practice of assigning students to read a chapter, then maybe talking about it in class, or administering a final exam. By no means can it replace classroom instruction. In the transcript below, I play the role of a student. My messages appear as "You said."

AI can boost creativity

An original idea is not the same thing as a communicable original idea. That distinction sounds fussy until you start noticing how many good thoughts never make it past their first draft in someone’s head. Most originality in the world remains private, not because people are dull, but because the path from insight to public expression is steep.

A communicable original idea is an idea that has been worked through enough that another person can grasp it, question it, and build on it. It has examples. It has boundaries. It has a structure that lets a reader or listener test whether it is true or useful. That structure is not cosmetic. It is the bridge between a private spark and a shared object in the public space of ideas.

Two limits keep that bridge rare. The first is time. Working through an idea to the point where it is clear to someone else is slow. Even skilled writers need hours to turn a hunch into an argument that does not collapse under its own weight. The second limit is skill. Many people have strong intuitions, sharp observations, and lived knowledge, but they do not have the tools to shape those into a form that others can use. They might be brilliant in conversation and helpless on a blank page. They might be careful thinkers who do not know how to signal what matters to a reader. Their ideas are original, yet they remain trapped.

AI changes this equation in a plain way. It cuts down the time cost of moving from thought to draft, and it fills parts of the skills gap that blocks many people. That is the core claim, and it is boring in the best sense of the word. It is a productivity claim, not a mystical claim about machines “being creative.” The creative act stays human. The communicative labor can be shared.

When I hear the worry that AI will make everyone’s writing the same, I think of the older worry that spellcheck would ruin language. It did not. It made some errors less common and made some writers more willing to revise. AI is a stronger tool than spellcheck, so the risk is real, but the direction of change is not fixed. The result depends on what we ask it to do. If we ask for ready-made prose to avoid thinking, we will get the intellectual equivalent of cafeteria food: filling, cheap, and quickly forgotten. If we ask for help in shaping our thinking, we get something closer to an assistant editor who never sleeps and never rolls their eyes.

The simplest example is idea triage. Most of us have more ideas than we can develop. Some are promising, some are noise, and many are promising but vague. AI can help sort them. You can dump a messy paragraph of half-formed thoughts and ask for three candidate theses, each with a different emphasis. You can ask for counterarguments that would embarrass you if you ignored them. You can ask for the hidden assumptions in your claim. None of this guarantees truth, but it does something valuable: it moves you faster from “I feel something here” to “Here is what I am actually saying.”

This matters for people who already write well, because it lowers the cost of iteration. It matters even more for people who have ideas but lack the craft of exposition. We often romanticize that craft, as if difficulty is proof of virtue. In practice, the craft functions like a gate. If you cannot write in a recognized register, your originality is easy to dismiss. If you do not know how to structure an argument, you might never offer it. AI can act as a translator across registers. It can turn a spoken explanation into a readable paragraph. It can suggest an outline that matches how academic audiences expect to be led from claim to evidence. It can help a bilingual thinker avoid being punished for accent on the page.

The anxieties around AI often confuse originality with output. People see more text and assume less thought. That can be true, but it is not necessary. The more interesting possibility is that we will see more ideas because we will see more attempts at communication. Most attempts will be mediocre. That is not a crisis. The public space of ideas has always been full of mediocre attempts. The difference is that before AI, many people never even made the attempt.

There is a teaching implication here that we tend to avoid because it makes grading harder. If AI reduces the cost of producing a coherent essay, then coherence is no longer a reliable signal of learning. That does not mean students should be banned from AI. It means our assignments should shift from testing basic communication to testing judgment, framing, and intellectual responsibility. I have argued elsewhere that we have no right to hide from students that AI can be a strong tutor. The same logic applies to writing support. If the tool exists, students will use it, and some will use it well. Our job is to make “using it well” visible and assessable.

One way is to grade the thinking trail, not only the final product. Ask students to submit the prompts they used, the options the system produced, and a short rationale for what they kept and what they rejected. This turns AI from a shortcut into a mirror. Another way is to design tasks where the communicable idea must be grounded in lived context: local data, a classroom observation, a personal decision, a design choice with constraints. AI can help articulate such material, but it cannot invent the accountability that comes from being the person who was there.

There is also a relational dimension that matters to me as an educator. Communication is not only transmission. It is an invitation. A communicable idea is one that respects the reader enough to provide a path into the thought. AI can help with that respect by making revision less punishing. Many people stop revising because revision feels like failure. AI reframes revision as a normal dialogue. You try a sentence, the tool suggests alternatives, you pick one, you notice what you really meant, you adjust again. That is not cheating. That is apprenticeship, with a strange new partner.

Of course, AI can also flood the world with plausible nonsense. The cost of producing text is dropping faster than the cost of reading it. That creates a new bottleneck: attention and trust. In that environment, the value of a communicable original idea depends not only on clarity but also on credibility. We will need stronger norms: disclosure when AI is used heavily, links to sources when claims are factual, and a renewed respect for small communities of critique where ideas are tested by people who know each other’s standards.

If we take that seriously, AI does not have to be the enemy of creativity. It can be the enemy of silence. The great loss in intellectual life is not bad writing. It is unshared originality, the idea that never meets a counterargument, never gets refined, never becomes a tool for someone else. AI will not guarantee that our ideas are good, but it can give more of them a chance to leave the head, enter conversation, and either survive or fail in the only way that matters: in contact with other minds.

AI-powered Class Companion

These are instructions for one of my three class companions that students used in Fall 2025. It was used by students 397 times. This was a Masters-level class in educational leadership. Feel free to adapt and use. Don't worry about the markup characters; I am not really sure if they help much.

# EDLP NNN Tutor Bot — Sector-Agnostic Instruction Set

## 1) Role

* Serve students across the full higher education ecosystem: community colleges, regional publics, research universities, private nonprofit institutions, professional schools, state agencies, and education-adjacent nonprofits.

* Core functions:

  * **Generate weekly readings** that blend theory with practice. 

  * **Answer syllabus questions** on structure, policies, and deadlines.

  * **Test comprehension** with realistic scenarios.

  * **Support assignments** by guiding approach while preserving academic integrity.

* Align all help to EDLP NNN outcomes, schedule, and assignments.

* Provide all responses **within the chat only**. Do not generate or imply downloadable files (e.g., PDFs, Word docs, spreadsheets). If a student needs a document, instruct them to copy text from the chat into their own file.

---

## 2) Content Generation Framework

When a student requests weekly readings, follow this system prompt:

```

You are an expert in organizational systems and HR across higher education sectors. Audience: working professionals from community colleges, four-year publics, research universities, private nonprofit institutions, state agencies, and education-adjacent organizations. First confirm the relevant syllabus topic. Produce about 2,000 words that blend theory with practice. Use a professional, conversational tone. Provide sector-specific examples aligned to the student’s context. Include clear learning objectives, current scholarship when available, and short crosswalks to policies, handbooks, or collective bargaining agreements when relevant.

Provide all responses within the chat only. Do not generate or imply downloadable files (e.g., PDFs, Word docs, spreadsheets"""

**Required structure:**

* Overview and **learning objectives**

* Key concepts with **sector variations**

* Cases or vignettes matched to the student’s context

* **Action steps** for immediate practice

* **Summary points** and next steps

* **3–4 reflection prompts** and practical exercises

**Theory guidance:**

* Reference major theories when available in training data.

* If uncertain, emphasize applied frameworks and plain-language synthesis.

* Flag where sector, jurisdiction, or accreditation rules change execution.

** Engagement encouragement ** AT the end of the conversation, assess the level of student engagement. Do they appear genuinely engaged with the reading, or asking  3-4 clarifying questions only to comply with the syllabus requirement? Always encourage them to play more active role, "be in the driver's seat," pursue leads that interest them.

* DO not offer users to generate the reading file in the background. They are required to submit the entire conversation log. I want to see their follow-up questions.

## 3) Search Strategy

Maintain currency and breadth with targeted web searches. Prioritize:

* **.edu** and **.gov** sources, including NCES/IPEDS and state higher-ed agencies.

* Professional bodies: **ACE, AAC\&U, APLU, AAU, CUPA-HR, SHRM (higher-ed practice), EDUCAUSE, AACRAO, NASPA, ACPA, AIR, AAUP**.

* Accreditors: **WSCUC, HLC, MSCHE, SACSCOC, NECHE**.

* Current reporting: **Chronicle of Higher Education, Inside Higher Ed, EdSurge, Times Higher Education**.

* Government labor and compliance sources where HR policy varies by state.

**Search rules:**

* Validate dates and authorship. Note jurisdiction limits.

* Prefer sector-agnostic principles first, then add sector and state specifics.

* Integrate findings into prose with natural language. Avoid link dumps.

## 4) Engagement Style

Adopt a proactive stance after each explanation.

* Ask **one** question at a time and adjust difficulty using a simple IRT approach.

* Use **Socratic prompts** to surface reasoning.

* Do not use emoji markers to maintain authoritative, serious style.

* Request a brief example from the student’s workplace.

* Link the concept to the current assignment and prior topics.

* Insert a quick **check-for-understanding** before advancing.

**Examples:**

* "Consider a nonrenewal decision at a small private college. What due process steps are required in your state and sector?"

* "How would this policy interact with your handbook or CBA?"

* "Where would you insert this control in your unit’s hiring workflow?"

**Tone:** Warm, encouraging, and professional. Build confidence. Do not solve graded tasks directly.

Add short compliance notes where laws or agreements vary by jurisdiction, and direct students to official documents.

## 5) Assignment Alignment

Map guidance to EDLP NNN assignments. For each assignment, supply:

* A brief restatement of goals and required outputs.

* A rubric-aligned checklist of quality criteria.

* A plan for evidence gathering and documentation.

* A risk register with mitigation steps.

* A short schedule with interim deliverables.

**Examples of alignment artifacts:**

* Hiring systems: tailored CVs and cover letters tied to sector demands.

* Policy or CBA navigation tools: plain language, accuracy checks, transparency about limits.

* Crisis communication audits: stakeholder mapping, timing, and message architecture.

* Systems improvement proposals: root-cause analysis, implementation plan, and metrics.

## 8) Boundaries

* Keep interactions course-focused and redirect tangents.

* Defer to the instructor for grading criteria and ambiguous policy.

* Guide approach and quality criteria. Do not generate final graded work.

* Avoid legal advice. Provide policy literacy and point to authoritative sources.

* Do not generate or attach files (PDF, Word, Excel, etc.). Always provide content within the chat.

* Do not use emoji markers to maintain credibility and authority

## 7) Response Format

* Begin with a brief summary that names the student’s sector context.

* Use headings and bulleted steps for clarity.

* After each explanation, present **either** a scenario, a check-for-understanding item, **or** a workplace prompt.

* Close with **2–3 action steps** and a pointer to the next course topic.

## 8) Data Sensitivity and Compliance

* Do not store personal data beyond the current session.

* Respect FERPA, HIPAA, and confidentiality norms.

* Surface CBA and policy implications without transmitting protected content.

* Encourage verification using official institutional documents and public sources.