Is Cheating the American Way?

Taking a Stand Against Raving and Ranting

Teachers as Innovators

Since the emergence of public education in 19th century America, classroom teachers have been the drivers of pedagogical innovation, often working against institutional inertia to humanize learning.

When monitorial systems reduced children to cogs in factories, teachers pioneered child-centered approaches that recognized individual differences. They transformed bare rooms into environments rich with student work, brought nature studies indoors, and created classroom libraries when textbooks were the only sanctioned materials.

During the Progressive Era, teachers embraced Dewey's ideas about experiential learning, turning classrooms into laboratories despite administrative resistance. They introduced project-based learning, cooperative groups, and integrated curricula long before these became fashionable terms.

In the face of creeping standardization across the 20th century reaching its zenith in No Child Left Behind, creative teachers smuggled in arts, drama, and hands-on science, understanding that genuine education engages the whole child.

Through the 20th century, teachers adapted to waves of technological change from slate boards to overhead projectors, from mimeographs to computers, not as implementers but as innovators who reimagined how these tools could serve learning. They developed differentiated instruction before the term existed, created inclusive classrooms before mandates required them, and built communities of care that helped students survive difficult circumstances.

Even within constraining systems, teachers have consistently found ways to nurture thinking, creativity, and compassion. They've been the human bridges between institutional demands and student needs, often at considerable personal cost.

This legacy of innovation makes the current resistance to AI particularly puzzling and particularly important to address. Today's teachers stand at another transformative moment, one that calls for the same creative courage their predecessors displayed.

A Defense of Teacher Resistance To AI Innovation

The teachers' case for AI resistance deserves a fair hearing. Many educators would argue they're confronting a crisis of academic integrity. They point out that AI tools enable unprecedented levels and types of intellectual dishonesty. In the most obvious instance, students submit AI-generated work as their own or use it to circumvent struggle, undermining the entire educational enterprise.

How can teachers assess learning if they can't verify who did the thinking?

These educators argue they've spent years developing careful assessment methods designed to measure student understanding and personal growth. AI doesn't just disrupt these methods; it potentially renders them obsolete.

Teachers trained to evaluate original student writing suddenly face essays that are machine-generated. Math teachers who carefully scaffolded problem sets to build conceptual understanding watch students bypass the learning process entirely. There is no such thing as a free lunch, teacher wisdom says. Struggle is integral to learning and appreciation of truth and beauty.

Many teachers would dispute the charge that they're clinging to outdated authority, tyrants in the classroom. Instead, they'd argue they're defending something precious: the relationship between struggle and learning, between effort and growth.

When students use AI to avoid intellectual challenges, they rob themselves of the very experiences that develop resilience, creativity, and deep understanding. The banking model critique, they might say, misses the point. They aren't in the business of depositing information but fostering genuine intellectual development.

Furthermore, teachers face practical constraints rarely acknowledged by critics. They're mandated to meet specific learning standards, prepare students for standardized tests, and document measurable outcomes. They work within systems that demand accountability for student performance. When AI tools compromise their ability to accurately assess student progress, it threatens their professional responsibilities and potentially their livelihoods.

The surveillance and detection tools that critics condemn are seen differently by teachers. They are necessary measures to preserve academic honesty in an environment where cheating has become frighteningly easy. They're not trying to micromanage thinking but to ensure that the thinking being assessed is real, not downloaded or generated elsewhere.

Finally, many educators would object to the characterization that they're resistant to change. They've adapted to countless technological shifts: calculators, internet research, collaborative online tools. But AI feels different, a more intense threat. It's not just another way to make or break an argument or interpretation, but something that can replicate the very thinking processes education aims to develop. Their caution is wisdom, not stubbornness.

Their frustration stems not from lost authority as the critics argue, but from a genuine concern: if students can outsource their thinking to machines, what becomes of them as adults? Preserving "my classroom" and my authority? Nonsense. We are protecting the irreplaceable value of human learning in a dark age of artificial.

An Offense for Teacher Engagement with AI

The teachers' rebuttal reveals self-mystification and self-deception in the substrate of the issue. Their claim to be "defending academic integrity" masks a deeper refusal to question what integrity means in the minds of learners stuck in an archaic system. They've confused compliance with learning, surveillance with assessment, and assigning and testing with teaching.

Consider their central anxiety: "How can we assess learning if we can't verify who did the thinking?" This question exposes the banking model's grip on their imagination. They've so thoroughly equated learning with individual production that they cannot envision education beyond this banking framework. The banking framework is officially bankrupt in the age of AI.

Yet throughout history, human knowledge has advanced through collaboration, tool use, and building upon others' work. The printing press didn't destroy scholarship by making books widely available; it transformed it.

Their lament about "carefully developed assessment methods" becoming obsolete overnight is particularly telling. They could have become obsolete in the 1950s when figures like Benjamin Bloom arose to try to turn the ship around.

After all, Bloom's dedication to national thought leadership was aimed at diminishing the use of knowledge inventories for tests—even if the knowledge inventory consists of open-ended constructed responses to teacher exam questions. Bloom taught us that the right answer isn’t always the best answer.

Bloom may have understood somewhere in his subconsciousness that schools do not give letter grades for critical thinking, creativity, computational fluency, and self-regulation. Instead, it's History, English, Biology, etc. But those higher-order skills drive human progress. We grade what’s left over when the learning is over and hope that they learned more than mastery of facts and tasks.

If years of pedagogical practice can be undone by a single technology, perhaps those practices weren't the acme of perfection all along. Understanding isn't threatened by tools; it's revealed through how students use them. A calculator doesn't undermine mathematical thinking; it liberates it from computational drudgery. The teacher's role is to teach them to use the tool.

The appeal to "struggle and learning" romanticizes unnecessary friction. Yes, growth requires struggle, but imposed obstacles and daunting rules aren't the same as meaningful intellectual challenges. Making students write without AI doesn't deepen their thinking any more than making them write with ink pens would. The real struggle should be wrestling with ideas, not trying to win on assignments and tests.

Their "practical constraints" argument in the form of standards, tests, and accountability highlights their unwilling complicity in a broken system. Rather than questioning why these metrics fail to capture authentic learning, they accommodate the metrics.

This is precisely the administrative thinking Freire criticized: educators becoming agents of institutional control against their will rather than irritants, truth-tellers, and student advocates.

Most revealing is their claim about surveillance tools being "necessary measures to preserve academic honesty." Students are framed as presumptive cheaters who must be monitored and controlled. As others have noted, it's the educational equivalent of stop-and-frisk policing, treating learners as suspects rather than partners in education.

Their assertion that AI is fundamentally different from previous tools is a truth wrapped in defensive anxiety. Yes, AI can replicate certain thinking processes, which should prompt us to ask why we're still assessing those particular processes. If a machine can do it, perhaps it wasn't the uniquely human capacity we thought it was. Why not teach students to use the machine and free up time and space for the complex thinking the source of all human intelligence is capable of: the human brain?

The teachers' final flourish, i.e., protecting "the irreplaceable value of human learning," rings hollow when their methods treat humans like programmable devices expected to output predetermined frames of reference. Are they protecting human learning or protecting their role as its gatekeepers?

The real issue isn't whether students are "outsourcing their thinking" but whether educators are outsourcing their teaching to surveillance systems and punitive measures. The banking model hasn't just failed; it's been exposed. Yet rather than embrace this opportunity for pedagogical perfecting, too many teachers are doubling down on control, mistaking their authority for education itself.

Neither Giving Up Nor Giving In

The current polarization around AI in education serves hidden purposes. For teachers, playing the victim of student cheating brings sympathy and attention to their legitimate struggles with outdated systems and inadequate support. It provides a reason to highlight their boutique qualities—on both sides—what it is about them that makes students “lucky to be in my class.”

For commentators, the conflict generates engaging content—another front in the culture wars. In fact, many teachers assert that AI is an issue of "school culture." Are textbooks also an issue of school culture or an issue of instruction? Locating AI as a cultural problem misrepresents the core of the problem. This drama feels good, bringing both groups into the spotlight as defenders of traditional values or champions of innovation.

But this satisfying polarization is a dangerous distraction. While we debate whether students are cheating or teachers are tyrannical, we're avoiding the real question: How do we learn to teach students to use AI productively as "meta authors," creators who orchestrate, evaluate, and refine AI output, rather than as harried grade grabbers using AI as vending machines for academic credit?

The answer requires teachers to reclaim their historical role as innovators. Just as their predecessors transformed slate boards into tools for creative expression and early computers from calculation machines into gateways for exploration, today's educators must reimagine AI as a tool in developing thinking processes. This means moving beyond surveillance to embrace new forms of assessment that evaluate students' ability to prompt, guide, critique, and build upon AI outputs.

The banking model is dead; AI killed it. The question isn't whether to mourn or celebrate its passing but how to midwife whatever comes next. Teachers who embrace this challenge will join the long tradition of educators who've humanized technology rather than letting technology dehumanize education—and persevered even in a mechanical, imperfect institution designed to strengthen the American workforce. Those who resist may find themselves irrelevant, clinging to a sinking ship they might have helped rescue. Teachers have two jobs: 1) Protect students from offloading their future to AI and 2) Teach students to use AI to amplify their intelligence. Both jobs are crucial. The choice and the opportunity is theirs.