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.