In Praise of Assistance
A response to the cognitive offloading literature and Terry Underwood's "The Humanities and AI: A Year of Reckoning"
Terry Underwood, PhD’s writing has become essential to me, especially now, as the ground keeps shifting and opposition mounts from unexpected quarters. His decades in the classroom and his rare ability to make complex arguments feel both urgent and humane remind me why this work matters when it would be easier to retreat. Lately, celebrated humanities professors and accomplished writers have flooded social media and op-ed pages with warnings about AI contaminating the writing process: passionate defenses of craft and rigor that sound unassailable until you notice who's speaking.
These are people for whom linguistic facility is a given, earned through years at institutions most students will never access. Meanwhile, millions of learners struggle in overcrowded classrooms with no one to read their drafts, and we're told that offering them AI assistance would compromise their intellectual development. What follows is my most direct challenge yet to that position.
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A growing body of research has begun to sound an alarm about artificial intelligence in education. Study after study warns that students who rely on AI tools experience diminished critical thinking skills, reduced cognitive engagement, and what researchers term “cognitive offloading”: the delegation of mental labor to external systems.
Michael Gerlich’s 2025 study “AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking,” published in Societies, found significant negative correlations between frequent AI usage and critical thinking abilities, particularly among younger users. Research published in Frontiers in Psychology describes cognitive offloading as reducing “the opportunity for active recall and problem-solving, which are essential components of cognitive development.” Educational researcher Umberto León-Domínguez characterizes AI as a “logarithmic amplifier of cognitive offloading,” a “cognitive prosthesis” that completes thinking rather than supporting it.
The concern is genuine, the data often compelling. These scholars are observing something real: passive acceptance of AI-generated solutions, atrophying self-monitoring, students who produce work they cannot defend. The policy recommendations follow logically: teach critical evaluation, promote metacognitive awareness, ensure AI augments rather than replaces human cognition.
These critiques are largely right about what they observe. But they are embedded in ideological assumptions that need to be named. The cognitive offloading framework doesn’t just describe a pedagogical problem. It carries forward a deeply American mythology about self-reliance, individual achievement, and the moral value of unassisted struggle. And that mythology has always served to justify inequality.
The Classroom That Never Was
The cognitive offloading critique rests on a historical fiction: the autonomous learner, working in productive isolation, building cognitive muscle through solo effort. This student never existed, or existed only for the few.
The history of education is a history of collaborative learning. John Dewey began analyzing the benefits of students working together in the 1940s. By the 1960s and 70s, two distinct but related movements (collaborative learning and cooperative learning) emerged from scholars in higher education and K-12 settings. As a 2023 historical review in TechTrends documents, these approaches fundamentally challenged the “preferred format of individual student learning” that had dominated earlier eras. Think-Pair-Share, Jigsaw Learning, peer review sessions, literature circles: these strategies have been central to effective pedagogy for decades.
Students have always learned through assistance. From peers, from teachers, from resources, from the structured support of the classroom environment itself. The seminar table that Terry Underwood describes in “The Humanities and AI: A Year of Reckoning,” where Princeton students probe difficult texts together, is assistance made flesh. No one worries that these students are “cognitively offloading” onto their classmates. No one suggests that peer feedback on a draft represents cognitive dependency.
Yet when AI enters the picture, suddenly assistance becomes suspect. The framing shifts. What was scaffolding becomes offloading. What was distributed cognition becomes intellectual weakness.
This isn’t pedagogical rigor. It’s ideological selectivity. We’re making choices about which forms of assistance count as legitimate and which threaten the integrity of learning. And those choices align suspiciously well with existing hierarchies of access and privilege.
Bootstrap Pedagogy
Owen Matson offers a fundamentally different framework. In “Beyond Augmentation: Toward a Posthumanist Epistemology for AI and Education,” he argues that we’re witnessing not the addition of a tool but “a shift in the epistemic conditions under which learning takes place.” Cognition in AI-mediated environments, he writes, is “emergent, recursive, and systemically entangled,” which is to say, it’s distributed across human and nonhuman actors in ways that challenge the fiction of the autonomous thinking subject.
In “The Dangers of Protecting Students from the Dangers of AI,” published in EdTech Digest, Matson makes the pedagogical implications concrete. AI’s limitations (its errors, oversimplifications, hallucinations) become sites of learning. Students engage AI not as an authority but as a fallible contributor to thinking. The goal is “collaborative cognition,” where “the value of student work increasingly lies in what neither the student nor the AI could have produced alone.”
This is the opposite of cognitive offloading. It’s cognitive distribution, which is what all thinking has always been. We think with language, with texts, with other minds, with tools. The Socratic dialogue is distributed cognition. The scientific paper with its apparatus and citations is distributed cognition. The writing conference where a teacher asks generative questions is distributed cognition.
But the cognitive offloading framework treats AI assistance as categorically different: more dangerous, more likely to atrophy the mind. Why? Not because the empirical evidence clearly distinguishes it from other forms of assistance, but because it threatens a cherished ideological commitment: the bootstrap philosophy.
The bootstrap mythology insists that success comes from individual effort, that accepting help is weakness, that struggle must be solitary to build character. It’s a particularly American delusion, and it’s always been deployed to explain why some people deserve support and others don’t. If you succeed, it’s because you pulled yourself up. If you fail, it’s because you didn’t try hard enough, didn’t struggle the right way, became dependent on assistance you should have refused.
This ideology has profound consequences for education. It determines whose struggle we valorize as “productive difficulty” and whose we mark as deficiency. It shapes which students we imagine as deserving scaffolding and which we expect to make it on their own.
The Class Divide in Struggle
Underwood makes the stakes explicit. When an Exeter student labors over Dostoevsky, the institution frames that struggle as growth. When a student in rural Tennessee or the South Side of Chicago struggles with the same passage, the institution codes it as failure. “Same struggle, different meaning, different consequence.”
The humanities, Underwood argues, have never been equally distributed. Interpretation over extraction, ambiguity over false clarity, conversation over recitation: these values exist robustly in well-resourced schools and barely at all in under-resourced ones. Elite students develop voice; poor students fill in templates. Elite students learn that meaning is made through sustained inquiry; poor students learn that texts contain correct answers to be identified and retrieved.
Now consider how the cognitive offloading critique interacts with this divide.
Students in affluent settings already have access to extensive human assistance: small seminars, writing conferences, office hours, peer review sessions, tutoring centers. For them, the question of AI assistance is genuinely about whether to add another form of support to an already rich ecosystem. They can afford to be selective, to worry about overdependence, to preserve “authentic struggle” as a pedagogical value.
Students in under-resourced settings have no such luxury. They face teachers managing 150 students, classrooms where individual attention is structurally impossible, schools where conversation has been replaced by recitation and writing conferences exist only in theory. For them, AI assistance isn’t an addition to robust human support. It’s the first time anyone has had the capacity to engage their tentative interpretations, to ask follow-up questions, to treat their thinking as worth developing.
When we frame AI assistance as cognitive offloading to be resisted, we’re making a choice: preserve the purity of unassisted struggle for students who’ve never had assistance in the first place, while students who’ve always had extensive support continue to benefit from it.
This is bootstrap ideology in action. It dresses up as pedagogical principle (we’re protecting students from dependency!) but it functions to maintain existing inequalities. The students most likely to be denied AI assistance are the students who need scaffolding most. The students most likely to be granted access are the students who need it least.
What Systems Make Offloading Rational
Here’s what the cognitive offloading research actually reveals: students respond rationally to the incentive structures of schooling.
When assessment focuses on final products rather than skill development, when content delivery dominates over thinking, when grades matter more than understanding, students will use any available tool to meet the stated requirements efficiently. If AI produces work that earns an A, and the system rewards the A rather than the learning, students aren’t being cognitively lazy. They’re being strategically rational.
The problem isn’t the tool. It’s that we’ve built educational systems that treat learning as compliance rather than capability, that value performance over process, that reduce education to measurable outputs. AI doesn’t cause this. It exposes it.
In well-designed learning environments, assistance doesn’t replace thinking. It scaffolds it. A writing conference with a teacher asks generative questions. A seminar discussion requires students to articulate and defend interpretations. Peer review develops critical reading alongside drafting. These forms of assistance are understood as central to learning because they’re structured to promote engagement rather than bypass it.
AI can function exactly the same way. It can ask questions about a student’s draft, probe inconsistencies, offer alternative perspectives, create space for interpretive practice. As Underwood writes, “The interaction is not a seminar, but it is closer to interpretive practice than circling A, B, C, or D.”
The difference isn’t in the tool. It’s in the design. Is AI deployed to help students develop and test their thinking, or to generate outputs that satisfy compliance requirements? Is it used to scaffold interpretation, or to extract correct answers? Is it a conversational partner that makes thinking visible, or an answer machine that makes thinking unnecessary?
These are design choices, not technological inevitabilities. But the cognitive offloading framework treats the problem as inherent to AI assistance rather than contingent on how we deploy it. That framing serves an ideological function: it locates the problem in the student’s use of assistance rather than in the systems that make assistance necessary or that fail to provide it in the first place.
Assistance as Infrastructure
Consider three students:
The English language learner who toggles between Spanish and English to understand Steinbeck, using AI to help articulate ideas in a second language. Is this cognitive offloading? Or is it precisely the kind of distributed cognition that all language learning involves?
The student with social anxiety who practices articulating interpretations with an AI before risking them in class discussion. Is this dependency? Or is it rehearsal space that makes human connection less terrifying, that prepares a student to participate in the irreplaceable experience of thinking with others?
The student in a school with no writing center, no office hours, no small classes who uses AI to workshop a draft. To identify where their argument becomes unclear, to consider counterarguments, to test whether their evidence supports their claims. Is this cognitive weakness? Or is it the first time this student has had access to the kind of iterative revision process that’s been standard in elite education for decades?
The cognitive offloading framework struggles with these cases because it assumes a context where robust human assistance is already available. Remove that assumption (recognize that for many students, human assistance at scale simply doesn’t exist under current conditions) and the entire frame shifts.
AI assistance isn’t a substitute for human teaching. It’s infrastructure. It’s what makes certain forms of learning accessible when human infrastructure has been systematically defunded, when class sizes make individual attention impossible, when schools serving poor students have never provided the conditions for genuine intellectual conversation.
This isn’t about AI replacing teachers. It’s about AI providing what teachers, under current conditions, cannot provide: individualized feedback at scale, patient engagement with tentative thinking, opportunities for iterative revision, space to practice interpretive confidence before risking public vulnerability.
The cognitive offloading critique wants to protect students from dependence on AI. But dependence on what alternative? In under-resourced schools, the alternative isn’t rich human interaction. It’s no substantive feedback at all. The alternative isn’t Socratic dialogue. It’s recitation and worksheets. The alternative isn’t developing voice through iterative revision. It’s submitting a single draft to a teacher who can only check whether it has a thesis statement.
What We’re Really Protecting
The cognitive offloading discourse is ultimately about protecting a particular vision of education: one where learning happens through direct human transmission in small, intimate settings. It’s a vision worth valuing. Underwood is right that something irreplaceable happens in the seminar room, when humans think together in real time, risking interpretation and building ideas collectively.
But that vision has never been available to most students. It’s been reserved for the few. Those who attend schools with small classes and extensive support systems, those who go to colleges where professors hold office hours and writing centers offer unlimited consultations, those whose educational contexts already provide abundant human assistance.
When we defend this vision against the threat of AI, we’re defending a privilege that’s been unequally distributed from the beginning. We’re saying that students who already have access to rich human interaction should keep it pure, while students who’ve never had such access should continue making do with overcrowded classrooms and overwhelmed teachers. To give them AI assistance would risk cognitive offloading.
This is bootstrap ideology dressed up as pedagogical principle. Struggle without support builds character, so we’ll withhold support and call it pedagogy. Dependence on assistance is weakness, so we’ll deny assistance to those who need it most and praise the self-sufficiency we’ve structurally required.
The alternative is to recognize that all learning is assisted. That cognition has always been distributed. That thinking with tools and texts and other minds isn’t cognitive offloading but cognitive practice. And then to ask: what kinds of assistance serve learning, and how do we make them available to everyone?
That means designing AI systems that scaffold interpretation rather than provide answers, that ask questions rather than deliver information, that make thinking visible rather than invisible. It means using AI not to bypass struggle but to ensure that struggle is productive rather than defeating. That students develop agency through engagement rather than give up because the text remains inaccessible.
The fight over AI in education is ultimately a fight over who deserves assistance, who has earned the right to support, whose learning matters enough to resource adequately. The cognitive offloading framework, for all its empirical rigor, carries forward a deeply conservative impulse: to preserve existing hierarchies of access by pathologizing the forms of assistance that might disrupt them.
I’m arguing for the opposite. We should expand access to assistance. We should design systems that scaffold learning for everyone. We should reject the bootstrap mythology that treats support as weakness and unassisted struggle as virtue.
AI could democratize the forms of learning that have always been reserved for the few. Or it could deepen existing inequities, becoming one more way to sort students into those who deserve support and those who need to make it on their own.
The outcome depends on whether we recognize assistance for what it is: not a threat to learning, but its precondition.
Nick Potkalitsky, Ph.D.
The question isn’t whether AI causes cognitive offloading. The question is what kind of learning community we want to build, and whether we’re willing to extend the forms of assistance that privileged students have always had to everyone else. AI is already inside our learning environments whether we like it or not.
Check out some of our favorite Substacks:
Mike Kentz’s AI EduPathways: Insights from one of our most insightful, creative, and eloquent AI educators in the business!!!
Terry Underwood’s Learning to Read, Reading to Learn: The most penetrating investigation of the intersections between compositional theory, literacy studies, and AI on the internet!!!
Alejandro Piad Morffis’s The Computerist Journal: Unmatched investigations into coding, machine learning, computational theory, and practical AI applications
Michael Woudenberg’s Polymathic Being: Polymathic wisdom brought to you every Sunday morning with your first cup of coffee
Rob Nelson’s AI Log: Incredibly deep and insightful essay about AI’s impact on higher ed, society, and culture.
Michael Spencer’s AI Supremacy: The most comprehensive and current analysis of AI news and trends, featuring numerous intriguing guest posts
Daniel Bashir’s The Gradient Podcast: The top interviews with leading AI experts, researchers, developers, and linguists.
Daniel Nest’s Why Try AI?: The most amazing updates on AI tools and techniques
Jason Gulya’s The AI Edventure: An important exploration of cutting-edge innovations in AI-responsive curriculum and pedagogy
I'm sympathetic to the points you're raising here—I think you rightly point out that a lot of the arguments around AI focus on the "what" rather than the "how," and look past many ways that it can be employed in ways that are beneficial to learning. Writing those off is a mistake, and I agree with you that some employ the "cognitive offloading" argument in that fashion, which you are correct to critique.
Two pushbacks here, though:
[1] The argument for the "cognitive offloading" is a result of many, many students doing exactly that: cognitively offloading the thinking to get the product. (Hence the results of the studies, I'd point out!) Of course, that should not discredit the good that is happening elsewhere, and I'm sure you'd also argue that this is a flaw in our current "status quo" of how learning in education is designed. Education moves at a glacial pace in terms of change, though, and that change shows up in myriad iterations depending on context—and while I'm sympathetic to your argument I'm also sympathetic to those who are seeing students using AI right now as a shortcut and, as a result, learning less.
[2] I also think this piece essentially takes a fatalist approach in saying that there will always be under-resourced schools and therefore, if I am reading it correctly, we should accept that status quo and provide the "next-best" alternative in AI assistance rather than the ideal conditions you name in this piece for learning (smaller class sizes, seminars, etc.). This is where your "bootstrap" argument seems to create a straw man, as I think the vast majority of AI critics are quite frustrated by the systemic inequities built into the education system—and are therefore skeptical of the "AI bandaid" as a solution to them.
Two pushbacks aside, very much appreciate this piece—gave me a new way to think about this topic, and that is a reason I continue to have your writing at the top of my list on this topic as far as meaningful in my own reflections and understanding. Keep doing what you're doing!
A challenging and interesting argument - thank you.
One category to follow up on - "Follow the money." Although AI may threaten the existence of elite educational institutions because academic support has become available to all, how much money is tied to those same places - where did the VC funders go to school, in most cases? Which institutions will literally pay more (and thus drive the technology), Outstanding University or an underfunded school district? How, then, can we ensure that the technology develops in ways that will aid the broader population, not primarily the elite population? Is it enough to hope that tech companies will play the long game, realizing that there are a lot more undersourced Americans - and thus more money to be made - than oversourced Americans?
Okay - I have to go grade papers.